DETAILED ACTION
This action is in response to communications filed on 01/30/2026. Claims 1, 8, 14, and 20 have been amended. No claims have been added nor removed. Claims 1-20 are presented for examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Applicant has amended claims 1, 8, 14, and 20 to include newly claimed limitations and provides citation to the specification in at least paragraphs 25 and 30 of the originally filed specification.
Examiner confirms that adequate support is provided in the specification for all amended claims such that it is apparent the applicant had possession of the claimed invention at the time of filing. No new mater has been introduced by way of amendment.
Response to Arguments
Claim Objections
The applicant has amended claim 14 in response to the previously set forth claim objection of claim 14.
The amendments are satisfactory and the claim objection has been withdrawn.
35 U.S.C. § 101
The applicant has amended claims 1, 14, and 20 in response to the rejections previously set forth under 35 U.S.C. § 101. Applicant submits that such amendments provide significantly more than the recited judicial exceptions, particularly noting that the additional elements provide “limitations that confine the judicial exception to a particular, practical application of the judicial exception” because the elements include performing physical limitations such as drilling a borehole at a specific location, changing a drilling mud, and selecting a drill bit. The applicant further argues that the physical machines are not merely applying a process but they are being altered or changed by the process to thereby improve the outcome of subsequent operations using physical machines.
Applicants arguments with regard to the rejection under 35 U.S.C. § 101 have been considered but are not persuasive. While the examiner agrees that the amended limitations are additional elements, the examiner does not agree that the additional elements integrate the judicial exception into a practical application nor amount to significantly more than the recited judicial exception for the following reasons. The claim limitations have been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for amounting to the words “apply it” with regard for the judicial exception. When determining whether a claim simply recites a judicial exception with the words “apply it”, examiners consider 3 key points: (1) whether the claim recites only the idea of a solution or outcome, (2)whether the claim invokes computers or other machinery as a tool to perform an existing process, and (3) the particularity or generality of the judicial exception.
In order for additional elements to be more than “apply it”, the claim must recite a technological solution to a technological problem. Per the claimed limitations, the claim recites the idea of an outcome or a solution but fails to recite details of how a solution to a problem is accomplished. For example, in claim 1, the claim recites determining a borehole design and a drilling bit through a sequence of steps which can be done in the human mind or using pen and paper as assistive physical aids (the scoring of design concepts based on received parameters). The claim then recites “drilling the borehole in a second drilling phase with the drilling bit selected by the borehole design in the first drilling phase”. This limitation does not clearly reflect a technological solution to a technological problem such that it would be readily apparent that the claimed invention provides any improvement beyond the improvements to the judicial exception itself. Drilling the borehole is recited in such a generic way that it does not appear that any inventive concept is demonstrated in the claimed limitation either alone or in combination with the judicial exception (either through the relationship of the judicial exception to the additional element or through how the judicial exception is necessarily applied). Any purported improvement flows as a direct consequence to the improvements made by the judicial exception (the risk scoring process) and is not a consequence of how the judicial exception is applied to the field of use. Furthermore, the claim invokes the use of generically-recited machinery to perform existing processes. Borehole drilling is a known, existing process that is performed using a drill bit. The claim merely invokes their usage but does not so limit the claim in any inventive capacity. The courts have found additional elements similar to this to be mere instructions to apply an exception in at least the following example in MPEP 2106.05(f) “A method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair”. The present claims appear to likewise reflect this same structure of optimizing designs and including a final step of using a tool (bit, mud, equipment) in a non-inventive way. The claim fails to restrict how the selected components are used in a meaningful or inventive way and also fails to provide any description of the mechanism linking the selection of the component to the use of the component. Accordingly the application of the judicial exception is highly generic and the additional element does not integrate the judicial exception into a practical application. The courts have found that adding the words “apply it” with the judicial exception do not qualify the claim as significantly more than the judicial exception. When viewing the claim as a whole, there are no particularly claimed relationships between the steps of the judicial exception and the limitation that describes applying the exception such that it is apparent any inventive concept that the additional element contributes to, either alone, or in conjunction with the judicial exception. In order for a claim to improve technology, the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome must be reflected by the claim. As stated above, the means by which the judicial exception is applied is recited generically. Further, there are no details recited such that any components recited would be uniquely identifiable as a particular machine(s) or be anything beyond what is considered routine and conventional in the art.
The inventive concept has been found to be within the recited judicial exception. Per MPEP 2106.05(a), It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. Accordingly, because the additional elements fail to provide any asserted improvements, the claims remain rejected under 35 U.S.C. § 101.
35 U.S.C. § 103
Applicant has amended claim language in response to the previously cited rejections under 35 U.S.C. § 103 to distinguish that different groups of claim elements occur during different borehole operation plan phases. Applicant argues that Jeong does not address separating actions between different borehole operation plan phases and that Jeong’s operational steps appear to occur within one phase such as planning or operation phase but does not specifically carry over results from one phase to the next. Applicant further argues that Veeningen focuses on a single operational phases. Applicant recognizes that Veeningen utilizes a planning and drilling process, but argues that Veeningen does not use a feasibility phase and does not contemplate two distinct drilling phases.
Applicant arguments have been considered but are not persuasive.
Regarding claim 1, a “drilling phase” is not defined by the claim to hold any significant meaning. Under broadest reasonably interpretation, a drilling phase may encompass a specified duration during a drilling operation. Jeong discloses determining an initial plan based on a risk profile and subsequently proceeding to drill a well using the parameters identified in the initial plan. Jeong then discloses, during the drilling operation, refining the adjustment of drilling parameters in real time and also updated the projected risk to reflect the current real time drilling conditions based on measurements. Each iteration of the evaluation to update the parameters and risk profile is understood to be a phase of drilling. Accordingly, Jeong teaches the amendment to claim 1. Regarding claim 8, the iterative nature of Jeong’s real-time updates further suggests that the risk update could continue to a third phase of updates and accordingly Jeong is relied upon to disclose the newly added feature of claim 8 in the same manner as applied to claim 1.
Regarding claim 14, Jeong discloses identifying risks using a pre-drill advisor for controllable drilling parameters to generate a risk profile prior to drilling ((Jeong, ¶51) "For
correcting the expected risks raised by Pre-Drill advisor, the controllable drilling parameters (mud weight, flowrate, drillstring velocity, speed (revolutions-per-minute or RPM), and rate of penetration (ROP)) may be adjusted. For example, a Nearest Neighbor Search (NNS) algorithm may be employed to select settings within time and cost as shown in the FIG. 9"). Once the new risk profile is determined prior to drilling, drilling may be executed and parameters are used that correspond to the risk profile as well as continuously updated during the drilling operation (as an execution phase). While Jeong only explicitly discloses mud weight/density as a controllable drilling parameter for changing, Veeningen explicitly discloses considering mud type, as indicated by the claim, within the pertinent controllable parameters that are considered in the risk assessment ((Veeningen, ¶93) "11. The system will generate the appropriate mud types, corresponding rheology, and composition based on the lithology, previous calculations, and the
users selection."); ((Veeningen, ¶122 and ¶188) "Values of the Input Data 20a that are used as input for the Risk Assessment Algorithms 24 and the Risk Assessment Logical Expressions 22 are as follows:…"). Accordingly, the newly added limitations to claim 14 are reasonably taught and/or suggested by the prior art of record.
Regarding claim 20, Veeningen appears to contemplate multiple planning aspects of the design in terms of risk assessment, particularly noting that the disclosed risk tool may be used for feasibility considerations ((Veeningen, ¶37) "Asset Teams will use the software associated with the 'Automatic Well Planning Software System' as a scoping tool for cost estimates, and assessing mechanical feasibility, so that target selection and well placement decisions can be
made more knowledgeably, and more efficiently."). Jeong is relied upon to determine the bit depth for (as drilling equipment used for a wellhead) and using that bit depth within a model (as the placement of that equipment) during use of the planning software ((Jeong, ¶47) "The method 400 may proceed to receiving drilling parameters for a new well, as at 406, and determining that the drilling parameters meet engineering specifications for well equipment, as at 408. A well plan may specify many different drilling parameters. FIG. 5 illustrates plots of four
examples of such drilling parameters, namely, over balance pressure, flow rate, rotation rate, and bit depth. These drilling parameters may be fed to a model of the subterranean domain, using the drilling system components selected, in order to determine that the drilling parameters meet engineering specifications for the well equipment, as at 408."). Accordingly, the newly added limitations to claim 20 are reasonably taught and/or suggested by the prior art of record.
For the reasons noted in this response, in conjunction with the updated rejection of this office action, the rejections to the claims under 35 U.S.C. § 103 have been maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “detailed planning phase” in claim 20 is a relative term which renders the claim indefinite. The term “detailed” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The planning phase of the borehole operation plan is indefinite because use of the term “detailed” is not quantifiably or specifically ascertainable to distinguish the bounds of the claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The following section follows the 2019 Patent Eligibility Guidance (PEG) for analyzing subject matter eligibility:
Step 1 - Statutory Category:
Step 1 of the PEG analysis entails considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101 (process, machine, manufacture, or composition of matter).
Step 2A Prong 1 - Judicial exception:
In Step 2A Prong 1, examiners evaluate whether the claim recites a judicial exception (an abstract idea, law of nature, or a natural phenomenon).
Step 2a Prong 2 - Integration into a practical application:
If claims recite a judicial exception, the claim requires further analysis in Step 2A Prong 2. In Step 2A Prong 2, examiners evaluate whether the claim as a whole integrates the exception into a practical application.
Step 2B - Significantly More:
If the additional elements identified in Step 2A Prong 2 do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception and requires further analysis under Step 2B- Significantly More.
As noted in the MPEP 2106.05(II): The identification of the additional element(s) in the claim from Step 2A Prong 2, as well as the conclusions from Step 2A Prong 2 on the considerations discussed in MPEP 2106.05(a) -(c), (e), (f), and (h) are to be carried over. Claim limitations identified as Insignificant Extra-Solution Activities are further evaluated to determine if the elements are beyond what is well -understood, routine, and conventional (WURC) activity, as dictated by MPEP 2106.05(II).
Independent Claims:
Claim 1:
Step 1: Claim 1 and its dependent claims 2-13 are directed to a method which falls within one of the four statutory categories of a process.
Step 2A Prong 1: Claim 1 recites a judicial exception, noted in bold:
determining one or more borehole design concepts for the borehole utilizing the borehole location parameters, the borehole associated data, and the geographic location of interest; The claim limitation can be reasonably read to entail evaluating borehole location parameters, borehole associated data, and the geographic location of interest in order to make a judgment as to the borehole design concepts. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
assigning one or more risks to each of the one or more borehole design concepts; The claim limitation can be reasonably read to entail evaluating borehole design concepts to qualify the risk associated with the concept. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
generating a sub-risk score for one or more risk types for each of the one or more borehole design concepts using the one or more risks; The claim limitation can be reasonably read to entail evaluating the risks of the borehole design concepts to determine a sub-risk score. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Furthermore, the generation of a score is the recitation of a quantified mathematical relationship describing the degree of impact for the risk type. Therefore, this claim also includes the recitation of abstract ideas of mathematical concepts.
generating a final risk score for each of the borehole design concepts, using a sum algorithm to combine the sub-risk score for each of the one or more risk types; and The claim limitation can be reasonably read to entail using a sum algorithm to combine sub risk scores in order to determine a final risk score. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Furthermore, the utilization of a sum algorithm to generate the score is the explicit recitation of a mathematical calculation and therefore the claim also includes the abstract idea as a mathematical concept.
filtering non-satisfactory borehole design concepts from the one or more borehole design concepts when the final risk score for the respective borehole design concept fails to satisfy a first risk tolerance parameter or one or more sub-risk scores of the final risk score for the respective borehole design concept fails to satisfy a respective sub-risk risk tolerance parameter; The claim limitation can be reasonably read to entail evaluating risk scores with regard for tolerance parameters to determine if borehole design concepts are satisfactory or not. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Furthermore, the evaluation of the risks with regard to tolerance parameters is the recitation of comparing two values as mathematical relationships. Therefore, this claim additionally includes the recitation of abstract ideas as mathematical concepts.
determining the borehole design and a drilling bit in a first drilling phase from the one or more borehole design concepts using the final risk score and the sub-risk scores for each respective borehole design concept; and The claim limitation can be reasonably read to entail evaluating the final risk score and the sub risk scores to make a judgment as to the borehole design and drilling bit. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Therefore, the claim recites a judicial exception.
Step 2A Prong 2: Additional elements were identified and are noted in italics.
receiving borehole location parameters for a borehole, borehole associated data relating to the borehole, and a geographic location of interest for the borehole;- This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering.
drilling the borehole in a second drilling phase with the drilling bit selected by the borehole design in the first drilling phase. - This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) because the limitation is the recitation of a generic equivalent of “apply it” with regard to the design values obtained as part of the judicial exception.
The courts have found that merely reciting the words “apply it” or a generic equivalent (Mere Instructions to Apply an Exception (MPEP 2106.05(f))) and adding insignificant extra- solution activity to the judicial exception (Insignificant Extra Solution Activity (MPEP 2106.05(g))) does not integrate the judicial exception into a practical application.
When viewed independently and within the claim as a whole, the additional element does not appear to integrate the judicial exception into a practical application.
Step 2B: As discussed in Step 2A Prong 2, additional elements were identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) which must be further evaluated to determine if they are beyond WURC activities. Additional elements identified otherwise and conclusions from Step 2A Prong 2 are carried over for evaluating if the claim, as a whole, amounts to an inventive concept that is significantly more than the judicial exception:
receiving borehole location parameters for the borehole, borehole associated data relating to the borehole, and a geographic location of interest for the borehole – This limitation has been identified as the insignificant extra solution activity of mere data gathering, as stated previously. Under broadest reasonable interpretation and when read in light of the specification, the claim limitation includes receiving data over a network. This computer functionality have been recognized by the courts as well understood, routine, and conventional computer functionality when claimed in a merely generic manner, such as in this limitation.
The courts have found that simply appending insignificant extra solution activities that are well-understood, routine, and conventional activities to the judicial exception does not qualify the limitations as “significantly more” than the recited judicial exception. The remaining additional element was identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)), as stated previously. The courts have found reciting the words “apply it” does not qualify the limitations as “significantly more” than the recited judicial exception.
With the additional elements viewed independently and as part of the ordered combination, the claim as a whole does not appear to amount to significantly more than the recited judicial exception because the claim is using generic computing components recited at a high level of generality and functioning in their normal capacity in conjunction with well-understood, routine, and conventional activity to enable the performance of a task that can practically be performed within the human mind or using pen and paper as an assistive physical aid. The value obtained from the abstract idea steps is used in a generic and expected way that amounts to generally applying the judicial exception for the intended use. Therefore, the claim does not include additional elements, alone or in combination that are sufficient to amount to significantly more than the recited judicial exception.
Conclusion: Based on this rationale, the claim has been deemed to be ineligible subject matter under 35 U.S.C. 101.
Claim 14:
Step 1: Claim 14 and its dependent claims 15-19 are directed to a system which falls within one of the four statutory categories of a machine.
Step 2A Prong 1: Claim 1 recites a judicial exception, noted in bold:
determine one or more borehole design concepts for the borehole utilizing the borehole location parameters, the borehole associated data, and the geographic location of interest; The claim limitation can be reasonably read to entail evaluating borehole location parameters, borehole associated data, and the geographic location of interest in order to make a judgment as to the borehole design concepts. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
assign one or more risks to each of the one or more borehole design concepts; The claim limitation can be reasonably read to entail evaluating borehole design concepts to qualify the risk associated with the concept. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
generate a sub-risk score for one or more risk types for each of the one or more borehole design concepts; The claim limitation can be reasonably read to entail evaluating the risks of the borehole design concepts to determine a sub-risk score. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Furthermore, the generation of a score is the recitation of a quantified mathematical relationship describing the degree of impact for the risk type. Therefore, this claim also includes the recitation of abstract ideas of mathematical concepts.
generate a final risk score for each of the one or more borehole design concepts, using a weighted value algorithm to combine the sub-risk score for each of the one or more risk types; The claim limitation can be reasonably read to entail using a sum algorithm to combine sub risk scores in order to determine a final risk score. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Furthermore, the utilization of a sum algorithm to generate the score is the explicit recitation of a mathematical calculation and therefore the claim also includes the abstract idea as a mathematical concept.
filter non-satisfactory borehole design concepts from the one or more borehole design concepts when the final risk score for the respective borehole design concept fails to satisfy a first risk tolerance parameter or one or more sub-risk scores of the final risk score for the respective borehole design concept fails to satisfy a respective sub-risk risk tolerance parameter, The claim limitation can be reasonably read to entail evaluating risk scores with regard for tolerance parameters to determine if borehole design concepts are satisfactory or not. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Furthermore, the evaluation of the risks with regard to tolerance parameters is the recitation of comparing two values as mathematical relationships. Therefore, this claim additionally includes the recitation of abstract ideas as mathematical concepts.
determine the borehole design in a planning phase of a borehole operation plan, from the one or more borehole design concepts using the final risk score and the sub-risk scores for each respective borehole design concept, and The claim limitation can be reasonably read to entail evaluating the final risk score and the sub risk scores to make a judgment as to the borehole design. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Therefore, the claim recites a judicial exception.
Step 2A Prong 2: Additional elements were identified and are noted in italics.
a data transceiver, configured to – This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) because the limitation invokes the use of computers as a tool to perform an existing task.
receive borehole location parameters for the borehole, borehole associated data relating to the borehole, and a geographic location of interest for the borehole; and - This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering.
a borehole risk analyzer, configured to - This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) because the limitation invokes the use of computers as a tool to perform an existing task.
communicate with the data transceiver and – This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)).
alter a drilling mud used in the borehole in an execution phase of the borehole operation plan, as selected by the borehole design. - This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) because the limitation is the recitation of a generic equivalent of “apply it” with regard to the design values obtained as part of the judicial exception.
The courts have found that merely reciting the words “apply it” or a generic equivalent or invoking the use of computers to perform an existing task (Mere Instructions to Apply an Exception (MPEP 2106.05(f))) and adding insignificant extra- solution activity to the judicial exception (Insignificant Extra Solution Activity (MPEP 2106.05(g))) does not integrate the judicial exception into a practical application.
When viewed independently and within the claim as a whole, the additional element does not appear to integrate the judicial exception into a practical application.
Step 2B: As discussed in Step 2A Prong 2, additional elements were identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) which must be further evaluated to determine if they are beyond WURC activities. Additional elements identified otherwise and conclusions from Step 2A Prong 2 are carried over for evaluating if the claim, as a whole, amounts to an inventive concept that is significantly more than the judicial exception:
receive borehole location parameters for the borehole, borehole associated data relating to the borehole, and a geographic location of interest for the borehole; and - This limitation has been identified as the insignificant extra solution activity of mere data gathering, as stated previously. Under broadest reasonable interpretation and when read in light of the specification, the claim limitation includes receiving data over a network. This computer functionality has been recognized by the courts as well understood, routine, and conventional computer functionality when claimed in a merely generic manner, such as in this limitation.
communicate with the data transceiver and – This limitation has been identified as mere data gathering and outputting. Under broadest reasonable interpretation and when read in light of the specification, the claim limitation includes receiving data over a network. This computer functionality has been recognized by the courts as well understood, routine, and conventional computer functionality when claimed in a merely generic manner, such as in this limitation.
The courts have found that simply appending insignificant extra solution activities that are well-understood, routine, and conventional activities to the judicial exception does not qualify the limitations as “significantly more” than the recited judicial exception. The remaining additional elements were identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)), as stated previously. The courts have found reciting the words “apply it” and invoking the use of generic computers to perform existing processes does not qualify the limitations as “significantly more” than the recited judicial exception.
With the additional elements viewed independently and as part of the ordered combination, the claim as a whole does not appear to amount to significantly more than the recited judicial exception because the claim is using generic computing components recited at a high level of generality and functioning in their normal capacity in conjunction with well-understood, routine, and conventional activity to enable the performance of a task that can practically be performed within the human mind or using pen and paper as an assistive physical aid. The value obtained from the abstract idea steps is used in a generic way that amounts to generally applying the judicial exception for the intended use (alter a drilling mud is not disclosed in a particularly inventive manner). Therefore, the claim does not include additional elements, alone or in combination that are sufficient to amount to significantly more than the recited judicial exception.
Conclusion: Based on this rationale, the claim has been deemed to be ineligible subject matter under 35 U.S.C. 101.
Claim 20:
Step 1: Claim 20 is directed to a computer program product which falls within one of the four statutory categories of a manufacture.
Step 2A Prong 1: Claim 20 recites a judicial exception, noted in bold:
determining one or more borehole design concepts for the borehole utilizing the borehole location parameters, the borehole associated data, and the geographic location of interest; The claim limitation can be reasonably read to entail evaluating borehole location parameters, borehole associated data, and the geographic location of interest in order to make a judgment as to the borehole design concepts. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
assigning one or more risks to each of the one or more borehole design concepts; The claim limitation can be reasonably read to entail evaluating borehole design concepts to qualify the risk associated with the concept. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
generating a sub-risk score for one or more risk types for each of the one or more borehole design concepts using the one or more risks; The claim limitation can be reasonably read to entail evaluating the risks of the borehole design concepts to determine a sub-risk score. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Furthermore, the generation of a score is the recitation of a quantified mathematical relationship describing the degree of impact for the risk type. Therefore, this claim also includes the recitation of abstract ideas of mathematical concepts.
generating a final risk score for each of the one or more borehole design concepts, using an average algorithm to combine the sub-risk score for each of the one or more risk types; and The claim limitation can be reasonably read to entail using a sum algorithm to combine sub risk scores in order to determine a final risk score. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Furthermore, the utilization of a sum algorithm to generate the score is the explicit recitation of a mathematical calculation and therefore the claim also includes the abstract idea as a mathematical concept.
filtering non-satisfactory borehole design concepts from the one or more borehole design concepts when the final risk score for the respective borehole design concept fails to satisfy a first risk tolerance parameter or one or more sub-risk scores of the final risk score for the respective borehole design concept fails to satisfy a respective sub-risk risk tolerance parameter; The claim limitation can be reasonably read to entail evaluating risk scores with regard for tolerance parameters to determine if borehole design concepts are satisfactory or not. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Furthermore, the evaluation of the risks with regard to tolerance parameters is the recitation of comparing two values as mathematical relationships. Therefore, this claim additionally includes the recitation of abstract ideas as mathematical concepts.
determining the borehole design in a feasibility study phase of a borehole operation plan, from the one or more borehole design concepts using the final risk score and the sub-risk scores for each respective borehole design concept; and The claim limitation can be reasonably read to entail evaluating the final risk score and the sub risk scores to make a judgment as to the borehole design. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Therefore, the claim recites a judicial exception.
Step 2A Prong 2: Additional elements were identified and are noted in italics.
receiving borehole location parameters for the borehole, borehole associated data relating to the borehole, and a geographic location of interest for the borehole;- This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering.
placing, during a detailed planning phase of the borehole operation plan, a drilling equipment for a wellhead at a location determined by the borehole design - This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) because the limitation is the recitation of a generic equivalent of “apply it” with regard to the design values obtained as part of the judicial exception.
The courts have found that merely reciting the words “apply it” or a generic equivalent (Mere Instructions to Apply an Exception (MPEP 2106.05(f))) and adding insignificant extra- solution activity to the judicial exception (Insignificant Extra Solution Activity (MPEP 2106.05(g))) does not integrate the judicial exception into a practical application.
When viewed independently and within the claim as a whole, the additional element does not appear to integrate the judicial exception into a practical application.
Step 2B: As discussed in Step 2A Prong 2, additional elements were identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) which must be further evaluated to determine if they are beyond WURC activities. Additional elements identified otherwise and conclusions from Step 2A Prong 2 are carried over for evaluating if the claim, as a whole, amounts to an inventive concept that is significantly more than the judicial exception:
receiving borehole location parameters for the borehole, borehole associated data relating to the borehole, and a geographic location of interest for the borehole – This limitation has been identified as the insignificant extra solution activity of mere data gathering, as stated previously. Under broadest reasonable interpretation and when read in light of the specification, the claim limitation includes receiving data over a network. This computer functionality have been recognized by the courts as well understood, routine, and conventional computer functionality when claimed in a merely generic manner, such as in this limitation.
The courts have found that simply appending insignificant extra solution activities that are well-understood, routine, and conventional activities to the judicial exception does not qualify the limitations as “significantly more” than the recited judicial exception. The remaining additional element was identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)), as stated previously. The courts have found reciting the words “apply it” does not qualify the limitations as “significantly more” than the recited judicial exception.
With the additional elements viewed independently and as part of the ordered combination, the claim as a whole does not appear to amount to significantly more than the recited judicial exception because the claim is using generic computing components recited at a high level of generality and functioning in their normal capacity in conjunction with well-understood, routine, and conventional activity to enable the performance of a task that can practically be performed within the human mind or using pen and paper as an assistive physical aid. The value obtained from the abstract idea steps is used in a generic and expected way that amounts to generally applying the judicial exception for the intended use. Therefore, the claim does not include additional elements, alone or in combination that are sufficient to amount to significantly more than the recited judicial exception.
Conclusion: Based on this rationale, the claim has been deemed to be ineligible subject matter under 35 U.S.C. 101.
Dependent Claims:
Examiner notes limitations identified as judicial exceptions are indicated in italicized bold and limitations identified as additional elements are indicated using italics.
Claim 2
Step 1: Regarding dependent claim 2, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 2 additionally recites the limitation disapproving a borehole design concept from moving forward when an associated concept grouping score fails against a concept grouping risk tolerance parameter which can reasonably be read to entail evaluating a grouping score with regard to a grouping risk tolerance parameter. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Furthermore, the comparison of one numeric value to another is the recitation of a mathematical relationship between values. Accordingly, the claim also recites the abstract idea of mathematical concepts.
Step 2A Prong 2 & Step 2B: Claim 2 does not recite any additional elements that would integrate the judicial exceptions into a practical application nor amount to significantly more.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 3
Step 1: Regarding dependent claim 3, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 3 additionally recites the limitation determining more than one borehole design that has a respective final risk score that meets or is better than the first risk tolerance parameter; and which can reasonably be read to entail evaluating the risk score with respect to a risk tolerance parameter to make a judgment on borehole designs. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Furthermore, the comparison of the risk score value with respect to a risk tolerance parameter is the recitation of a mathematical relationship between two numbers and therefore the claim also recites the abstract idea of mathematical concepts. The claim additionally recites the limitation recommending a recommended borehole design from the more than one borehole design, utilizing the sub-risk score and the final risk score for each of the one or more borehole design concepts which can reasonably be read to entail evaluating the sub-risk score and the final risk score for borehole design concepts in order to make a judgment as to a recommended borehole design. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Step 2A Prong 2 & Step 2B: Claim 2 does not recite any additional elements that would integrate the judicial exceptions into a practical application nor amount to significantly more.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 4
Step 1: Regarding dependent claim 4, the judicial exception of independent claim 1 and dependent claim 3 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 4 does not recite any additional judicial exceptions.
Step 2A Prong 2: Claim 4 additionally recites the limitation wherein the first risk tolerance parameter utilizes two or more risk tolerance parameters, where each risk tolerance parameter in the two or more risk tolerance parameters is associated with a different borehole design risk level. This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) The courts have ruled generally linking the use of the judicial exception to a particular technological environment or field of use does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to generally linking the judicial exception to a particular technological environment or field of use are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 5
Step 1: Regarding dependent claim 5, the judicial exception of independent claim 1 and dependent claim 3 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 5 does not recite any additional judicial exceptions.
Step 2A Prong 2: Claim 5 additionally recites the limitation wherein the recommending is performed by a machine learning system. This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) because the limitation invokes the use of generic computing components to perform an existing task. The courts have ruled invoking the use of computers to perform an existing task does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to using computers in their normal capacity and in a generic way to enable an existing task are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 6
Step 1: Regarding dependent claim 6, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 6 additionally recites the limitation grouping the one or more risks from the one or more borehole design concepts utilizing a risk level with at least two levels, which can reasonably be read to entail evaluating borehole design concepts with risk levels to make a judgment on how the risks should be grouped. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Step 2A Prong 2 & Step 2B: Claim 6 does not recite any additional elements that would integrate the judicial exceptions into a practical application nor amount to significantly more.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 7
Step 1: Regarding dependent claim 7, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 7 additionally recites the limitation selecting a risk matrix from a risk model, and the assigning the one or more risks utilizes the risk matrix, which can reasonably be read to entail evaluating a risk model to determine a risk matrix that is appropriate for the application. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Step 2A Prong 2 & Step 2B: Claim 7 does not recite any additional elements that would integrate the judicial exceptions into a practical application nor amount to significantly more.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 8
Step 1: Regarding dependent claim 8, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 8 additionally recites the limitation modifying at least one risk from the one or more risks in a third drilling phase based on the second drilling phase; and updating a risk matrix, which can reasonably be read to entail evaluating risks to make a judgment as to how the risk should be adjusted between a second and third phase of drilling and modifying a risk matrix based on the evaluation. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Step 2A Prong 2 & Step 2B: Claim 8 does not recite any additional elements that would integrate the judicial exceptions into a practical application nor amount to significantly more.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 9
Step 1: Regarding dependent claim 9, the judicial exception of independent claim 1 and dependent claim 8 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 9 does not recite any additional judicial exceptions.
Step 2A Prong 2: Claim 9 additionally recites the limitation wherein the risk matrix is a new risk matrix. This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)).The courts have ruled generally linking the use of a judicial exception to a particular environment or field of use does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to generally linking the use of a judicial exception to a particular technological environment or field of use are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 10
Step 1: Regarding dependent claim 10, the judicial exception of independent claim 1 and dependent claim 8 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 10 additionally recites the limitation wherein the modifying at least one risk includes selecting a risk category and at least one risk category attribute, which can reasonably be read to entail making a judgement as to which risk category and risk category attribute to select. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Step 2A Prong 2 & Step 2B: Claim 10 does not recite any additional elements that would integrate the judicial exceptions into a practical application nor amount to significantly more.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 11
Step 1: Regarding dependent claim 11, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 1 additionally recites the limitation wherein the generating the sub-risk score utilizes a rank, weighting parameter, or priority indicator for each risk in each of the one or more borehole design concepts, which can reasonably be read to entail evaluating borehole design concepts to derive a score using an algorithm and also utilizing rank, weighting parameter or priority indicator. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Furthermore, because the limitation recites a rank, weighting parameter, and priority indicator, the claim also recites a numeric value corresponding to mathematical relationships amongst other values. Therefore, the claim additionally recites the abstract idea of mathematical concepts.
Step 2A Prong 2 & Step 2B: Claim 11 does not recite any additional elements that would integrate the judicial exceptions into a practical application nor amount to significantly more.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 12
Step 1: Regarding dependent claim 12, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 12 additionally recites the limitation wherein the generating the sub-risk score utilizes a statistics-based algorithm to combine a risk score for each of the one or more risks for each risk type of the one or more risk types, and the statistics-based algorithm utilizes one of a sum, an average, a mean, or a weighted value, which can reasonably be read to entail evaluating an algorithm to determine the scores. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Furthermore, because the algorithms are explicit recitations of mathematical calculations, the claim limitation further includes the recitation of the abstract ideal of mathematical concepts.
Step 2A Prong 2 & Step 2B: Claim 12 does not recite any additional elements that would integrate the judicial exceptions into a practical application nor amount to significantly more.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 13
Step 1: Regarding dependent claim 13, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 13 does not recite any additional judicial exceptions.
Step 2A Prong 2: Claim 13 additionally recites the limitation wherein the borehole associated data is received from one or more sensors located downhole the borehole. This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) and Field of Use and Technological Environment (MPEP 2106.05(h)).The courts have ruled appending insignificant extra solution activity to a judicial exception and generally linking the use of a judicial exception to a particular technological environment or field of use does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: Because a limitation was found to be Insignificant Extra Solution Activity (MPEP 2106.05(g)), further analysis is required to determine if the limitation is well-understood, routine, and conventional activity. Receiving data is a computer function that is recognized by the courts as well understood routine and conventional activity when claimed in a merely generic manner, such as within this claim. The courts have found that limitations that amount to appending well-understood, routine and conventional activity to the judicial exception and generally linking the use of the judicial exception to a particular technological environment or field of use are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 15
Step 1: Regarding dependent claim 15, the judicial exception of independent claim 14 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 15 additionally recites the limitation and perform a risk analysis and recommendation process to recommend a recommended borehole design using the borehole location parameters, the borehole associated data, the geographic location of interest, the one or more borehole design concepts, and the one or more risks., which can reasonably be read to entail evaluating steps for a risk analysis and making judgment based on the analysis for a recommendation for the borehole design. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Step 2A Prong 2: Claim 15 additionally recites the limitation a machine learning system, which has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) as using a generic computer to perform an existing task. The claim further recites the limitation configured to communicate with the data transceiver and the borehole risk analyzer. This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering and outputting The courts have ruled that appending insignificant extra solution activity to a judicial exception does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: Because a limitation was identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)), the limitation requires further evaluation to determine if the element is well-understood, routine, and conventional activity. The courts recognize the computer functions of receiving and transmitting data as well understood routine and conventional computer functions when claimed in a merely generic manner, such as in this claim. The courts have found that limitations that amount to invoking the use of generic computers as tools to perform existing processes and appending well understood routine and conventional activity to a judicial exception are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 16
Step 1: Regarding dependent claim 16, the judicial exception of independent claim 14 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 16 does not recite any additional judicial exceptions.
Step 2A Prong 2: Claim 16 additionally recites the limitation a result transceiver, which has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of generic computing components to perform an existing process. The claim further recites configured to communicate results, interim outputs, the one or more risks, the sub-risk score for each of the one or more borehole design concepts, and the final risk score to a user system, a data store, or a computing system which has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering and outputting. The courts have ruled invoking the use of computers to perform an existing process and appending insignificant extra solution activity to a judicial exception does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: Because an additional element was identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)), the limitation requires further analysis to determine if the limitation is well-understood, routine, and conventional activity. Communicating results encompasses receiving and transmitting data over a network which has been recognized by the courts as well-understood, routine and conventional computer functionality when claimed in a generic manner. The courts have found that limitations that amount to using generic computers in their normal capacity to perform existing tasks and appending well-understood, routine, and conventional activity are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 17
Step 1: Regarding dependent claim 17, the judicial exception of independent claim 14 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 17 does not recite any additional judicial exceptions.
Step 2A Prong 2: Claim 17 additionally recites the limitation wherein the computing system is a borehole operation planning system. This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)). The courts have ruled generally linking the use of a judicial exception to a particular technological environment or field of use does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to generally linking the use of a judicial exception to a particular technological environment or field of use are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 18
Step 1: Regarding dependent claim 18, the judicial exception of independent claim 14 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 18 additionally recites the limitation wherein an output from the user system is used to update a risk matrix of a risk model, which can reasonably be read to entail evaluating output data to determine a modification of the values of the risk matrix. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Step 2A Prong 2 & Step 2B: Claim 18 does not recite any additional elements that would integrate the judicial exceptions into a practical application nor amount to significantly more.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 19
Step 1: Regarding dependent claim 19, the judicial exception of independent claim 14 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 19 additionally recites the limitation evaluate more than one borehole design, and select for recommendation one recommended borehole design from the more than one borehole designs using ranks, weighting parameters, priority indicators, or statistics-based algorithms applied to the one or more risks, the sub-risk score for each of the one or more borehole design concepts, or the final risk score, which can reasonably be read to entail evaluating borehole designs and performing risk evaluation using numerical methods to ultimately choose and recommend appropriate design. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Because the claim further recites statistics-based algorithms (and when read in light of the specification where statistics-based algorithms include sum, average, mean and median), the claim also recites mathematical calculations and therefore recites the abstract idea of mathematical concepts.
Step 2A Prong 2: Claim 19 additionally recites the limitation wherein the borehole risk analyzer is further configured to. This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)). The courts have ruled invoking generic computers as a tool perform an existing process does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount using generic computers in their normal capacity to perform existing processes are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Jeong et al. (U.S. Patent Pub. No. 2020/0355839), hereinafter referred to as Jeong, and further in view of Veeningen et al. (US Patent Pub No 2005/0236184), hereinafter referred to as Veeningen.
Regarding claim 1, Jeong discloses (except the limitations surrounded by brackets ([[..]])) A method of drilling boreholes in multiple phases using one or more risk scores for a borehole design, comprising: Examiner interprets this limitation as the method that identifies quantified risk values (risk scores) for a well plan of a wellbore (borehole design of the borehole) (“The next stage of the method 200 may be to build a machine learning model that identifies risks in a planned well based on drilling measurements and/or parameters." (Jeong, ¶43); "Such predictions may inform users of likely drilling risks, and thus allow for changed drilling plans (e.g., well location or trajectory, drilling equipment selection, drilling parameters)." (Jeong, ¶44); "A well plan may specify many different drilling parameters." (Jeong, ¶47); "The machine learning model may thus be configured to associate certain drilling parameters, conditions, etc., with certain risks, and thereby quantify the risks as a value, e.g., a percentage, ranking, etc." (Jeong, ¶48)). Drilling parameters are iteratively updated, thereby indicating that multiple phases occur during drilling, each phase consisting of an updated set of corresponding parameters ((Jeong, ¶53) "The remaining risks may be deemed acceptable, and thus the method 400 may proceed to drilling a well using the drilling parameters, as at 414. During the drilling, however, the method 400 may continue, e.g., in real-time, to evaluate risk. For example, the method 400 may include receiving measurements taken while drilling the well using the drilling parameters, as at 416. These measurements may then be fed to the machine learning model, which may again evaluate the risks associated with the drilling parameters, and may update the risk profile based in part on these measurements, as at 418. The measurements may include all the real-time drilling measurements (e.g. hookload, surface weight on bit, flow rate, rotation rate, stand pipe pressure, equivalent circulating density) and contextual information (e.g. mud density, wellbore geometry, and geomechanics information) which are used for building the machine learning model. If the risk profile again indicates that high risk areas are upcoming, the method 400 may loop back to adjusting one or more of the drilling parameters at 412 and iteratively update the drilling parameters, again, potentially in real time as drilling is underway.")
receiving borehole location parameters for the borehole, borehole associated data relating to the borehole, and a geographic location of interest for the borehole; Examiner interprets this limitation as receiving offset well data that includes the wellbore trajectory parameters (location parameters), drilling parameters and drilling equipment selection (borehole associated data), and well location (geographic location of interest) which can be assessed and modified for well plans (“The method 200 may include receiving offset well data, as at 202." (Jeong, ¶40); "Such predictions may inform users of likely drilling risks, and thus allow for changed drilling plans (e.g., well location or trajectory, drilling equipment selection, drilling parameters)." (Jeong, ¶44)).
determining one or more borehole design concepts for the borehole utilizing the borehole location parameters, the borehole associated data, and the geographic location of interest; Examiner interprets this limitation as specifying (determining) drilling parameters (design concepts) that include trajectory parameters, drilling parameters and equipment selection, and well location data. Examiner interprets the specified drilling parameters as the basis of the well plans that define the borehole design ((Jeong, ¶40) "FIG. 2 illustrates a flowchart of a method 200 for planning a well, according to an embodiment.” ); ((Jeong, ¶44) " Such predictions may inform users of likely drilling risks, and thus allow for changed drilling plans (e.g., well location or trajectory, drilling equipment selection, drilling parameters.").
[[assigning one or more risks to each of the one or more borehole design concepts; ]]
[[generating a sub-risk score for one or more risk types for each of the one or more borehole design concepts using the one or more risks;]]
[[generating a final risk score for each of the borehole design concepts, using a
sum algorithm to combine the sub-risk score for each of the one or more risk types; and]]
filtering non-satisfactory borehole design concepts from the one or more borehole design concepts when the [[final risk score]] for the respective borehole design concept fails to satisfy a first risk tolerance parameter or one or more [[sub-risk scores of the final risk score]] for the respective borehole design concept fails to satisfy a respective [[sub-risk]] risk tolerance parameter; Risk values are described as being compared with risk tolerance values ((Jeong, ¶48) "The machine learning model may thus be configured to associate certain drilling parameters, conditions, etc., with certain risks, and thereby quantify the risks as a value, e.g., a percentage, ranking, etc. This value may be compared with risk tolerance values, which may be predetermined or otherwise devised, in order to establish whether the risk is acceptable, or to qualify the risk as high, medium, low, etc."). Risk profiles are likewise evaluated with respect to tolerance values ((Jeong, ¶49) "The drilling risk profile may visually describe levels of drilling risk in the well with respect to time. FIG. 7 illustrates an example of a risk profile 700. In this example the risk profile 700 includes three levels of risk, high, medium, and low, which may be identified by the machine learning model based on the offset data. Further, the x-axis represents time ( or more specifically, in this case, sample number), and the y-axis represents is the risk indicator of the stuck-pipe events. If the computed risk (likelihood of the drilling events) is higher than a certain threshold value, the risk profile area may change color (represented in gray in FIG. 7)."). Drilling parameters are adjusted when a risk profile has been identified as high risk, thereby eliminating the particular drilling parameter from the design plan and effectively filtering the design plan from that parameter ((Jeong, ¶50-51) "The drilling risk profile 700 of FIG. 7 may correspond to the drilling parameters of FIG. 6, which were used in the engineering analysis of block 408 (the results of which are shown in FIG. 5). Thus, even though the drilling parameters passed the engineering analysis in block 408, the machine learning model's drilling risk profile indicates an area 702 of high risk. In response to one or more high risk areas being identified, the method 400 may proceed to adjusting one or more of the drilling parameters based on the drilling risk profile, as at 412. As shown in FIG. 8, an automated system may suggest deviations from the initial plan. For example, in over balance pressure, the initial plan 800 may be deviated from, as indicated at 802 in the later samples ( corresponding to the area of high risk 702), toward the right of the plot. For correcting the expected risks raised by Pre-Drill advisor, the controllable drilling parameters (mud weight, flowrate, drillstring velocity, speed (revolutions-per-minute or RPM), and rate of penetration (ROP)) may be adjusted."); ((Jeong, ¶52) " FIG. 10 illustrates a new risk profile, generated after deviating from the initial plan 800 according to the adjustments to the parameters illustrated in FIG. 9. As can be seen, the area of high risk is no longer present.")
determining the borehole design [[and a drilling bit]] in a first drilling phase from the one or more borehole design concepts [[using the final risk score and the sub-risk scores for each respective borehole design concept; and]] A well plan is described as being characterized by drilling parameters ((Jeong, ¶47) "A well plan may specify many different drilling parameters."). The drilling plan may be adjusted based on identified risk ((Jeong, ¶44) "Such predictions may inform users of likely drilling risks, and thus allow for changed drilling plans ( e.g., well location or trajectory, drilling equipment selection, drilling parameters)."); ((Jeong, ¶51) " In response to one or more high risk areas being identified, the method 400 may proceed to adjusting one or more of the drilling parameters based on the drilling risk profile, as at 412. As shown in FIG. 8, an automated system may suggest deviations from the initial plan."). Drilling parameters may be determined as an initial plan, as well as during iterations of evaluation throughout the drilling process, thereby indicating that at least one phase of drilling is considered with regard to establishing the plan ((Jeong, ¶52-53) "FIG. 10 illustrates a new risk profile, generated after deviating from the initial plan 800 according to the adjustments to the parameters illustrated in FIG. 9. As can be seen, the area of high risk is no longer present. The remaining risks may be deemed acceptable, and thus the method 400 may proceed to drilling a well using the drilling parameters, as at 414. During the drilling, however, the method 400 may continue, e.g., in real-time, to evaluate risk. For example, the method 400 may include receiving measurements taken while drilling the well using the drilling parameters, as at 416. These measurements may then be fed to the machine learning model, which may again evaluate the risks associated with the drilling parameters, and may update the risk profile based in part on these measurements, as at 418. The measurements may include all the real-time drilling measurements (e.g. hookload, surface weight on bit, flow rate, rotation rate, stand pipe pressure, equivalent circulating density) and contextual information (e.g. mud density, wellbore geometry, and geomechanics information) which are used for building the machine learning model. If the risk profile again indicates that high risk areas are upcoming, the method 400 may loop back to adjusting one or more of the drilling parameters at 412 and iteratively update the drilling parameters, again, potentially in real time as drilling is underway.")
drilling the borehole in a second drilling phase with the drilling [[bit]] selected by the borehole design in the first drilling phase. Discussion is provided that states predictions of the risks enable changes of drilling plans including drilling equipment selection, wherein a drilling tool is understood to be considered drilling equipment ((Jeong, ¶44) "With the machine learning model built, the method 200 may proceed to predicting drilling risks in the new well using the machine learning model, as at 210. Such predictions may inform users of likely drilling risks, and thus allow for changed drilling plans ( e.g., well location or trajectory, drilling equipment selection, drilling parameters)"). The drilling plan is used as a guide for drilling, indicating that drilling equipment in the drill plan will be used in the borehole where drilling is executed ((Jeong, ¶47) "A well plan may specify many different drilling parameters."); ((Jeong, ¶53) "The remaining risks may be deemed acceptable, and thus the method 400 may proceed to drilling a well using the drilling parameters, as at 414."). The drilling operation continues to iteratively operate and update the drilling plan according to parameters previously identified as the desirable controllable parameters, thereby indicating that equipment selected in a previous iteration of evaluation is used in a current iteration of evaluation (as different drilling phases) ((Jeong, ¶52-53) "FIG. 10 illustrates a new risk profile, generated after deviating from the initial plan 800 according to the adjustments to the parameters illustrated in FIG. 9. As can be seen, the area of high risk is no longer present. The remaining risks may be deemed acceptable, and thus the method 400 may proceed to drilling a well using the drilling parameters, as at 414. During the drilling, however, the method 400 may continue, e.g., in real-time, to evaluate risk. For example, the method 400 may include receiving measurements taken while drilling the well using the drilling parameters, as at 416. These measurements may then be fed to the machine learning model, which may again evaluate the risks associated with the drilling parameters, and may update the risk profile based in part on these measurements, as at 418. The measurements may include all the real-time drilling measurements (e.g. hookload, surface weight on bit, flow rate, rotation rate, stand pipe pressure, equivalent circulating density) and contextual information (e.g. mud density, wellbore geometry, and geomechanics information) which are used for building the machine learning model. If the risk profile again indicates that high risk areas are upcoming, the method 400 may loop back to adjusting one or more of the drilling parameters at 412 and iteratively update the drilling parameters, again, potentially in real time as drilling is underway.")
Jeong does not alone disclose; however, Jeong in view of Veeningen discloses assigning one or more risks to each of the one or more borehole design concepts; Individual risks are specified and correlated to a high risk, medium risk or low risk ((Veeningen, ¶920-924) "Risk Calculation #1-Individual Risk Calculation: Referring to the 'Risk Assessment Output Data' 18b1 set forth above, there are fifty-four (54) 'Individual Risks' currently specified. For an 'Individual Risk': a High risk=90, a Medium risk=70, and a Low risk=10"). Individual risks represent different borehole design concepts ((Veeningen, ¶248-302) "[0248] The following 'Individual Risks' are calculated (1) H2S and CO2 [[…]] (54) Cuttings.").
generating a sub-risk score for one or more risk types for each of the one or more borehole design concepts using the one or more risks; When read in light of the specification, a risk type may include a concept, category or group for a borehole design ((Instant specification, ¶17) "A final risk score can also specify a sub risk score per risk type, e.g., a concept, category, or group, for a borehole design."). A risk subcategory score is calculated for each risk value corresponding to an individual risk, wherein there are four subcategories by which risk scores can be computed ((Veeningen, ¶939-940) "Referring to the 'Risk Assessment Output Data'l8bl set forth above, the following 'Subcategory Risks' are defined: (a) gains, (b) losses, (c) stuck and (d) mechanical, where a 'Subcategory Risk' (or 'Risk Subcategory') is defined as follows:
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j=number of individual risks, "). The individual risk represents a different borehole design concept, as stated previously.
generating a final risk score for each of the borehole design concepts, using a sum algorithm to combine the sub-risk score for each of the one or more risk types; and A total risk is calculated by averaging (which includes a summation) the risk subcategory scores for each of the four subcategories ((Veeningen, ¶953-654) "Risk Calculation #5-Total Risk. The total risk calculation is based on the following categories: (a) gains, (b) losses, (c) stuck, and (d) mechanical.
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"). The risk subcategory scores consider each of the individual risks corresponding to a different borehole design concept, thereby indicating that the final risk score is generated with regard to each of the concepts.
…final risk score… sub-risk scores of the final score … sub-risk Sub-risk and final risk scores are generated using the methodologies, as stated above, to generate average individual risk values and actual risk values comprised of the average individual risk values. (Veeningen, ¶939-940); (Veeningen, ¶953-654). Subcategory risk tolerance parameters are characterized ((Veeningen, ¶944-946) "Red risk display for Risk Subcategory)=40. Yellow risk display for 20<=Risk Subcategory <40.Green risk display for Risk Subcategory<20")
determining a drilling bit Bit type is selected as part of the optimum well design ((Veeningen, ¶33) "The user input trajectory and earth properties parameters; the system use this data and various catalogs to calculate and deliver an optimum well design thereby generating a plurality of outputs, such as drill string design, casing seats, mud weights, bit selection and use, hydraulics, and the other essential factors for the drilling task."). Bit selection includes bit type ((Veeningen, ¶1030 and ¶1036) "Bit selection Output Data 42bl [1031] The 'Bit selection Output Data'42bl is generated by the 'Bit selection Algorithms'48. The 'Bit selection Output Data'42bl, that is generated by the 'Bit selection Algorithms' 48, includes the following types of output data:[[…]] [1036] (5) Bit Type"). ((Veeningen, ¶53) "Anew and novel algorithm used by the 'Automatic Well Planning Software System' selects appropriate bit types that are best suited to the anticipated rock strengths, hole sizes, and drilled intervals.")
using the final risk score and the sub-risk scores for each respective borehole design concept; and The methodology is described as leveraging a risk assessment to inform design decisions ((Veeningen, ¶33) " An 'Automatic Well Planning Software System' is disclosed in this specification. The 'Automatic Well Planning Software System' of the present invention is a "smart" tool for rapid creation of a detailed drilling operational plan that provides economics and risk analysis. The user inputs trajectory and earth properties parameters; the system uses this data and various catalogs to calculate and deliver an optimum well design thereby generating a plurality of outputs, such as drill string design, casing seats, mud weights, bit selection and use, hydraulics, and the other essential factors for the drilling task."); ((Veeningen, ¶117) " The 'Automatic Well Planning Software System' of the present invention is capable of delivering a comprehensive technical risk assessment, and it can do this automatically. Lacking an integrated model of the technical well design to relate design decisions to associated risks, the 'Automatic Well Planning Software System' can attribute the risks to specific design decisions and it can direct users to the appropriate place to modify a design choice in efforts to modify the risk profile of the well."). The risk analysis process includes generating the final risk score and the sub-risk score, as stated previously, thereby indicating that the design uses the final risk score and the sub risk scores ((Veeningen, ¶111-112) "The Risk Assessment sub-task 16a associated with the 'Automatic Well Planning Software System' of the present invention is a system that will automatically assess risks associated with the technical well design decisions in relation to the earth's geology and geomechanical properties and in relation to the mechanical limitations of the equipment specified or recommended for use. Risks are calculated in four ways: (1) by 'Individual Risk Parameters', (2) by 'Risk Categories', (3) by 'Total Risk', and ( 4) the calculation of 'Qualitative Risk Indices' for each.")
drilling bit Bit type is selected as part of the optimum well design ((Veeningen, ¶33) "The user input trajectory and earth properties parameters; the system use this data and various catalogs to calculate and deliver a optimum well design thereby generating a plurality of outputs, such as drill string design, casing seats, mu weights, bit selection and use, hydraulics, and the other essential factors for the drilling task."). Bit selection includes bit type ((Veeningen, ¶1030 and ¶1036) "Bit selection Output Data 42bl [1031] The 'Bit selection Output Data'42bl is generated by the 'Bit selection Algorithms'48. The 'Bit selection Output Data'42bl, that is generated by the 'Bit selection Algorithms' 48, includes the following types of output data:[[…]] [1036] (5) Bit Type"); ((Veeningen, ¶53) "Anew and novel algorithm used by the 'Automatic Well Planning Software System' selects appropriate bit types that are best suited to the anticipated rock strengths, hole sizes, and drilled intervals.")
Veeningen and Jeong are analogous arts to the claimed invention in that they are both related to the same field of endeavor of risk management and design optimization for wellbores. Jeong discloses a method that quantifies the value of risk for each individual drilling parameter and uses the quantified values to create an overall risk profile which contains the individual drilling parameters but does not provide detail as to how the individual risks are considered within the drilling profile ("The machine learning model may thus be configured to associate certain drilling parameters, conditions, etc., with certain risks, and thereby quantify the risks as a value, e.g., a percentage, ranking, etc." (Jeong, ¶48)); ("The method 400 may proceed to generating a drilling risk profile for the new well, using the machine learning model, based on the drilling parameters, as at 410. As mentioned above, the machine learning model may be trained using previously identified risk inferences in the offset well data, or through observations of drilling risk in association with drilling parameters in the offset well data." (Jeong, ¶48)); ("The drilling risk profile 700 of FIG. 7 may correspond to the drilling parameters of FIG. 6, which were used in the engineering analysis of block 408 (the results of which are shown in FIG. 5)." (Jeong, ¶50)). Veeningen discloses a method where subcategory risks are generated per category type and subsequently utilized to create a total risk score. By applying the risk assessment scoring methodology (that includes the explicit quantification of final risk and sub-risk scores) disclosed by Veeningen into the design methodology that considers risk in a less granular way, as disclosed by Jeong, one having skill in the art would arrive at the claimed invention. It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have incorporated the separate individual risk scores of each design parameter used to generate the total risk score, as taught by Veeningen, because considering the impact of each risk parameter with regard for the design task enables users to more accurately identify the type and location of risk such that drilling engineers can more effectively focus their efforts ((Veeningen, ¶62) "The risk assessment accurately identifies the type and location of risk in the wellbore enabling drilling engineers to focus their detailed engineering efforts most effectively"); ((Veeningen, ¶113) "Individual Risk Parameters are calculated along the measured depth of the well and color coded into high, medium, or low risk for display to the user. Each risk will identify to the user: an explanation of exactly what is the risk violation, and the value and the task in the workflow controlling the risk. These risks are calculated consistently and transparently allowing users to see and understand all of the known risks and how they are identified. These risks also tell the users which aspects of the well justify further engineering effort to investigate in more detail.); ((Veeningen, ¶117) "Lacking an integrated model of the technical well design to relate design decisions to associated risks, the 'Automatic Well Planning Software System' can attribute the risks to specific design decisions and it can direct users to the appropriate place to modify a design choice in efforts to modify the risk profile of the well.") Leveraging individual risk scores within the total risk score further enables detailed analysis to determine relative risk for different design tasks ((Veeningen, ¶116) "Risk indexing-Each individual risk parameter is used to produce an individual risk index which is a relative indicator of the likelihood that a particular risk will occur."). Furthermore, Jeong discloses selecting drilling equipment to employ during drilling but does not explicitly disclose the selection of a drill bit. However, Veeningen discloses explicitly selecting a drill bit as part of a plan based on risk. Because Jeong suggests selecting drilling equipment for use and Veeningen explicitly discloses a well-known and utilized piece of equipment for drilling, it would have been obvious to combine the prior art references to incorporate the explicit selection of the drill bit in the drilling equipment of the plan disclosed by Jeong. Accordingly, the combination of prior art references would have been obvious.
Regarding claim 2, the proposed combination discloses The method as recited in Claim 1, as stated previously.
The proposed combination in further view of Jeong discloses further comprises disapproving a borehole design concept from moving forward when [[an associated concept grouping score]] fails against a [[concept grouping]] risk tolerance parameter. Risk values are described as being compared with risk tolerance values ((Jeong, ¶48) "The machine learning model may thus be configured to associate certain drilling parameters, conditions, etc., with certain risks, and thereby quantify the risks as a value, e.g., a percentage, ranking, etc. This value may be compared with risk tolerance values, which may be predetermined or otherwise devised, in order to establish whether the risk is acceptable, or to qualify the risk as high, medium, low, etc."). Drilling parameters are adjusted when a risk profile has been identified as high risk, thereby eliminating the particular drilling parameter from the design plan and effectively filtering the design plan from that parameter ((Jeong, ¶50-51) "The drilling risk profile 700 of FIG. 7 may correspond to the drilling parameters of FIG. 6, which were used in the engineering analysis of block 408 (the results of which are shown in FIG. 5). Thus, even though the drilling parameters passed the engineering analysis in block 408, the machine learning model's drilling risk profile indicates an area 702 of high risk. In response to one or more high risk areas being identified, the method 400 may proceed to adjusting one or more of the drilling parameters based on the drilling risk profile, as at 412. As shown in FIG. 8, an automated system may suggest deviations from the initial plan. For example, in over balance pressure, the initial plan 800 may be deviated from, as indicated at 802 in the later samples ( corresponding to the area of high risk 702), toward the right of the plot. For correcting the expected risks raised by Pre-Drill advisor, the controllable drilling parameters (mud weight, flowrate, drillstring velocity, speed (revolutions-per-minute or RPM), and rate of penetration (ROP)) may be adjusted."); ((Jeong, ¶52) " FIG. 10 illustrates a new risk profile, generated after deviating from the initial plan 800 according to the adjustments to the parameters illustrated in FIG. 9. As can be seen, the area of high risk is no longer present.")
The proposed combination in view of Jeong does not explicitly disclose; however, the proposed combination in further view of Veeningen discloses an associated concept grouping score … concept grouping … Group/category/subcategory risks are considered as a level of granularity in the risk assessment ((Veeningen, ¶114) "Group/category risks are calculated by incorporating all of the individual risks in specific combinations. Each individual risk is a member of one or more Risk Categories. Four principal Risk Categories are defined as follows: (1) Gains, (2) Losses, (3) Stuck, and (4) Mechanical; since these four Rick Categories are the most common and costly groups of troublesome events in drilling worldwide. "); ((Veeningen, ¶938-939) "Risk Calculation #3-Risk Subcategory Referring to the 'Risk Assessment Output Data'l8bl set forth above, the following 'Subcategory Risks' are defined: (a) gains, (b) losses, (c) stuck and (d) mechanical, where a 'Subcategory Risk' (or 'Risk Subcategory') is defined as follows: "). The subcategories have corresponding tolerance parameters ((Veeningen, ¶944-946) "Red risk display for Risk Subcategory>=40. Yellow risk display for 20<=Risk Subcategory<=40. Green risk display for Risk Subcategory<20. ")
It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have further modified the proposed combination to include concept grouping risk scores and concept grouping risk tolerance parameters as another level of granularity by which to filter design concepts from the borehole design because a simple substitution of a known element for another to obtain predictable results would have led one having skill in the art to make the combination in order to arrive at the claimed invention. As stated previously, Jeong discloses the utilization of tolerance parameters compared to risks in order to inform adjustments of drilling plans. Veeningen provides a level of granularity to the risk assessment that Jeong does not provide. By imparting the subcategory risk and associated risk tolerance parameters into the methodology of Jeong that adjusts drilling parameters based on risks compared to tolerance values, one having skill in the art would arrive at the claimed invention. One having skill would particularly be compelled to incorporate the filtering of concepts at this level of granularity because Veeningen explicitly notes that the four risk categories analyzed are the most common and costly groups of troublesome events in drilling ((Veeningen, ¶114) "Group/category risks are calculated by incorporating all of the individual risks in specific combinations. Each individual risk is a member of one or more Risk Categories. Four principal Risk Categories are defined as follows: (1) Gains, (2) Losses, (3) Stuck, and (4) Mechanical; since these four Rick Categories are the most common and costly groups of troublesome events in drilling worldwide. "). Accordingly, it would have been obvious to one having skill in the art to assess and filter the drilling parameters against risks that most severely impact operations so as to reduce the likelihood of occurrence in a risk mitigation plan.
Regarding claim 3, the proposed combination discloses The method as recited in Claim 1, further comprising: as stated previously.
The proposed combination in further view of Jeong discloses determining more than one borehole design that has a respective [[final risk score]] that meets or is better than the first risk tolerance parameter; and Examiner interprets this limitation as generating an initial risk profile (borehole design) that includes the drilling parameters that define the well plan as the design (Jeong, Fig. 4 item 410) and subsequently generating another risk profile(s) (borehole design(s)) to update the parameters to generate a new plan (Jeong, Fig 4 item 412-418); (“FIG. 10 illustrates a new risk profile, generated after deviating from the initial plan 800 according to the adjustments to the parameters illustrated in FIG. 9." (Jeong, ¶52)). During drilling, additional evaluations are performed and the drilling parameters are adjusted accordingly to create additional designs ((Jeong, ¶53) "If the risk profile again indicates that high risk areas are upcoming, the method 400 may loop back to adjusting one or more of the drilling parameters at 412 and iteratively update the drilling parameters, again, potentially in real time as drilling is underway."). The risk profiles are evaluated visually for determining if high risk areas exist ((Jeong, ¶50) "Thus, even though the drilling parameters passed the engineering analysis in block 408, the machine learning model's drilling risk profile indicates an area 702 of high risk."). The risk profile is evaluated against a threshold value (as a risk tolerance parameter) ((Jeong, ¶49) "The drilling risk profile may visually describe levels of drilling risk in the well with respect to time. FIG. 7 illustrates an example of a risk profile 700. In this example the risk profile 700 includes three levels of risk, high, medium, and low, which may be identified by the machine learning model based on the offset data. Further, the x-axis represents time ( or more specifically, in this case, sample number), and the y-axis represents is the risk indicator of the stuck-pipe events. If the computed risk (likelihood of the drilling events) is higher than a certain threshold value, the risk profile area may change color (represented in gray in FIG. 7).")
recommending a recommended borehole design from the more than one borehole
design, [[utilizing the sub-risk score and the final risk score for each of the one or more borehole design concepts]] Examiner interprets this limitation as the method suggesting (recommending) adjustments to a well plan (borehole design) that deviate from the initial plan (more than one borehole design). The well plan is based on a risk profile which contains the risk rankings of the drilling parameters (“FIG. 8 illustrates plots of the example drilling parameters along with recommended adjustments thereto, according to an embodiment. FIG. 9 illustrates a view of a risk profile for the plan implementing the recommended adjustments, according to an embodiment.” (Jeong, ¶16- ¶17)); ("As shown in FIG. 8, an automated system may suggest deviations from the initial plan." (Jeong, ¶51)).
Jeong does not explicitly disclose; however, Veeningen discloses a final risk score. The total risk is computed and has associated risk tolerances for different levels of risk. (Veeningen, ¶954-957).
utilizing the sub-risk score and the final risk score for each of the one or more borehole design concepts Subcategory risk scores and a total risk score are computed (Veeningen, ¶954-957); (Veeningen, ¶938-939).The risk analysis (comprised of generating the sub category risk and total risk scores) is used to inform the design plan ((Veeningen, ¶33) "An 'Automatic Well Planning Software System' is disclosed in this specification. The 'Automatic Well Planning Software System' of the present invention is a "smart" tool for rapid creation of a detailed drilling operational plan that provides economics and risk analysis. The user inputs trajectory and earth properties parameters; the system uses this data and various catalogs to calculate and deliver an optimum well design thereby generating a plurality of outputs, such as drill string design, casing seats, mud weights, bit selection and use, hydraulics, and the other essential factors for the drilling task. System tasks are arranged in a single workflow in which the output of one task is included as input to the next.")
It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have further modified the proposed combination to utilize the scores for the risk analysis as disclosed by Veeningen in the design determination and recommendation because some teaching or suggestion would have led one having ordinary skill in the art to do so in order to arrive at the claimed invention. Jeong provides a primarily qualitative analysis for drilling parameter risk and risk profiles, though does discuss comparison of these elements with regard to a threshold value. Veeningen explicitly computes numeric values as scores to quantify the risk and particularly suggests that the risk analysis is leveraged to deliver an optimum well design without explicitly noting how it’s done. Accordingly, by providing the more comprehensive and detailed risk analysis with explicit scoring of risks disclosed by Veeningen into the methodology disclosed by Jeong, one having skill would arrive at the claimed invention.
Regarding claim 4, the proposed combination discloses The method as recited in Claim 3, as stated previously.
The proposed combination in further view of Jeong discloses wherein the first risk tolerance parameter utilizes two or more risk tolerance parameters, where each risk tolerance parameter in the two or more risk tolerance parameters is associated with a different borehole design risk level. Examiner interprets this limitation as utilizing risk tolerance thresholds for generating the drilling risk profile that is used for the recommendation, wherein three risk tolerance values are utilized and correspond to the risk levels of low, medium, and high ("The machine learning model may thus be configured to associate certain drilling parameters, conditions, etc., with certain risks, and thereby quantify the risks as a value, e.g., a percentage, ranking, etc. This value may be compared with risk tolerance values, which may be predetermined or otherwise devised, in order to establish whether the risk is acceptable, or to qualify the risk as high, medium, low, etc." (Jeong, ¶48)).
Regarding claim 5, the proposed combination discloses The method as recited in Claim 3, as stated previously.
The proposed combination in further view of Jeong discloses wherein the recommending is performed by a machine learning system. Examiner interprets this limitation as an automated system that generates the suggested deviations. Suggestions may be based off of search algorithms such as a Nearest Neighbor Search and grid-search which are utilized within machine learning for hyperparameter tuning, further indicating that the automated suggestion system incorporates machine learning. ("Thus, even though the drilling parameters passed the engineering analysis in block 408, the machine learning model's drilling risk profile indicates an area 702 of high risk. In response to one or more high risk areas being identified, the method 400 may proceed to adjusting one or more of the drilling parameters based on the drilling risk profile, as at 412. As shown in FIG. 8, an automated system may suggest deviations from the initial plan. For example, in over balance pressure, the initial plan 800 may be deviated from, as indicated at 802 in the later samples (corresponding to the area of high risk 702), toward the right of the plot. For correcting the expected risks raised by Pre-Drill advisor, the controllable drilling parameters (mud weight, flowrate, drillstring velocity, speed (revolutions-per-minute or RPM), and rate of penetration (ROP)) may be adjusted. For example, a Nearest Neighbor Search (NNS) algorithm may be employed to select settings within time and cost as shown in the FIG. 9. Other search algorithms may also be employed, such as grid-search techniques, depending, for example, on the reliability of the mitigation plan, computational cost, and speed." (Jeong, ¶50-51)).
Regarding claim 6, the proposed combination discloses The method as recited in Claim 1, further comprising: as stated previously.
The proposed combination in further view of Jeong discloses grouping the one or more risks from the one or more borehole design concepts utilizing a risk level with at least two levels. Examiner interprets this limitation as including the drilling parameters’ risks associated with a well plan within a risk profile (the grouping of the risks from the borehole design concepts) that includes three levels of risks ("In this example, the risk profile 700 includes three levels of risk, high, medium, and low, which may be identified by the machine learning model based on the offset data." (Jeong, ¶49)).
Regarding claim 7, the proposed combination discloses The method as recited in Claim 1, further comprising: as stated previously.
The proposed combination in further view of Veeningen discloses selecting a risk matrix from a risk model, and When read in light of the specification(¶32), risks can be assigned from a library of risks stored as part of a risk model. A risk assessment catalog contains a risk matrix catalog ((Veeningen, ¶218-220) "Risk Assessment Catalogs 28. The following paragraphs will set forth the 'Risk Assessment Catalogs'28 which are used by the 'Risk Assessment Logical Expressions'22 and the 'Risk Assessment Algorithms'24. Values of the Catalogs 28 that are used as input data for Risk Assessment Algorithms 24 and the Risk Assessment Logical Expressions 22 include the following: (1) Risk Matrix Catalog"). The risk matrix can be selected, as per the GUI in Figure 2C.
the assigning the one or more risks utilizes the risk matrix. The assigning of the individual risks includes the assignment of both a value and a color, wherein it is understood the color coding of the corresponding risk level correlates to that of the risk matrix ((Veeningen, ¶921-927) " Referring to the 'Risk Assessment Output Data'l8bl set forth above, there are fifty-four (54) 'Individual Risks' currently specified. For an 'Individual Risk': a High risk=90, a Medium risk=70, and a Low risk=lO High risk color code=Red Medium risk color code=Yellow Low risk color code=Green"). Values from catalogs, including a risk matrix catalog, are used as part of the Risk assessment Logical expressions, wherein the risk assessment logical expressions are leveraged to provide the assignment of the color/number value of the individual risk ((Veeningen, ¶219-220) " The following paragraphs will set forth the 'Risk Assessment Catalogs'28 which are used by the 'Risk Assessment Logical Expressions'22 and the 'Risk Assessment Algorithms'24. Values of the Catalogs 28 that are used as input data for Risk Assessment Algorithms 24 and the Risk Assessment Logical Expressions 22 include the following: (1) Risk Matrix Catalog"); ((Veeningen, ¶928) " If the 'Risk Assessment Logical Expressions'22 assigns a 'high risk' rank to a particular 'Input Data calculation result', the 'Risk Assessment Algorithms'24 will then assign a value '90' to that 'Input Data calculation result' and a color 'red' to that 'Input Data calculation result'.")
Regarding claim 8, the proposed combination discloses The method as recited in Claim 1, further comprising: as stated previously.
The proposed combination in further view of Veeningen discloses (except the limitations surrounded by brackets ([[..]])) modifying at least one risk from the one or more risks [[in a third drilling phase based on the second drilling phase; and ]] User functionality exists for customizing risk thresholds for the 54 individual risk categories ((Veeningen, ¶57) "In FIG.4, in the 'Automatic Well Planning Software System', drilling event 'risks' are quantified in a total of 54 risk categories of which the user can customize the risk thresholds."). The drilling parameters
updating a risk matrix. A risk matrix is produced as a result, as depicted in Figures 2C, 8, 15, 15c. The results are output to the user ((Veeningen, ¶107) In FIG. 8, those plurality of tasks are divided into four groups: (1) Input task 10, where input data is provided, (2) Wellbore Geometry task 12 and Drilling Parameters task 14, where calculations are performed, and (3) a Results task 16, where a set of results are calculated and presented to a user. [[…]] The Results task 16 includes the following sub-tasks: (1) Risk Assessment 16a, (2) Risk Matrix, (3) Time and cost data, (4) Time and cost chart, (5) Monte Carlo, (6) Monte Carlo graph, (7) Summary report, and (8) montage."). The outputs are described as being modifiable by the user ((Veeningen, ¶33) "The user can modify most outputs, which permits fine-tuning of the input values
for the next task.")
The proposed combination in further view of Jeong discloses modifying drilling parameters corresponding to risk during drilling wherein the drilling parameters are modified iteratively (in a plurality of phases) so as to encompass modifying the risks corresponding to updated parameters in a third drilling phase based on the second drilling phase; and ((Jeong, ¶49-50) "If the computed risk (likelihood of the drilling events) is higher than a certain threshold value, the risk profile area may change color (represented in gray in FIG. 7). The drilling risk profile 700 of FIG. 7 may correspond to the drilling parameters of FIG. 6, which were used in the engineering analysis of block 408 (the results of which are shown in FIG. 5)."); ((Jeong, ¶53) "During the drilling, however, the method 400 may continue, e.g., in real-time, to evaluate risk. For example, the method 400 may include receiving measurements taken while drilling the well using the drilling parameters, as at 416. These measurements may then be fed to the machine learning model, which may again evaluate the risks associated with the drilling parameters, and may update the risk profile based in part on these measurements, as at 418. The measurements may include all the real-time drilling measurements (e.g. hookload, surface weight on bit, flow rate, rotation rate, stand pipe pressure, equivalent circulating density) and contextual information (e.g. mud density, wellbore geometry, and geomechanics information) which are used for building the machine learning model. If the risk profile again indicates that high risk areas are upcoming, the method 400 may loop back to adjusting one or more of the drilling parameters at 412 and iteratively update the drilling parameters, again, potentially in real time as drilling is underway. As shown in FIG. 10, the projected risk (i.e., the risk established prior to drilling), represented by line 1002, may be modified to more accurately reflect the drilling conditions as those drilling conditions become known based on the measurements taken during the drilling process. As such, the "actual" risk 1004may be determined, which may be different from the projected risk, as can be seen in FIG. 10.")
Regarding claim 9, the proposed combination discloses The method as recited in Claim 8, as stated previously. The proposed combination in further view of Veeningen discloses wherein the risk matrix is a new risk matrix. A risk matrix is produced as a result, as depicted in Figures 2C, 8, 15, 15c. The results are output to the user ((Veeningen, ¶107) In FIG. 8, those plurality of tasks are divided into four groups: (1) Input task 10, where input data is provided, (2) Wellbore Geometry task 12 and Drilling Parameters task 14, where calculations are performed, and (3) a Results task 16, where a set of results are calculated and presented to a user. [[…]] The Results task 16 includes the following sub-tasks: (1) Risk Assessment 16a, (2) Risk Matrix, (3) Time and cost data, (4) Time and cost chart, (5) Monte Carlo, (6) Monte Carlo graph, (7) Summary report, and (8) montage.").
Regarding claim 10, the proposed combination discloses The method as recited in Claim 8, as stated previously. The proposed combination in further view of Jeong discloses wherein the modifying at least one risk includes selecting a risk category and at least one risk category attribute. User functionality exists for customizing risk thresholds for the 54 individual risk categories, wherein the threshold is understood to be an attribute of the risk category ((Veeningen, ¶57) "In FIG.4, in the 'Automatic Well Planning Software System', drilling event 'risks' are quantified in a total of 54 risk categories of which the user can customize the risk thresholds.")
Regarding claim 11, the proposed combination discloses The method as recited in Claim 1, as stated previously.
The proposed combination in further view of Veeningen discloses wherein the generating the sub-risk score utilizes a rank, weighting parameter, or priority indicator for each risk in each of the one or more borehole design concepts. The risk subcategory is calculated utilizing a severity (as a weighting parameter) and a contribution indicator (as a priority indicator) for each value of j through n of the individual risks, which are associated with 54 parameters ((Veeningen, ¶939-943) "Referring to the 'Risk Assessment Output Data'l8bl set forth above, the following 'Subcategory Risks' are defined: (a) gains, (b) losses, (c) stuck and (d) mechanical, where a 'Subcategory Risk' (or 'Risk Subcategory')
is defined as follows:
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j=number of individual risks, 0<=Severity<=5, and Ni=either 1 or 0 depending on whether the Risk Valuei contributes to the sub category Severityi=from the risk matrix catalog. '"). A plurality of ranks is additionally produced for the subcategory risks ((Veeningen, ¶931) "However, in addition, in response to the 'Ranked Individual Risks' from the Logical Expressions 22, the Risk Assessment Algorithms 24 will also generate a plurality of ranked 'Risk Categories' and a plurality of ranked 'Subcategory Risks'")
Regarding claim 12, the proposed combination discloses The method as recited in Claim 1, as stated previously. The proposed combination in further view of Veeningen discloses wherein the generating the sub-risk score utilizes a statistics-based algorithm to combine a risk score for each of the one or more risks for each risk type of the one or more risk types, and the statistics-based algorithm utilizes one of a sum, an average, a mean, or a weighted value. The risk subcategory calculation uses a divided summation that includes a severity value, wherein the individual risk scores for each type of individual risk are combined through the summation ((Veeningen, ¶39) " Referring to the 'Risk Assessment Output Data'l8bl set forth above, the following 'Subcategory Risks' are defined: (a) gains, (b) losses, (c) stuck and (d) mechanical, where a 'Subcategory Risk' (or 'Risk Subcategory') is defined as follows:
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Regarding claim 13, the proposed combination discloses The method as recited in Claim 1, as stated previously. The proposed combination in further view of Jeong discloses wherein the borehole associated data is received from one or more sensors located downhole the borehole. Examiner interprets this limitation as receiving measurements taken while drilling the well where the measurements may include real-time drilling measurements obtained via sensing from sensors that are associated with downhole equipment (“The entities 122 may include entities based on data acquired via sensing, observation, etc. (e.g., the seismic data 112 and other information 114). An entity may be characterized by one or more properties (e.g., a geometrical pillar grid entity of an earth model may be characterized by a porosity property). Such properties may represent one or more measurements (e.g., acquired data), calculations, etc.” (Jeong, ¶26); “As an example, the geologic environment 150 may be outfitted with any of a variety of sensors, detectors, actuators, etc. For example, equipment 152 may include communication circuitry to receive and to transmit information with respect to one or more networks 155. Such information may include information associated with downhole equipment 154, which may be equipment to acquire information, to assist with resource recovery, etc. Other equipment 156 may be located remote from a well site and include sensing, detecting, emitting or other circuitry.” (Jeong, ¶37); "For example, the method 400 may include receiving measurements taken while drilling the well using the drilling parameters, as at 416… The measurements may include all the real-time drilling measurements (e.g. hookload, surface weight on bit, flow rate, rotation rate, stand pipe pressure, equivalent circulating density) and contextual information (e.g. mud density, wellbore geometry, and geomechanics information) which are used for building the machine learning model." (Jeong, ¶53)).
Regarding claim 14, Jeong discloses (except the limitations surrounded by brackets ([[..]])) A system to determine [[a type of drilling mud]] to use in a borehole using one or more risk scores for a borehole design of the borehole, comprising: Drilling parameters for a well are determined by a computing system that determines corresponding risk profiles for a design ((Jeong, ¶5) " Embodiments of the disclosure may also provide a computing system including one or more processors and a memory system including one or more non-transitory computer- readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations. The operations include receiving offset well data collected while drilling one or more offset wells, generating a machine learning model configured to predict drilling risks from drilling measurements or inferences, based on the offset well data, receiving drilling parameters for a new well, determining that the drilling parameters are within an engineering design window, generating a drilling risk profile for the new well using the machine learning model, and adjusting one or more of the drilling parameters for the new well, after determining the drilling parameters are within the engineering design window, and after determining the drilling risk profile, based on the drilling risk profile")
a data transceiver, When read in light of the specification, the data transceiver can be part of the borehole risk analyzer (Instant Specification ¶49). When further read in light of the specification, the borehole analyzer can be a machine learning system (Instant Specification, ¶50) and can include memory or data storage as well as a processor (Instant Specification, ¶52). Therefore, both of these components can reasonably read to be equivalent to the computing system as taught by Jeong Fig. 11, ¶54-¶60. configured to receive borehole location parameters for the borehole, borehole associated data relating to the borehole, and a geographic location of interest for the borehole; and Examiner interprets this limitation as receiving offset well data that includes the wellbore trajectory parameters (location parameters), drilling parameters and drilling equipment selection (borehole associated data), and well location (geographic location of interest) which can be assessed and modified for well plans (“The method 200 may include receiving offset well data, as at 202." (Jeong, ¶40); "Such predictions may inform users of likely drilling risks, and thus allow for changed drilling plans (e.g., well location or trajectory, drilling equipment selection, drilling parameters)." (Jeong, ¶44)).
a borehole risk analyzer, configured to: communicate with the data transceiver A risk prediction module is part of the same computing system used to receive data as in that stated above ((Jeong, ¶57) " In some embodiments, computing system 1100 contains one or more risk prediction module(s) 1108.") and determine one or more borehole design concepts for the borehole utilizing the borehole location parameters, the borehole associated data, and the geographic location of interest; Examiner interprets this limitation as specifying (determining) drilling parameters (design concepts) that include trajectory parameters, drilling parameters and equipment selection, and well location data. Examiner interprets the specified drilling parameters as the basis of the well plans that define the borehole design ((Jeong, ¶40) "FIG. 2 illustrates a flowchart of a method 200 for planning a well, according to an embodiment.” ); ((Jeong, ¶44) " Such predictions may inform users of likely drilling risks, and thus allow for changed drilling plans (e.g., well location or trajectory, drilling equipment selection, drilling parameters."). [[assign one or more risks to each of the one or more borehole design concepts; ]]
[[generate a sub-risk score for one or more risk types for each of the one or more borehole design concepts;]]
[[generate a final risk score for each of the one or more borehole design concepts, using a weighted value algorithm to combine the sub-risk score for each of the one or more risk types;]]
filter non-satisfactory borehole design concepts from the one or more borehole design concepts when the [[final risk score]] for the respective borehole design concept fails to satisfy a first risk tolerance parameter or one or more [[sub-risk scores of the final risk score]] for the respective borehole design concept fails to satisfy a respective [[sub-risk]] risk tolerance parameter; Risk values are described as being compared with risk tolerance values ((Jeong, ¶48) "The machine learning model may thus be configured to associate certain drilling parameters, conditions, etc., with certain risks, and thereby quantify the risks as a value, e.g., a percentage, ranking, etc. This value may be compared with risk tolerance values, which may be predetermined or otherwise devised, in order to establish whether the risk is acceptable, or to qualify the risk as high, medium, low, etc."). Risk profiles are likewise evaluated with respect to tolerance values ((Jeong, ¶49) "The drilling risk profile may visually describe levels of drilling risk in the well with respect to time. FIG. 7 illustrates an example of a risk profile 700. In this example the risk profile 700 includes three levels of risk, high, medium, and low, which may be identified by the machine learning model based on the offset data. Further, the x-axis represents time ( or more specifically, in this case, sample number), and the y-axis represents is the risk indicator of the stuck-pipe events. If the computed risk (likelihood of the drilling events) is higher than a certain threshold value, the risk profile area may change color (represented in gray in FIG. 7)."). Drilling parameters are adjusted when a risk profile has been identified as high risk, thereby eliminating the particular drilling parameter from the design plan and effectively filtering the design plan from that parameter ((Jeong, ¶50-51) "The drilling risk profile 700 of FIG. 7 may correspond to the drilling parameters of FIG. 6, which were used in the engineering analysis of block 408 (the results of which are shown in FIG. 5). Thus, even though the drilling parameters passed the engineering analysis in block 408, the machine learning model's drilling risk profile indicates an area 702 of high risk. In response to one or more high risk areas being identified, the method 400 may proceed to adjusting one or more of the drilling parameters based on the drilling risk profile, as at 412. As shown in FIG. 8, an automated system may suggest deviations from the initial plan. For example, in over balance pressure, the initial plan 800 may be deviated from, as indicated at 802 in the later samples ( corresponding to the area of high risk 702), toward the right of the plot. For correcting the expected risks raised by Pre-Drill advisor, the controllable drilling parameters (mud weight, flowrate, drillstring velocity, speed (revolutions-per-minute or RPM), and rate of penetration (ROP)) may be adjusted."); ((Jeong, ¶52) " FIG. 10 illustrates a new risk profile, generated after deviating from the initial plan 800 according to the adjustments to the parameters illustrated in FIG. 9. As can be seen, the area of high risk is no longer present.")
determine the borehole design in a planning phase of a borehole operation plan, from the one or more borehole design concepts [[using the final risk score and the sub-risk scores for each respective borehole design concept; and]] A well plan is described as being characterized by drilling parameters ((Jeong, ¶47) "A well plan may specify many different drilling parameters."). The drilling plan may be adjusted based on identified risk ((Jeong, ¶44) "Such predictions may inform users of likely drilling risks, and thus allow for changed drilling plans ( e.g., well location or trajectory, drilling equipment selection, drilling parameters)."). The design may be derived as part of an initial planning phase ((Jeong, ¶51) " In response to one or more high risk areas being identified, the method 400 may proceed to adjusting one or more of the drilling parameters based on the drilling risk profile, as at 412. As shown in FIG. 8, an automated system may suggest deviations from the initial plan.")
alter a drilling mud used in the borehole in an execution phase of the borehole operation plan, as selected by the borehole design. Discussion is provided that states predictions of the risks enable changes of drilling plans including drilling parameters ((Jeong, ¶44) "With the machine learning model built, the method 200 may proceed to predicting drilling risks in the new well using the machine learning model, as at 210. Such predictions may inform users of likely drilling risks, and thus allow for changed drilling plans ( e.g., well location or trajectory, drilling equipment selection, drilling parameters)"). Drilling parameters include mud weight, thereby indicating that the drilling plan characterizes the mud used and thus the drilling mud would be altered corresponding to a change in the mud weight parameter ((Jeong, ¶51) " For example, in over balance pressure, the initial plan 800 may be deviated from, as indicated at 802 in the later samples ( corresponding to the area of high risk 702), toward the right of the plot. For correcting the expected risks raised by Pre-Drill advisor, the controllable drilling parameters (mud weight, flowrate, drillstring velocity, speed (revolutions-per-minute or RPM), and rate of penetration (ROP)) may be adjusted.").The drilling plan is used as a guide for drilling, indicating that drilling equipment in the drill plan will be used in the borehole where drilling is executed ((Jeong, ¶47) "A well plan may specify many different drilling parameters."); ((Jeong, ¶53) "The remaining risks may be deemed acceptable, and thus the method 400 may proceed to drilling a well using the drilling parameters, as at 414.")
Jeong alone does not disclose; however, Jeong in view of Veeningen discloses determining by the system a type of drilling mud ((Veeningen, ¶92) "The system will generate the appropriate mud types, corresponding rheology, and composition based on the lithology, previous calculations, and the users selection.")
assign one or more risks to each of the one or more borehole design concepts; Individual risks are specified and correlated to a high risk, medium risk or low risk ((Veeningen, ¶920-924) "Risk Calculation #1-Individual Risk Calculation: Referring to the 'Risk Assessment Output Data' 18b1 set forth above, there are fifty-four (54) 'Individual Risks' currently specified. For an 'Individual Risk': a High risk=90, a Medium risk=70, and a Low risk=10"). Individual risks represent different borehole design concepts ((Veeningen, ¶248-302) "[0248] The following 'Individual Risks' are calculated (1) H2S and CO2 [[…]] (54) Cuttings.").
generate a sub-risk score for one or more risk types for each of the one or more borehole design concepts; When read in light of the specification, a risk type may include a concept, category or group for a borehole design ((Instant specification, ¶17) "A final risk score can also specify a sub risk score per risk type, e.g., a concept, category, or group, for a borehole design."). A risk subcategory score is calculated for each risk value corresponding to an individual risk, wherein there are four subcategories by which risk scores can be computed ((Veeningen, ¶939-940) "Referring to the 'Risk Assessment Output Data'l8bl set forth above, the following 'Subcategory Risks' are defined: (a) gains, (b) losses, (c) stuck and (d) mechanical, where a 'Subcategory Risk' (or 'Risk Subcategory') is defined as follows:
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j=number of individual risks, "). The individual risk represents a different borehole design concept, as stated previously.
generate a final risk score for each of the one or more borehole design concepts, using a weighted value algorithm to combine the sub-risk score for each of the one or more risk types; A total risk is calculated by averaging the risk subcategory scores for each of the four subcategories ((Veeningen, ¶953-954) "Risk Calculation #5-Total Risk. The total risk calculation is based on the following categories: (a) gains, (b) losses, (c) stuck, and (d) mechanical.
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"). The calculation for the Risk subcategory considers an severity value, thereby indicating that the calculation for risk total incorporates the weighted value as part of the calculation ((Veeningen, ¶939) "
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"). The risk subcategory scores consider each of the individual risks corresponding to a different borehole design concept, thereby indicating that the final risk score is generated with regard to each of the concepts. … final risk score … sub-risk scores of the final risk score … sub-risk … Sub-risk and final risk scores are generated using the methodologies, as stated above, to generate average individual risk values and actual risk values comprised of the average individual risk values. (Veeningen, ¶939-940); (Veeningen, ¶953-654). Subcategory risk tolerance parameters are characterized ((Veeningen, ¶944-946) "Red risk display for Risk Subcategory)=40. Yellow risk display for 20<=Risk Subcategory <40.Green risk display for Risk Subcategory<20")
using the final risk score and the sub-risk scores for each respective borehole design concept; and The methodology is described as leveraging a risk assessment to inform design decisions ((Veeningen, ¶33) " An 'Automatic Well Planning Software System' is disclosed in this specification. The 'Automatic Well Planning Software System' of the present invention is a "smart" tool for rapid creation of a detailed drilling operational plan that provides economics and risk analysis. The user inputs trajectory and earth properties parameters; the system uses this data and various catalogs to calculate and deliver an optimum well design thereby generating a plurality of outputs, such as drill string design, casing seats, mud weights, bit selection and use, hydraulics, and the other essential factors for the drilling task."); ((Veeningen, ¶117) " The 'Automatic Well Planning Software System' of the present invention is capable of delivering a comprehensive technical risk assessment, and it can do this automatically. Lacking an integrated model of the technical well design to relate design decisions to associated risks, the 'Automatic Well Planning Software System' can attribute the risks to specific design decisions and it can direct users to the appropriate place to modify a design choice in efforts to modify the risk profile of the well."). The risk analysis process includes generating the final risk score and the sub-risk score, as stated previously, thereby indicating that the design uses the final risk score and the sub risk scores ((Veeningen, ¶111-112) "The Risk Assessment sub-task 16a associated with the 'Automatic Well Planning Software System' of the present invention is a system that will automatically assess risks associated with the technical well design decisions in relation to the earth's geology and geomechanical properties and in relation to the mechanical limitations of the equipment specified or recommended for use. Risks are calculated in four ways: (1) by 'Individual Risk Parameters', (2) by 'Risk Categories', (3) by 'Total Risk', and ( 4) the calculation of 'Qualitative Risk Indices' for each.")
Veeningen and Jeong are analogous arts to the claimed invention in that they are both related to the same field of endeavor of risk management and design optimization for wellbores. Jeong discloses a method that quantifies the value of risk for each individual drilling parameter and uses the quantified values to create an overall risk profile which contains the individual drilling parameters but does not provide detail as to how the individual risks are considered within the drilling profile ("The machine learning model may thus be configured to associate certain drilling parameters, conditions, etc., with certain risks, and thereby quantify the risks as a value, e.g., a percentage, ranking, etc." (Jeong, ¶48)); ("The method 400 may proceed to generating a drilling risk profile for the new well, using the machine learning model, based on the drilling parameters, as at 410. As mentioned above, the machine learning model may be trained using previously identified risk inferences in the offset well data, or through observations of drilling risk in association with drilling parameters in the offset well data." (Jeong, ¶48)); ("The drilling risk profile 700 of FIG. 7 may correspond to the drilling parameters of FIG. 6, which were used in the engineering analysis of block 408 (the results of which are shown in FIG. 5)." (Jeong, ¶50)). Veeningen discloses a method where subcategory risks are generated per category type and subsequently utilized to create a total risk score. By applying the risk assessment scoring methodology (that includes the explicit quantification of final risk and sub-risk scores) disclosed by Veeningen into the design methodology that considers risk in a less granular way, as disclosed by Jeong, one having skill in the art would arrive at the claimed invention. It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have incorporated the separate individual risk scores of each design parameter used to generate the total risk score, as taught by Veeningen, because considering the impact of each risk parameter with regard for the design task enables users to more accurately identify the type and location of risk such that drilling engineers can more effectively focus their efforts ((Veeningen, ¶62) "The risk assessment accurately identifies the type and location of risk in the wellbore enabling drilling engineers to focus their detailed engineering efforts most effectively"); ((Veeningen, ¶113) "Individual Risk Parameters are calculated along the measured depth of the well and color coded into high, medium, or low risk for display to the user. Each risk will identify to the user: an explanation of exactly what is the risk violation, and the value and the task in the workflow controlling the risk. These risks are calculated consistently and transparently allowing users to see and understand all of the known risks and how they are identified. These risks also tell the users which aspects of the well justify further engineering effort to investigate in more detail.); ((Veeningen, ¶117) "Lacking an integrated model of the technical well design to relate design decisions to associated risks, the 'Automatic Well Planning Software System' can attribute the risks to specific design decisions and it can direct users to the appropriate place to modify a design choice in efforts to modify the risk profile of the well.") Leveraging individual risk scores within the total risk score further enables detailed analysis to determine relative risk for different design tasks ((Veeningen, ¶116) "Risk indexing-Each individual risk parameter is used to produce an individual risk index which is a relative indicator of the likelihood that a particular risk will occur."). Accordingly, the combination of prior art references would have been obvious.
Regarding claim 15, the proposed combination discloses The system as recited in Claim 14, further comprising: as stated previously.
The proposed combination discloses in further view of Jeong a machine learning system, configured to communicate with the data transceiver and the borehole risk analyzer, and perform a risk analysis and recommendation process to recommend a recommended borehole design using the borehole location parameters, the borehole associated data, the geographic location of interest, the one or more borehole design concepts, and the one or more risks. Examiner interprets this claim limitation as being taught by Jeong as an automated system (machine learning system) that identifies risk (performing risk analysis) and generates the suggested deviations (performs a recommendation process to yield recommendations) of a well plan (borehole design). The suggested deviations of the well plan are generated based on the drilling parameters (location parameters, associated data, geographic location of interest and design concepts) based on the drilling risk profile (which includes the risks of the drilling parameters). The automated system (machine learning system) is a method implemented on a computing system that also includes the data transceiver and borehole risk analyzer, as described in the rejection of Claim 14 and thus has the capability to communicate between the multiple subcomponents by transmitting data via software or hardware contained within the computing system. (“The next stage of the method 200 may be to build a machine learning model that identifies risks in a planned well based on drilling measurements and/or parameters.” (Jeong, ¶43); “In response to one or more high risk areas being identified, the method 400 may proceed to adjusting one or more of the drilling parameters based on the drilling risk profile, as at 412. As shown in FIG. 8, an automated system may suggest deviations from the initial plan.” (Jeong, ¶51)).
Regarding claim 16, the proposed combination discloses The system as recited in Claim 14, further comprising: as stated previously.
The proposed combination discloses in further view of Jeong a result transceiver, configured to communicate results, interim outputs, the one or more risks, the sub-risk score for each of the one or more borehole design concepts, and the final risk score to a user system, a data store, or a computing system. Examiner interprets this limitation as a computing system component (result transceiver) that can display (communicate) output (results or interim outputs) of an engineering assessment for a well plan (borehole design that considers risks) using specified parameters, drilling parameters (borehole design concepts), drilling risk profile (final risk score which includes the sub-risk scores). Since the display can be interfaced with a user, the display is considered an example of a user system but is part of a greater computing system which also inherently includes storage. (“FIG. 6 illustrates a display of an output of an engineering assessment for a well plan using specified parameters, according to an embodiment.” (Jeong, ¶14); “The drilling risk profile 700 of FIG. 7 may correspond to the drilling parameters of FIG. 6, which were used in the engineering analysis of block 408 (the results of which are shown in FIG. 5).” (Jeong, ¶50)).
Regarding claim 17, the proposed combination discloses The system as recited in Claim 16, as stated previously.
The proposed combination in further view of Jeong discloses wherein the computing system is a borehole operation planning system. Per the specification, a borehole operation planning system is not defined and thus the limitation is read within the scope of its broadest reasonable interpretation to be a system that is used for the planning of borehole operations. The method for planning a well can be implemented on a computing system, as described by Jeong (“FIG. 2 illustrates a flowchart of a method 200 for planning a well, according to an embodiment.” (Jeong, ¶40); “In some embodiments, the methods of the present disclosure may be executed by a computing system.” (Jeong, ¶54)). Therefore, the computing system taught by Jeong is a borehole operation planning system.
Regarding claim 18, the proposed combination discloses The system as recited in Claim 16, as stated previously.
The proposed combination in further view of Jeong disclose wherein an output from the user system is used to update a risk matrix of a risk model. Outputs are given for each task as part of a workflow, wherein outputs from a preceding tasks can be used as inputs to the next task. ((Veeningen, ¶33) " System tasks are arranged in a single workflow in which the output of one task is included as input to the next. The user can modify most outputs, which permits fine-tuning of the input values for the next task."). The risk assessment is output to the user interface prior to (as indicated by above) the risk matrix, as depicted in the results of Figure 2C, thereby indicating that that the output of the risk assessment informs the output of the risk matrix values. The risk matrix is described as being part of a Risk Assessment Catalog ((Veeningen, ¶219-220) " The following paragraphs will set forth the 'Risk Assessment Catalogs'28 which are used by the 'Risk Assessment Logical Expressions'22 and the 'Risk Assessment Algorithms'24. Values of the Catalogs 28 that are used as input data for Risk Assessment Algorithms 24 and the Risk Assessment Logical Expressions 22 include the following: (1) Risk Matrix Catalog")
Regarding claim 19, the proposed combination discloses The system as recited in Claim 14, as stated previously.
The proposed combination discloses in further view of Jeong (except the limitations surrounded by brackets ([[..]])) wherein the borehole risk analyzer is further configured to evaluate [[more than]] one borehole design, The risk prediction module is used to assess risk for a plan for a well ((Jeong, ¶57) " In the example of computing system 1100, computer system 1101A includes the risk prediction module 1108."); ((Jeong, ¶43) " The next stage of the method 200 may be to build a machine learning model that identifies risks in a planned well based on drilling measurements and/or parameters."); ((Jeong, ¶47) " The method 400 may proceed to receiving drilling parameters for a new well, as at 406, and determining that the drilling parameters meet engineering specifications for well equipment, as at 408. A well plan may specify many different drilling parameters.") [[and select for recommendation one recommended borehole design from the more than one borehole designs using ranks, weighting parameters, priority indicators, or statistics-based algorithms applied to the one or more risks, the sub-risk score for each of the one or more borehole design concepts, or the final risk score.]]
Jeong alone does not disclose; however, Jeong in view of Veeningen discloses more than Multiple scenarios characterized by different configurations can be evaluated ((Veeningen, ¶62) "Once the 'Automatic Well Planning Software System' has been localized, the ability to quickly run new scenarios and assess the business impact and associated risks of applying new technologies, procedures or approaches to well designs is readily possible,"). The software enables differentiation amongst technologies ((Veeningen, ¶39) "The software associated with the 'Automatic Well Planning Software System' will enable specialists and vendors to demonstrate differentiation amongst new or competing technologies. It will allow operators to quantify the risk and business impact of the application of these new technologies or procedures.")
and select for recommendation one recommended borehole design from the more than one borehole designs using ranks, weighting parameters, priority indicators, or statistics-based algorithms applied to the one or more risks, the sub-risk score for each of the one or more borehole design concepts, or the final risk score. A rig is proposed by the software after evaluating the risks of the rig properties ((Veeningen, ¶48) "From this input data, the 'Automatic Well Planning Software System' automatically selects the most appropriate rig and associated properties, costs, and mechanical capabilities. The rig properties include parameters like derrick rating to evaluate risks when running heavy casing strings, pump characteristics for the hydraulics, size of the BOP, which influences the sizes of the casings, and very importantly the daily rig rate and spread rate. The user can select a different rig than what the 'Automatic Well Planning Software System' proposed and can modify any of the technical specifications suggested by the software."); ((Veeningen, ¶111) "The Risk Assessment sub-task 16a associated with the 'Automatic Well Planning Software System' of the present invention is a system that will automatically assess risks associated with the technical well design decisions in relation to the earth's geology and geomechanical properties and in relation to the mechanical limitations of the equipment specified or recommended for use."). Total risk can quantify the risk of the design, wherein the total risk is calculated using a summation applied to the risk subcategories, wherein the risk subcategories consider individual risk ((Veeningen, ¶115) "The Total Risk for a scenario is calculated based on the cumulative results of all of the group/category risks along both the risk and depth axes."); ((Veeningen, ¶953-954) "Risk Calculation #5-Total RiskThe total risk calculation is based on the following categories: (a) gains, (b) losses, (c) stuck, and (d) mechanical.
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It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have further modified the proposed combination to incorporate the evaluation of borehole designs against other borehole designs because some teaching, suggestion, or motivation in the prior art would have led one having ordinary skill in the art to do so in order to arrive at the claimed invention. Jeong appears to contemplate analyzing a single design that is modified iteratively using the risk prediction module. Veeningen contemplates the comparison of multiple scenarios to ultimately arrive at a suggested rig that is proposed automatically by the software, wherein the proposed selection is generated responsively to the risk evaluation. Veeningen notes that the approach is beneficial in enabling business impact assessment for applying new technologies ((Veeningen, ¶62) " (5) Once the 'Automatic Well Planning Software System' has been localized, the ability to quickly run new scenarios and assess the business impact and associated risks of applying new technologies, procedures or approaches to well designs is readily possible,[[...]] (8) The 'Automatic Well Planning Software System' provides unique understanding of drilling risk and uncertainty enabling more realistic economic modeling and improved decision making, (9) The risk assessment accurately identifies the type and location of risk in the wellbore enabling drilling engineers to focus their detailed engineering efforts most effectively,"). Accordingly, to realize the benefits of the system of Veeningen, one having skill in the art would be obviously compelled to combine the parallel analysis of alternative designs as disclosed by Veeningen into the iterative refinement approach for design optimization as disclosed by Jeong.
Regarding claim 20, Jeong discloses (except the limitations surrounded by brackets ([[..]])) A computer program product having a series of operating instructions stored on a non-transitory computer-readable medium that directs a data processing apparatus when executed thereby to perform operations ((Jeong, ¶6) "Embodiments of the disclosure may further provide a non-transitory computer-readable medium storing instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations.") to generate one or more risk scores for a borehole design of a borehole, the operations comprising ((Jeong, ¶48) " The machine learning model may thus be configured to associate certain drilling parameters, conditions, etc., with certain risks, and thereby quantify the risks as a value, e.g., a percentage, ranking, etc. This value may be compared with risk tolerance values, which may be predetermined or otherwise devised, in order to establish whether the risk is acceptable, or to qualify the risk as high, medium, low, etc."):
receiving borehole location parameters for the borehole, borehole associated data relating to the borehole, and a geographic location of interest for the borehole; Examiner interprets this limitation as receiving offset well data that includes the wellbore trajectory parameters (location parameters), drilling parameters and drilling equipment selection (borehole associated data), and well location (geographic location of interest) which can be assessed and modified for well plans (“The method 200 may include receiving offset well data, as at 202." (Jeong, ¶40); "Such predictions may inform users of likely drilling risks, and thus allow for changed drilling plans (e.g., well location or trajectory, drilling equipment selection, drilling parameters)." (Jeong, ¶44)).
determining one or more borehole design concepts for the borehole utilizing the borehole location parameters, the borehole associated data, and the geographic location of interest; Examiner interprets this limitation as specifying (determining) drilling parameters (design concepts) that include trajectory parameters, drilling parameters and equipment selection, and well location data. Examiner interprets the specified drilling parameters as the basis of the well plans that define the borehole design ((Jeong, ¶40) "FIG. 2 illustrates a flowchart of a method 200 for planning a well, according to an embodiment.” ); ((Jeong, ¶44) " Such predictions may inform users of likely drilling risks, and thus allow for changed drilling plans (e.g., well location or trajectory, drilling equipment selection, drilling parameters.").
[[assigning one or more risks to each of the one or more borehole design concepts;]]
[[generating a sub-risk score for one or more risk types for each of the one or more borehole design concepts using the one or more risks;]]
[[generating a final risk score for each of the one or more borehole design concepts, using an average algorithm to combine the sub-risk score for each of the one or more risk types; and]]
filtering non-satisfactory borehole design concepts from the one or more borehole design concepts when the [[final risk score]] for the respective borehole design concept fails to satisfy a first risk tolerance parameter or one or more [[sub-risk scores of the final risk score]] for the respective borehole design concept fails to satisfy a respective [[sub-risk]] risk tolerance parameter; Risk values are described as being compared with risk tolerance values ((Jeong, ¶48) "The machine learning model may thus be configured to associate certain drilling parameters, conditions, etc., with certain risks, and thereby quantify the risks as a value, e.g., a percentage, ranking, etc. This value may be compared with risk tolerance values, which may be predetermined or otherwise devised, in order to establish whether the risk is acceptable, or to qualify the risk as high, medium, low, etc."). Risk profiles are likewise evaluated with respect to tolerance values ((Jeong, ¶49) "The drilling risk profile may visually describe levels of drilling risk in the well with respect to time. FIG. 7 illustrates an example of a risk profile 700. In this example the risk profile 700 includes three levels of risk, high, medium, and low, which may be identified by the machine learning model based on the offset data. Further, the x-axis represents time ( or more specifically, in this case, sample number), and the y-axis represents is the risk indicator of the stuck-pipe events. If the computed risk (likelihood of the drilling events) is higher than a certain threshold value, the risk profile area may change color (represented in gray in FIG. 7)."). Drilling parameters are adjusted when a risk profile has been identified as high risk, thereby eliminating the particular drilling parameter from the design plan and effectively filtering the design plan from that parameter ((Jeong, ¶50-51) "The drilling risk profile 700 of FIG. 7 may correspond to the drilling parameters of FIG. 6, which were used in the engineering analysis of block 408 (the results of which are shown in FIG. 5). Thus, even though the drilling parameters passed the engineering analysis in block 408, the machine learning model's drilling risk profile indicates an area 702 of high risk. In response to one or more high risk areas being identified, the method 400 may proceed to adjusting one or more of the drilling parameters based on the drilling risk profile, as at 412. As shown in FIG. 8, an automated system may suggest deviations from the initial plan. For example, in over balance pressure, the initial plan 800 may be deviated from, as indicated at 802 in the later samples ( corresponding to the area of high risk 702), toward the right of the plot. For correcting the expected risks raised by Pre-Drill advisor, the controllable drilling parameters (mud weight, flowrate, drillstring velocity, speed (revolutions-per-minute or RPM), and rate of penetration (ROP)) may be adjusted."); ((Jeong, ¶52) " FIG. 10 illustrates a new risk profile, generated after deviating from the initial plan 800 according to the adjustments to the parameters illustrated in FIG. 9. As can be seen, the area of high risk is no longer present.")
determining the borehole design in [[a feasibility study phase of]] a borehole operation plan, from the one or more borehole design concepts [[using the final risk score and the sub-risk scores for each respective borehole design concept; and]] A well plan is described as being characterized by drilling parameters ((Jeong, ¶47) "A well plan may specify many different drilling parameters."). The drilling plan may be adjusted based on identified risk ((Jeong, ¶44) "Such predictions may inform users of likely drilling risks, and thus allow for changed drilling plans ( e.g., well location or trajectory, drilling equipment selection, drilling parameters)."); ((Jeong, ¶51) " In response to one or more high risk areas being identified, the method 400 may proceed to adjusting one or more of the drilling parameters based on the drilling risk profile, as at 412. As shown in FIG. 8, an automated system may suggest deviations from the initial plan."). The well plan corresponds to the operations ((Jeong, ¶38) " As an example, a well may be drilled for a reservoir that is laterally extensive. In such an example, lateral variations in properties, stresses, etc. may exist where an assessment of such variations may assist with planning, operations, etc. to develop a laterally extensive reservoir ( e.g., via fracturing, injecting, extracting, etc.) ")
placing, during a detailed planning phase of the borehole operation plan, a drilling equipment for a wellhead at a location determined by the borehole design The drilling plan is used as a guide for drilling, indicating that drilling equipment in the drill plan will be used in the borehole where drilling is executed ((Jeong, ¶47) "A well plan may specify many different drilling parameters."); ((Jeong, ¶53) "The remaining risks may be deemed acceptable, and thus the method 400 may proceed to drilling a well using the drilling parameters, as at 414."). The drill bit depth is determined as part of the method and used in a model with the well equipment (placing the drilling equipment for a wellhead) to evaluate if the plan meets engineering specifications ((Jeong, ¶47) "The method 400 may proceed to receiving drilling parameters for a new well, as at 406, and determining that the drilling parameters meet engineering specifications for well equipment, as at 408. A well plan may specify many different drilling parameters. FIG. 5 illustrates plots of four examples of such drilling parameters, namely, over balance pressure, flow rate, rotation rate, and bit depth. These drilling parameters may be fed to a model of the subterranean domain, using the drilling system components selected, in order to determine that the drilling parameters meet engineering specifications for the well equipment, as at 408. An example of the output of such a model is shown in FIG. 6. This may be a threshold determination; if the drilling parameters result in loads on the equipment that exceed the equipment's capabilities, the drilling parameters may be rejected, or the well equipment may be changed. "). A well plan is created as part of the method, and the methodology is used to guide the planning, development, and operation phases of a well ((Jeong, ¶2) " The drilling parameters, well location and/or trajectory, etc. can then be modified to minimize such risk. Thus, this analysis provides a rough guidance for drill planning or operation.")
Jeong alone does not disclose; however, Jeong in view of Veeningen discloses assigning one or more risks to each of the one or more borehole design concepts; Individual risks are specified and correlated to a high risk, medium risk or low risk ((Veeningen, ¶920-924) "Risk Calculation #1-Individual Risk Calculation: Referring to the 'Risk Assessment Output Data' 18b1 set forth above, there are fifty-four (54) 'Individual Risks' currently specified. For an 'Individual Risk': a High risk=90, a Medium risk=70, and a Low risk=10"). Individual risks represent different borehole design concepts ((Veeningen, ¶248-302) "[0248] The following 'Individual Risks' are calculated (1) H2S and CO2 [[…]] (54) Cuttings.").
generating a sub-risk score for one or more risk types for each of the one or more borehole design concepts using the one or more risks; When read in light of the specification, a risk type may include a concept, category or group for a borehole design ((Instant specification, ¶17) "A final risk score can also specify a sub risk score per risk type, e.g., a concept, category, or group, for a borehole design."). A risk subcategory score is calculated for each risk value corresponding to an individual risk, wherein there are four subcategories by which risk scores can be computed ((Veeningen, ¶939-940) "Referring to the 'Risk Assessment Output Data'l8bl set forth above, the following 'Subcategory Risks' are defined: (a) gains, (b) losses, (c) stuck and (d) mechanical, where a 'Subcategory Risk' (or 'Risk Subcategory') is defined as follows:
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j=number of individual risks, "). The individual risk represents a different borehole design concept, as stated previously.
generating a final risk score for each of the one or more borehole design concepts, using an average algorithm to combine the sub-risk score for each of the one or more risk types; and A total risk is calculated by averaging the risk subcategory scores for each of the four subcategories ((Veeningen, ¶953-654) "Risk Calculation #5-Total Risk. The total risk calculation is based on the following categories: (a) gains, (b) losses, (c) stuck, and (d) mechanical.
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"). The risk subcategory scores consider each of the individual risks corresponding to a different borehole design concept, thereby indicating that the final risk score is generated with regard to each of the concepts.
…final risk score… sub-risk scores of the final score … sub-risk Sub-risk and final risk scores are generated using the methodologies, as stated above, to generate average individual risk values and actual risk values comprised of the average individual risk values. (Veeningen, ¶939-940); (Veeningen, ¶953-654). Subcategory risk tolerance parameters are characterized ((Veeningen, ¶944-946) "Red risk display for Risk Subcategory)=40. Yellow risk display for 20<=Risk Subcategory <40.Green risk display for Risk Subcategory<20")
a feasibility study phase of The software and methodology is described as being utilized as a scoping tool for assessing mechanical feasibility by asset teams ((Veeningen, ¶37) " Asset Teams will use the software associated with the 'Automatic Well Planning Software System' as a scoping tool for cost estimates, and assessing mechanical feasibility, so that target selection and well placement decisions can be made more knowledgeably, and more efficiently ")
using the final risk score and the sub-risk scores for each respective borehole design concept; and The methodology is described as leveraging a risk assessment to inform design decisions ((Veeningen, ¶33) " An 'Automatic Well Planning Software System' is disclosed in this specification. The 'Automatic Well Planning Software System' of the present invention is a "smart" tool for rapid creation of a detailed drilling operational plan that provides economics and risk analysis. The user inputs trajectory and earth properties parameters; the system uses this data and various catalogs to calculate and deliver an optimum well design thereby generating a plurality of outputs, such as drill string design, casing seats, mud weights, bit selection and use, hydraulics, and the other essential factors for the drilling task."); ((Veeningen, ¶117) " The 'Automatic Well Planning Software System' of the present invention is capable of delivering a comprehensive technical risk assessment, and it can do this automatically. Lacking an integrated model of the technical well design to relate design decisions to associated risks, the 'Automatic Well Planning Software System' can attribute the risks to specific design decisions and it can direct users to the appropriate place to modify a design choice in efforts to modify the risk profile of the well."). The risk analysis process includes generating the final risk score and the sub-risk score, as stated previously, thereby indicating that the design uses the final risk score and the sub risk scores ((Veeningen, ¶111-112) "The Risk Assessment sub-task 16a associated with the 'Automatic Well Planning Software System' of the present invention is a system that will automatically assess risks associated with the technical well design decisions in relation to the earth's geology and geomechanical properties and in relation to the mechanical limitations of the equipment specified or recommended for use. Risks are calculated in four ways: (1) by 'Individual Risk Parameters', (2) by 'Risk Categories', (3) by 'Total Risk', and ( 4) the calculation of 'Qualitative Risk Indices' for each.")
Veeningen and Jeong are analogous arts to the claimed invention in that they are both related to the same field of endeavor of risk management and design optimization for wellbores. Jeong discloses a method that quantifies the value of risk for each individual drilling parameter and uses the quantified values to create an overall risk profile which contains the individual drilling parameters but does not provide detail as to how the individual risks are considered within the drilling profile ("The machine learning model may thus be configured to associate certain drilling parameters, conditions, etc., with certain risks, and thereby quantify the risks as a value, e.g., a percentage, ranking, etc." (Jeong, ¶48)); ("The method 400 may proceed to generating a drilling risk profile for the new well, using the machine learning model, based on the drilling parameters, as at 410. As mentioned above, the machine learning model may be trained using previously identified risk inferences in the offset well data, or through observations of drilling risk in association with drilling parameters in the offset well data." (Jeong, ¶48)); ("The drilling risk profile 700 of FIG. 7 may correspond to the drilling parameters of FIG. 6, which were used in the engineering analysis of block 408 (the results of which are shown in FIG. 5)." (Jeong, ¶50)). Veeningen discloses a method where subcategory risks are generated per category type and subsequently utilized to create a total risk score. By applying the risk assessment scoring methodology (that includes the explicit quantification of final risk and sub-risk scores) disclosed by Veeningen into the design methodology that considers risk in a less granular way, as disclosed by Jeong, one having skill in the art would arrive at the claimed invention. It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have incorporated the separate individual risk scores of each design parameter used to generate the total risk score, as taught by Veeningen, because considering the impact of each risk parameter with regard for the design task enables users to more accurately identify the type and location of risk such that drilling engineers can more effectively focus their efforts ((Veeningen, ¶62) "The risk assessment accurately identifies the type and location of risk in the wellbore enabling drilling engineers to focus their detailed engineering efforts most effectively"); ((Veeningen, ¶113) "Individual Risk Parameters are calculated along the measured depth of the well and color coded into high, medium, or low risk for display to the user. Each risk will identify to the user: an explanation of exactly what is the risk violation, and the value and the task in the workflow controlling the risk. These risks are calculated consistently and transparently allowing users to see and understand all of the known risks and how they are identified. These risks also tell the users which aspects of the well justify further engineering effort to investigate in more detail.); ((Veeningen, ¶117) "Lacking an integrated model of the technical well design to relate design decisions to associated risks, the 'Automatic Well Planning Software System' can attribute the risks to specific design decisions and it can direct users to the appropriate place to modify a design choice in efforts to modify the risk profile of the well.") Leveraging individual risk scores within the total risk score further enables detailed analysis to determine relative risk for different design tasks ((Veeningen, ¶116) "Risk indexing-Each individual risk parameter is used to produce an individual risk index which is a relative indicator of the likelihood that a particular risk will occur."). Accordingly, the combination of prior art references would have been obvious.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
WO 2016134018 A2 discloses a multi-phase plan for designing a wellbore including a drilling plan and a cementing plan. See at least ¶37-39 and ¶45-46.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EMILY GORMAN LEATHERS whose telephone number is (571)272-1880. The examiner can normally be reached Monday-Friday, 9:00 am-5:00 pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, EMERSON PUENTE can be reached at (571) 272-3652. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/E.G.L./Examiner, Art Unit 2187
/JOHN E JOHANSEN/Examiner, Art Unit 2187
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