DETAILED ACTION
This action is in response to communications filed on 01/13/2026. Claims 1, 8 and 15 have been amended, claims 5-7, 12-14, and 19-20 have been cancelled. No new claims have been added. Claims 1, 8, and 15 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, and 15. Applicant references originally filed-claims 7, 14, and 20 as well as Figs 1A, 1B, and 4A-4C, in addition to paragraphs [0024]-[0034] and paragraphs [0068]-[0122] of the published application.
The referenced sections of the disclosure have been evaluated by the examiner to determine if adequate support is present such that it is apparent that the applicant possessed the claimed invention at the time of filing.
The limitation wherein the black box function uses the objective function to determine the predicted ROP based on satisfying a value of one or more of top drive torque, weight-on-bit, fluid flow rate, stick-slip index, bit wear rate, stuck pipe probability index, and cuttings concentration in annulus; and is sufficiently supported by at least originally filed claims 7, 14, and 20, ¶46, ¶47, and ¶62 of the specification.
The limitation wherein the plurality of drilling parameters comprises a weight-on-bit of the drill bit, a rotary speed of the drill bit, and a flow rate of the mud pump. is sufficiently supported by at least originally filed claims 5, 12, and 19.
The newly-added limitations in a first hole section of a wellbore; for the first hole section; determining, by the drilling system, a second hole section in the wellbore for the drilling operation, wherein the second hole section corresponds to entering a new subsurface formation in the drilling operation, and wherein the second hole section is different from the first hole section; the second hole section appears to be sufficiently supported by at least the following paragraphs of the specification:[0018], [0021], [0040], [0051], [0060], [0061] and [0062].
Based on this evaluation, it appears as though no new matter has been introduced by way of the provided amendments and it is apparent to the examiner that the applicant had possession of the claimed invention at the time of filing.
Response to Arguments
Rejection under 35 U.S.C. § 101
Applicant's arguments regarding the rejection under 35 U.S.C. § 101 have been fully considered but they are not persuasive.
Applicant has amended the claims and argues that any recited judicial exception is integrated into a practical application and the limitations of the claims provide significantly more than the recited judicial exception. Particularly, applicant argues that the “Apply It” consideration does not pertain to the amended claims, pointing to the 3 factors of the August 101 memo.
Applicant argues, with reference to factor 1 of the memo, the amended claims indicate a particular solution that requires a black box function including a particular physics-based model and a deep learning model, as well as a drilling system that includes sensors, a drill string, a drill bit, and a mud pump with their corresponding parameters. Applicant argues the amended claim does not broadly cover only the idea of a solution or outcome.
Examiner disagrees. Even when splitting claim limitations more granularly such that the recited judicial exceptions are separated from the computing components that are used to perform them, as refined in this action, the drilling system, the black box function, the physics based model, and the deep learning model are merely just tools in a given technological environment that are used to perform the recited judicial exceptions and stating that a determination is made using the components but not articulating how the components operate in any inventive capacity is merely the recitation of the idea of a solution or outcome. Additionally, the particularity of the elements is not claimed such that it would be apparent that they operate as particular and uniquely identifiable mechanisms. The judicial exception of determining an optimal ROP is limited by the recitation of such components to a technological environment comprised of such components. The specificity by which these components are recited does not provide the integral details as to demonstrate how the solution to a problem is accomplished (as an example, using a physics based model to determine drag, a deep learning model to determine a stuck pipe index, etc. only state that the models are used to obtain the values while failing to describe how). The components are recited merely as tools by which the judicial exception is executed (Mere Instructions to Apply an Exception (MPEP 2106.05(f)) and Field of Use and Technological Environment (MPEP 2106.05(h))). The inclusion of changing, automatically by the drilling system and during the drilling operation, a plurality of drilling parameters to adjust the first ROP to the predicted ROP in response to determining the predicted ROP likewise does not provide a particular solution to a problem- it is merely the recitation of taking the optimized value obtained as part of the mental process and applying it to the system in a non-inventive way- the changing of drilling parameters is known as the solution by which to modify an ROP in a system and doing so “automatically by the drilling system” is just automating an existing process using a generic computer and the tools in the technological environment.
Applicant further argues, with reference to factor 2 of the memo, that changing a weight-on-bit of a drill bit, a rotary speed of the drill bit, and a flow rate of a mud pump in a drilling system does not invoke the drilling system components merely as tools to invoke an existing process but instead improve an existing technology- particularly optimizing drilling at different hole sections through a subsurface.
Changing the weight on bit, the rotary speed, and the flow rate in a drilling system is an existing process by which to change the ROP in a drilling system already known in the art. Doing so automatically, as recited in the claim is just the recitation of using a computer as a tool to perform the existing process. As a note, applicant’s argument that the improvement provided by the claimed invention is an optimization is an admission that the inventive concept of the claimed invention is an optimization. An optimization is a process which can be performed in the human mind or using assistive aids- particularly by evaluating current relevant data to make a judgment on any changes necessary to achieve an optimally desired outcome in a system. Any purported improvement to technology would flow as a direct consequence of the improvement of the judicial exception (namely the determination of the predicted ROP). The judicial exception cannot provide the inventive concept but rather it must be provided by the additional elements of the claim.
Applicant further argues, with reference to factor 3 of the memo, the elements of the claim are very particular and specific and demonstrate an improvement to drilling technology.
Examiner disagrees. The claim does not reflect an improvement to drilling technology. The claim reflects an improvement to a method of determining an optimized value- a mental process. Per MPEP 2106.05(a), II.: "it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology."
Applicant further argues that, with respect to the August 101 memo, Examiner has incorrectly ignored the machine-learning limitations required by limitation (e) of amended independent claim 1 and therefore the Examiner is allegedly improperly evaluating the changing of drilling parameters to adjust the ROP in a vacuum and completely separate from limitations (d) and (e) of the amended claim.
Examiner disagrees. The claim recites a series of determination steps to identify a first rate of penetration, a second hole, and a predicted ROP, wherein the predicted ROP is determined using a black box function comprising a physics based model and a deep learning model as generic tools to perform such determination. The changing of drilling parameters is the recitation of applying the determined value to the system. The inventive concept is rooted in the determination of the predicted ROP, which is a mental process as a judicial exception, and the claim recites the black box function as a tool to enable such prediction in a computing environment, wherein the specificity of the black box function is not effectively demonstrated by the claim such that it would be apparent to one of skill in the art that the black box function contains any unique or inventive elements. Per MPEP 2106.05, I.: An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself.". In the amended claims, the additional elements do not provide the inventive concept, they instead support the inventive concept that is defined in the steps reciting judicial exceptions including mental process and mathematical calculation.
Under step 2B, applicant argues that the amended claim 1 provides an unconventional technique for changing drilling parameters by adjusting automatically a rate of penetration of a drilling operation based on a predicted rate of penetration. Applicant references the specification and argues that the independent claim 1 requires an unconventional technique that is a technology-based solution and software-based invention that improves the performance of the technology of optimizing a rate of penetration of a drilling operation.
This assertion is again an admission by the applicant that the claimed invention is an improvement to an optimization. An optimization is a process which can be practically be performed in the human mind using pen and paper as assistive physical aids, for example, by evaluating the current pertinent information of the drilling operation and making a judgement as to the best ROP to employ in the drilling operation. As stated above, per MPEP 2106.05(a)(II), “It is important to keep in mind that an improvement in the abstract idea itself is not an improvement in technology”. Rather, the inventive concept must be furnished by the additional elements.
Applicant further argues that the Examiner has failed to provide an evidentiary record indicating that the claimed steps merely recite well-understood, routine, and conventional activity under MPEP 2106.05(d)(I) and 2016.07(a) and Step 2B of the subject matter eligibility test. As such, applicant has requested that the Examiner provide evidence that the claimed “black box function” for determining predicted ROP is “well-understood, routine, and conventional” as arranged in the amended claim.
As stated in this office action, the black box function has been identified as an additional element categorized as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) because the element is the recitation of a generic computing component used as tool to implement the judicial exception. Determining the predicted ROP is the recitation of a judicial exception- namely mental process. Here, the claim invokes machinery as a tool to perform the existing process of determining optimal ROP value- which could be performed practically in the human mind, albeit understandably slower than that implemented on a computer. As described in this action, using computing components/machinery as a tool has been found by the courts to not amount to significantly more than the judicial exception (See MPEP 2106.05(A) which explicitly notes that including mere instructions to implement the abstract idea on a computer has been found by the courts to not qualify as significantly more when recited in a claim with a judicial exception). Thus, evidentiary support has been provided that such argued element is well-understood, routine, and conventional. The way that the individual elements interact with each other (per the ordered combination) does not provide an inventive concept that would transform the claim to amount to significantly more than the recited judicial exception particularly because those features that may be considered inventive are part of the mental process of optimizing ROP values.
Per applicant’s request for the 5 limitations of the claim to be evaluated as the ordered combination (See pages 19-20 of remarks), Examiner has made the following evaluation:
(1) "wherein the black box function comprises a physics-based model and a
deep learning model," This is an additional element. The additional element has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) for generally linking the judicial exception to the particular technological environment of making a determination using these elements. The elements are recited at such a high level of generality that they are understood to be generic machinery that is used to perform the judicial exception. The means by which the execute the functionality to make determinations or the arrangement by which they interact with one another is not claimed in a particular way such that an inventive concept would be apparently in the additional element. The courts have found that generally linking the use of the judicial exception to a particular technological environment or field of use as well as using computing components as a tool to perform the judicial exception are not enough to qualify the claim as significantly more than the judicial exception.
(2) "wherein the physics-based model determines a predicted drag value of
drilling equipment for the predicted ROP based on a drag formula, " This is a judicial exception as a mental process that uses generic computing components (physics-based model) as a tool to perform the mental process. The judicial exception is not considered when searching for the inventive concept because the judicial exception cannot provide the inventive concept and can only be provided by additional elements. The courts have found that using generic computing components as a tool to perform the judicial exception are not enough to qualify the claim as significantly more than the judicial exception.
(3) "wherein the deep learning model determines a predicted stuck pipe index for the drilling operation for the predicted ROP," This is a judicial exception as a mental process that uses generic computing components (deep learning model) as a tool to perform the mental process. The judicial exception is not considered when searching for the inventive concept because the judicial exception cannot provide the inventive concept and can only be provided by additional elements. The courts have found that using generic computing components as a tool to perform the judicial exception are not enough to qualify the claim as significantly more than the judicial exception.
( 4) "wherein the black box function uses an objective function to determine the predicted ROP based on satisfying a first user specified constraint of the predicted drag value and a second user specified constraint of the predicted stuck pipe index," This is a judicial exception as a mental process and mathematical calculation that uses generic computing components (black box function) as a tool to perform the mental process and execute the mathematical calculations with the objective function. The judicial exception is not considered when searching for the inventive concept because the judicial exception cannot provide the inventive concept and can only be provided by additional elements. The courts have found that using generic computing components as a tool to perform the judicial exception are not enough to qualify the claim as significantly more than the judicial exception.
(5) "wherein the black box function uses the objective function to determine the predicted ROP based on satisfying a value of one or more of top-drive torque, weight-on-bit, fluid-flow rate, stick-slip index, bit wear rate, stuck pipe probability index, and cuttings concentration in annulus." This is a judicial exception as a mental process and mathematical calculation that uses generic computing components (black box function) as a tool to perform the mental process and execute the mathematical calculations with the objective function. The judicial exception is not considered when searching for the inventive concept because the judicial exception cannot provide the inventive concept and can only be provided by additional elements. The courts have found that using generic computing components as a tool to perform the judicial exception are not enough to qualify the claim as significantly more than the judicial exception.
This evaluation has demonstrated that the additional elements include those identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) and Field of Use and Technological Environment (MPEP 2106.05(h)), which the courts have found do not amount to significantly more than the judicial exception. The arrangement of the additional elements (the order and the way in which the additional elements interact with one another) further does not provide an inventive concept and just limits how the judicial exception is performed and applied, wherein the judicial exception(s) are actually what contain the inventive concepts. Note that the arrangement of the steps in the judicial exception is irrelevant in this evaluation because only the additional elements should be evaluated to determine an inventive concept, See MPEP 2106.05 (II) “Evaluate whether any additional element or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP § 2106.05(d).”.
For the reasons stated above, in conjunction with the rejection of this office action, the rejections under 35 U.S.C. § 101 are maintained.
Rejection under 35 U.S.C. § 103
Applicant’s arguments with respect to claim(s) 1, 8, and 15 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Particularly, the challenged limitation by the applicant includes determining an ROP for a first hole section of a drilling operation and then determine a predicted ROP for a different hole section of the same drilling operation.
Examiner agrees that the previously-relied upon references fail to disclose the consideration of at least two sections in a drilling operation for predicting an ROP and subsequently adjusting the measurable ROP to the predicted value. However these newly-added features with consideration to a first section in a formation and a second section in a formation required further search and consideration and have been found to be disclosed by Alali as incorporated in the new grounds of rejection as stated herein. Jain discloses updating the prediction model used to predict ROP continually through the drilling process according to acquired real-time data, wherein the real time data includes formation lithology properties ((Jain, ¶89) "Furthermore, the hybrid model 201 may retrain ( e.g., validate) the hybrid model 201 via any of the training methods described above in regard to FIG. 4B in order to generate a real-time hybrid model 201 ( e.g., retrained hybrid model 201, updated hybrid model 201) based on the pre-well hybrid model 201 and the acquired real-time data. In other words, utilizing the online well data, the hybrid model 201 may enhance the pre-well hybrid model 201."); ((Jain, ¶31) "The sensors 140 may include one or more of sensors 140 that determine acceleration, weight on bit, torque, pressure, cutting element positions, rate of penetration, inclination, azimuth, formation lithology, etc."). But Jain does not contemplate necessarily adjusting the drilling parameters to achieve an ROP value according to identified changes in sections of the formation. However, Alali does suggest that this is in fact standard practice in ROP optimization- that is to adjust the ROP per each section. ((Alali, Page 4, Col 1, ¶4) " Current ROP optimization practice is to select one value for each controllable dynamic drilling parameter and adjust it per section"). Accordingly, by imposing the sequential considerations to sections of a formation into the workflow of applying the model disclosed by Jain which can identify different properties amongst differing formations, the claimed invention would have been achieved.
For the reasons stated above, in addition to the provided details in the updated rejection, the claims remain rejected under 35 U.S.C. § 103.
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, 8, and 15 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 is 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, … while advancing the drill bit during the drilling operation based on a plurality of drilling parameters specified by a user, a first rate of penetration (ROP) for the first hole section; . The claim limitation can be reasonably read to entail making an observation or a judgement as to a rate of penetration. The limitation recites the drilling system as performing the determination step. Under broadest reasonable interpretation and when read in light of the specification, the drilling system contains a generic computer to perform this function; however, the courts do not distinguish between a mental process performed in the human mind or on a generic computer. This task can be practically performed in the human mind or using an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process
determining, … a second hole section in the wellbore for the drilling operation, wherein the second hole section corresponds to entering a new subsurface formation in the drilling operation, and wherein the second hole section is different from the first hole section; This claim limitation can be reasonably read to entail making an observation of the formation by which the drilling is being performed and making a judgement as to the location of a second hole section that is different from the first section. Under broadest reasonable interpretation and when read in light of the specification, the drilling system contains a generic computer to perform this function; however, the courts do not distinguish between a mental process performed in the human mind or on a generic computer. 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.
determining, …, a predicted ROP for the second hole section of drilling operation using the rotary speed data, the weight-on-bit data, the pump flow rate data, and a black box function, The claim limitation can be reasonably read to entail evaluating rotary speed data, weight on bit data, and pump flow rate data with regard for a black box function in order to estimate an ROP with consideration for a predetermined condition. The limitation recites the drilling system as performing the determination step. Under broadest reasonable interpretation and when read in light of the specification, the drilling system contains a generic computer to perform this function; however, the courts do not distinguish between a mental process performed in the human mind or on a generic computer. This task can be practically performed in the human mind or using an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. When read in light of the specification, a blackbox function is an empirical function. Therefore, this claim limitation also includes the recitation of the judicial exception of abstract ideas as a mathematical concept.
… determines a predicted drag value of drilling equipment for the predicted ROP based on a drag formula, The claim limitation can be reasonably read to entail evaluating a drag formula to predict a drag value. The claim recites the physics based model as being used to perform the determination, wherein the physics-based model is understood to be a generic computing component recited at a high level of generality. This task can be performed within the human mind or using a pen and paper as an assistive physical aid and the courts do not distinguish between a mental process performed entirely in the human mind or those which are performed by a computing component. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. A drag formula is a mathematical equation which is the explicit recitation of a mathematical concept and therefore, this claim limitation also includes the recitation of the judicial exception of abstract ideas as a mathematical concept.
… determines a predicted stuck pipe index for the drilling operation for the predicted ROP, and The claim limitation can be reasonably read to entail calculating an index value for the stuck pipe phenomenon. The claim recites the deep learning as being used to perform the determination, wherein the deep learning model is understood to be a generic computing component recited at a high level of generality. This task can be performed within the human mind or using a pen and paper as an assistive physical aid and the courts do not distinguish between a mental process performed entirely in the human mind or those which are performed by a computing component. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Because an index value is understood as the result of a mathematical calculation, this claim limitation further includes the recitation of the judicial exception of abstract ideas of a mathematical concept.
… uses an objective function to determine the predicted ROP based on satisfying a first user specified constraint of the predicted drag value and a second user specified constraint of the predicted stuck pipe index; and The claim limitation can be reasonably read to entail evaluating user specified constraints in order to make a prediction of a rate of penetration using an objective function. The claim recites the black box function as being used to perform the determination, wherein the blackbox function is understood to be a generic computing component recited at a high level of generality. This task can be performed within the human mind or using a pen and paper as an assistive physical aid and the courts do not distinguish between a mental process performed entirely in the human mind or those which are performed by a computing component. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. An objective function is a mathematical equation and therefore the claim explicitly recites a mathematical concept. Consequently, this claim limitation includes the recitation of the judicial exception of abstract ideas as a mathematical concept.
… uses the objective function to determine the predicted ROP based on satisfying a value of one or more of top drive torque, weight on-bit, fluid flow rate, stick-slip index, bit wear rate, stuck pipe probability index, and cuttings concentration in annulus; and The claim limitation can be reasonably read to entail evaluating values in order to make a prediction of a rate of penetration using an objective function. The claim recites the black box function as being used to perform the determination, wherein the blackbox function is understood to be a generic computing component recited at a high level of generality. This task can be performed within the human mind or using a pen and paper as an assistive physical aid and the courts do not distinguish between a mental process performed entirely in the human mind or those which are performed by a computing component. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. An objective function is a mathematical equation and therefore the claim explicitly recites a mathematical concept. Consequently, this claim limitation includes the recitation of the judicial exception of abstract ideas as a mathematical concept.
Therefore, the claim recites a judicial exception.
Step 2A Prong 2: Additional elements were identified and are noted in italics.
performing a drilling operation in a first hole section of a wellbore using a drilling system, the drilling system comprising a plurality of sensors, a drill string, a drill bit, and a mud pump, - This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) because the limitations amounts to limiting the use of the judicial exception to the particular technological environment and field of use of drilling operations with a drilling system composed of the recited particular components.
wherein the plurality of sensors determine rotary speed data for the drill bit, weight-on-bit data for the drill bit, and pump flow rate data for the mud pump; - This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) because the limitation amounts to limiting the use of the judicial exceptions to the particular technological environment of using particular sensors.
by the drilling system - This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of generic computing components as a tool or machinery to perform the judicial exception
by the drilling system - This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of generic computing components as a tool or machinery to perform the judicial exception
by the drilling system - This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of generic computing components as a tool or machinery to perform the judicial exception
wherein the black box function comprises a physics-based model and a deep learning model, - This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) because the limitation further describes the technological environment in which the abstract ideas are executed.
wherein the physics-based model - This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of generic computing components as a tool or machinery to perform the judicial exception
wherein the deep learning model - This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of generic computing components as a tool or machinery to perform the judicial exception
wherein the black box function - This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of generic computing components as a tool or machinery to perform the judicial exception
wherein the black box function - This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of generic computing components as a tool or machinery to perform the judicial exception
changing, automatically by the drilling system and during the drilling operation, a plurality of drilling parameters to adjust the first ROP to the predicted ROP in response to determining the predicted ROP - This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) because the limitation amounts to the words “apply it” with regard to the predicted value obtained from the mental process/mathematical concepts. The limitation does not specify in detail how the predicted ROP is used to change the drilling parameters to adjust the ROP. Rather, the claim merely recites the idea of an outcome or a solution. The claim further recites that the changing is performed automatically by the drilling system, wherein the drilling system is understood to contain a generic computer such that the limitation is invoking the use of a computer to perform an existing process.
wherein the plurality of drilling parameters comprise a weight-on-bit value, a rotary speed value, and a pump flow rate value.- This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) because the limitation generally links the use of the judicial exception to the particular field of use and technological environment of using the recited drilling parameters
The courts have found that merely including instructions to implement an abstract idea on a computer or merely using a computer or other generic machinery as a tool to perform an abstract idea and reciting the words “apply it” or a generic equivalent with regard to the judicial exception (Mere Instructions to Apply an Exception (MPEP 2106.05(f))); and generally linking the use of a judicial exception to a particular technological environment or field of use (Field of Use and Technological Environment (MPEP 2106.05(h))) 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, no additional elements were identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) and therefore no further evaluation is required to determine if elements 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:
The additional elements were identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) and Field of Use and Technological Environment (MPEP 2106.05(h)), as stated previously. The courts have found that merely using a computer as a tool to perform a mental process, reciting the words “apply it” or equivalent with regard for the judicial exception and generally linking the use of a judicial exception to a particular technological environment or field of use 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 components recited at a high level of generality and functioning in their normal capacity in a particular technological environment of a drilling operation 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. 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: The inventive concept is rooted in the steps construed as the mental process and mathematical concepts. The arrangement by which the additional elements are supplied does not meaningfully limit the claim or transform the claim to have additional elements that provide any sort of inventive concept. The claimed components used to execute the judicial exceptions are not claimed in such a way that they could be construed as a particular machine having distinctly identifiable compositions. Any purported improvement to technology is a direct consequence of the improvement to the judicial exception which is primarily rooted in the optimization of an ROP for a drilling operation and the inventive concept is not furnished by any identified additional elements.
Based on this rationale, the claim has been deemed to be ineligible subject matter under 35 U.S.C. 101.
Claim 8:
Step 1: Claim 8 is directed to a system which falls within one of the four statutory categories of a machine.
The limitations of claim 8 are substantially similar to that recited in claim 1 and for brevity are not being restated. The distinguishing elements that are not recited in claim 1 are evaluated as follows:
Step 2A Prong 1: Claim 8 recites judicial exceptions as provided in claim 1, see above.
Step 2A Prong 2: Additional elements were identified other than that already discussed in claim 1 and are noted in italics.
a processor; and- This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) because the limitation invokes the use of computers to execute the judicial exceptions.
a memory coupled to the processor and storing instruction§., wherein the instructions, when executed by the processor, are configured to perform a method comprising - This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) because the limitation invokes the use of computers to execute the judicial exceptions.
The courts have found that merely including instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea and reciting the words “apply it” or a generic equivalent with regard to the judicial exception (Mere Instructions to Apply an Exception (MPEP 2106.05(f))) does not integrate the judicial exception into a practical application.
When viewed independently and within the claim as a whole, even with the additional elements stated beyond what has already been analyzed in claim 1, the additional elements do not appear to integrate the judicial exception into a practical application.
Step 2B: For the same rationale as provided in claim 1, the additional distinguishing elements do not provide significantly more than the recited judicial exception because they are merely invoking the use of generic computing components as tools to execute the judicial exception.
Conclusion: Based on this rationale, the claim has been deemed to be ineligible subject matter under 35 U.S.C. 101.
Claim 15:
Step 1: Claim 15 is directed to a wellsite which falls within one of the four statutory categories of a machine.
The limitations of claim 15 are substantially similar to that recited in claim 1 and for brevity are not being restated. The distinguishing elements that are not recited in claim 1 are evaluated as follows:
Step 2A Prong 1: Claim 15 recites judicial exceptions as provided in claim 1, see above
Step 2A Prong 2: Additional elements were identified other than that already discussed in claim 1 and are noted in italics.
a drilling system comprising a plurality of sensors, a drill string, a drill bit, and a mud pump, wherein the plurality of sensors determine rotary speed data for the drill bit, weight-on-bit data for the drill bit, and pump flow rate data for the mud pump;- This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) because it generally links the use of the judicial exception to the particular field of use and technological environment.
a control system connected to the drilling system, wherein the control system comprises a processor and a memory, and wherein the memory comprises a program configured to perform a method comprising - This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) because the limitation invokes the use of a computer to execute the judicial exception.
The courts have found that merely including instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea and reciting the words “apply it” or a generic equivalent with regard to the judicial exception (Mere Instructions to Apply an Exception (MPEP 2106.05(f))); and generally linking the use of a judicial exception to a particular technological environment or field of use (Field of Use and Technological Environment (MPEP 2106.05(h))) does not integrate the judicial exception into a practical application.
When viewed independently and within the claim as a whole, even with the additional elements stated beyond what has already been analyzed in claim 1, the additional elements do not appear to integrate the judicial exception into a practical application.
Step 2B: For the same rationale as provided in claim 1, the additional distinguishing elements do not provide significantly more than the recited judicial exception because they are merely invoking the use of generic computing components as tools to execute the judicial exception and generally limiting the use of the judicial exception to a particular technological environment and field of use.
Conclusion: Based on this rationale, the claim has been deemed to be ineligible 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, 8, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al (US 20190345809 A1), hereinafter referred to as Jain, in view of Alali (Alali, A., Abughaban, M., Aman, B., and Ravela, S., “Hybrid data driven drilling and rate of penetration optimization”, Journal of Petroleum Science and Engineering”, May 2021, Volume 200), hereinafter referred to as Alali, in view Mohaghegh et al (US Patent Pub. No. 20150300151), hereinafter referred to as Mohaghegh, in view of Wicks (US 20220397029 A1), hereinafter referred to as Wicks, Tsuchihashi (Tsuchihashi, N., Wada, R., Ozaki, M., Inoue, T., Mopuri, K., Bilen, H., Nishiyama, T., Fujita, K., and Kusanagi, K., “Early Stuck Pipe Sign Detection with Depth-Domain 3D Convolutional Neural Network Using Actual Drilling Data”, April 2021, SPE Journal 26, pp. 551-562 ), hereinafter referred to as Tsuchihashi, Mitchell et al (Mitchell, R., and Samuel R., “How Good Is the Torque/Drag Model?”, March 2009, SPE Drilling and Completion, pp 62-71), hereinafter referred to as Mitchell, and Madasu (International Publication No. 2020046351), hereinafter referred to as Madasu.
Regarding claim 1, Jain discloses (except the limitations surrounded by brackets ([[..]])) A method comprising: A method is disclosed for predicting an ROP during a drilling operation ((Jain, ¶7) "Some embodiments of the present disclosure include a method of providing predictive models of rates of penetration and wear of an earth-boring tool during a planned drilling operation")
performing a drilling operation [[in a first hole section of a wellbore]]Drilling operations are described as being performed as part of a method where real-time data is received during the drilling operation ((Jain, ¶7) "Some embodiments of the present disclosure include a method of providing predictive models of rates of penetration and wear of an earth-boring tool during a planned drilling operation. The method may include receiving real-time data from a drilling operation at a trained hybrid physics and machine-learning model, analyzing the real-time data via the hybrid physics and machine-learning model, providing, via the hybrid physics and machine-learning model and based at least partially on the analysis, a predictive model representing a rate of penetration of an earth-boring tool and wear of the earth-boring tool throughout at least part of a remainder of the drilling operation, providing one or more recommendations of drilling parameters based on the predictive model, and operating at least a portion of the drilling operation using the one or more recommendations of drilling parameters.") using a drilling system, the drilling system comprising a plurality of sensors, a drill string, a drill bit, and a mud pump, A drilling system is depicted in Figure 1 comprising sensors 140, a drill string 110, and a drill bit 116 ((Jain, ¶30) "The drill string 110 may include a tubular member 112 that carries a drilling assembly 114 at its bottom end. The tubular member 112 may be made up by joining drill pipe sections or it may be a string of coiled tubing. A drill bit 116 may be attached to the bottom end of the drilling assembly 114 for drilling the borehole 102 of a selected diameter in a formation 118."); A drilling fluid is described as being pumped through a tubular member as part of the system ((Jain, ¶33) "During drilling, a drilling fluid from a source 136 thereof may be pumped under pressure through the tubular member 112, which discharges at the bottom of the drill bit 116 and returns to the surface 122 via an annular space (also referred as the "annulus") between the drill string 110 and an inside sidewall 138 of the borehole 102."). Mud is noted as a drilling fluid ((Jain, ¶54) "For example, the hybrid model 201 may utilize the ROP limiters module 322 to determine effects of mud (e.g., oil-based mud, solids content of mud, mud weights, mud viscosity, etc.), vibrations, rate sensitivity in drilling, bit balling, etc., on ROP of an earth-boring tool during a planned drilling operation.") wherein the plurality of sensors determine rotary speed data for the drill bit, weight-on-bit data for the drill bit, and [[pump flow rate data for the mud pump;]] The sensors are described as determining weight on bit ((Jain, ¶31) "The sensors 140 may include one or more of sensors 140 that determine acceleration, weight on bit, torque, pressure, cutting element positions, rate of penetration, inclination, azimuth, formation lithology, etc.”). The sensors are further described as measuring drill bit rotation in terms of RPM ((Jain, ¶34) "The sensors 140 may include any number and type of sensors 140, including, but not limited to, sensors generally known as the measurement-while-drilling (MWD) sensors or the logging-while-drilling (LWD) sensors, and sensors 140 that provide information relating to the behavior of the drilling assembly 114, such as drill bit rotation (revolutions per minute or "RPM"), tool face, pressure, vibration, whirl, bending, and stick-slip.")
determining, by the drilling system while advancing the drill bit during the drilling operation based on a plurality of drilling parameters [[specified by a user]], a first rate of penetration (ROP) [[for the first hole section]]; An ROP is predicted at the beginning of a drilling operation using the hybrid model (as a first ROP) ((Jain, ¶69) "Additionally, the hybrid model 201 trains the bit mechanics module 318 by predicting the ROP of a given earth-boring tool within a planned drilling operation at the beginning of the drilling operation (e.g., run) in a sharp state using a design and/or bit metrology pre-drilling operation (e.g., pre-run), and the hybrid 201 trains the hybrid bit mechanics module 318 by predicting the ROP at an end of the drilling operation in a worn state using metrology of a dull bit (e.g., earth-boring tool)."). The hybrid model is described as being part of the drilling system, thereby indicating that the estimation is performed by the drilling system ((Jain, ¶30) "FIG. 1 is a schematic diagram of an example of a drilling system 100 that may utilize the apparatuses and methods disclosed herein for drilling boreholes"). The prediction of the ROP is described as occurring during a drilling operation ((Jain, ¶7) "Some embodiments of the present disclosure include a method of providing predictive models of rates of penetration and wear of an earth-boring tool during a planned drilling operation"). The drilling operation is characterized as having a drill bit attached to the bottom end of a drilling assembly that is rotated in the bottom hole assembly to remove formation material in the wellbore, thereby indicating that the drilling operation comprises advancing the drill bit ((Jain, ¶2) "A drill bit and/or reamer attached to the bottom end of the drilling assembly is rotated by rotating the drill string from the drilling rig and/ or by a drilling motor ( also referred to as a "mud motor") in the bottom hole assembly ("BHA") to remove formation material to drill the wellbore"). Drilling operations are described as operations that involve drilling parameters ((Jain, ¶39) "Furthermore, "drilling operations" may refer to any operations that involve ( e.g., would be benefited by information related to) any of the above drilling parameter and/or lithology parameters.").
[[determining by the drilling system, a second hole section in the wellbore for the drilling operation, wherein the second hole section corresponds to entering a new subsurface formation in the drilling operation, and wherein the second hole section is different from the first hole section]]
determining, by the drilling system, a predicted ROP The hybrid model (black box function) is used to predict an ROP value ((Jain, ¶36) "Furthermore, as is described in greater detail below, the prediction system 129 utilizes the hybrid model 201 to generate one or more ROP and wear predictive models for given earth-boring tools and planned drilling operations."). for [[the second hole section of the drilling operation]] using the rotary speed data, the weight-on-bit data, the [[pump]] flow rate data, and a black box function, The string rotations per minute (rotary speed) and hydraulic fluid flow rates (flow rate data) are used by the hybrid model (black box function) ((Jain, ¶49) "For instance, the hybrid model 201 may utilize data, such as, surface data, data related to a well profile, a wellbore quality, adjustable kick off and stabilizers in the bottom-hole-assembly, mud type, flow rates of hydraulic fluids, string rotations per minutes, buckling, and/or vibrations to predict axial and torsional friction to be experienced by an earth-boring tool during a planned drill operation."). Weight on bit data is also obtained from sensors, wherein the sensor data is used by the hybrid model, as stated previously ((Jain, ¶31) "The sensors 140 may include one or more of sensors 140 that determine acceleration, weight on bit, torque, pressure, cutting element positions, rate of penetration, inclination, azimuth, formation lithology, etc.")
wherein the black box function comprises a physics-based model and a deep learning model, A hybrid model (black box function) 201 is depicted as containing physics models 203 and machine learning models 205 in Figure 2.
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The machine learning models are described as including deep learning ((Jain, ¶74) "Furthermore, in yet further embodiments, the machine-learning models 205 may include decision tree learning, regression trees, boosted trees, gradient boosted tree, multilayer perceptron, one-vs-rest, Naïve Bayes, k-nearest neighbor, association rule learning, a neural network, deep learning, pattern recognition, or any other type of machine-learning.")
wherein the physics-based model determines a predicted drag value of drilling equipment for the predicted ROP [[based on a drag formula]], The hybrid model may use a torque and drag module to predict axial friction of an earth-boring tool ((Jain, ¶49) "The hybrid model 201 may utilize the torque and drag module 316 to predict (e.g., estimate) axial and torsional friction to be experienced by an earth-boring tool during a planned drill operation."). Modules are described as being within the physics models, thereby indicating that the torque and drag module is part of the physics based model ((Jain, ¶80) "Upon tuning the coefficient library, the uncertain parameters, and the measured data via the parameter tuning process, the hybrid model 201 provides the tuned data ( e.g., tuned coefficient values) to one or more of the modules within the physics models 203 and the machine-learning models 205 of the hybrid model 201, as shown in act 430 of FIG. 4B.").
wherein the deep learning model determines a predicted [[stuck pipe index]] for the drilling operation for the predicted ROP, The machine learning model is described as being used to predict changes in ROP and changes in wear due to measured parameters as well as unmeasured parameters ((Jain, ¶85) "Additionally, the hybrid model 201 may analyze and/or utilize the tuned data with one or more black-box machine-learning models and/or neural networks to predict changes in ROP and changes in wear due to the influence of unaccounted factors, as shown in act 440 of FIG. 4B. For example, the hybrid model 201 may analyze the tuned data with one or more black-box machine-learning models and/or neural networks to predict changes in ROP and changes in wear due to measured parameters such as bottom-hole assemblies, wellbore profile, vibrations, drilling crew, and rig, as well as unmeasured parameters such as wellbore quality.")
wherein the black box function uses an objective function The hybrid model (black box function) processes data with fitness functions ((Jain, ¶76) "Upon analyzing the input data via the above described modules and machine-learning models 205, the hybrid model 201 processes any data related to measured and/or determined drilling parameters (e.g., ROP and wear parameters) with fitness functions, as shown in act 424 of FIG. 4B and processes uncertain parameters, described above in regard to uncertainty quantification module 326 and FIG. 3A, via a parameter tuning process, as shown in act 426 of FIG. 4B.") Fitness functions are described as objective functions ((Jain, ¶77) "Processing the data related to measured and/or determined drilling parameters (e.g., ROP and wear parameters) with fitness functions ( e.g., error or objective functions) may include applying one or more fitness functions to the data to prediction errors in (i.e. differences between) reference solutions ( e.g. measured values of parameters being predicted) and model predicted values.") to determine the predicted ROP based on satisfying a first [[user specified]] constraint of the predicted drag value and a second [[user specified]] constraint [[of the predicted stuck pipe index; and]] The hybrid model evaluates the fitness function to determine if the error is within a tolerance and the hybrid model subsequently adjusts the coefficients in the hybrid model within the constraints (satisfying constraints) identified by the coefficient library ((Jain, ¶79) "For example, if the error determined via the fitness functions is greater than a tolerance ( or improvement in the error in successive iterations is greater than a tolerance), the hybrid model 201 utilizes an algorithm to adjust (e.g., tune) the coefficients in the hybrid model 201 within the constraints identified by the coefficient library and modules described above."). The hybrid model is utilized to predict ROP ((Jain, ¶87) "The trained hybrid model 201 may then be utilized to simulate or predict ( e.g., estimate) ROP and wear models for a given set of input values (e.g., parameters) of an earth-boring tool and/or planned drilling operation."). The drag value is predicted, as stated previously, from the drag and torque module. Constraints are identified by the modules, thereby indicating that a constraint can be applied to the value generated from the module ((Jain, ¶79) "For example, if the error determined via the fitness functions is greater than a tolerance ( or improvement in the error in successive iterations is greater than a tolerance), the hybrid model 201 utilizes an algorithm to adjust (e.g., tune) the coefficients in the hybrid model 201 within the constraints identified by the coefficient library and modules described above.").
wherein the black box function uses the objective function The hybrid model leverages a fitness function ((Jain, ¶76) " Upon analyzing the input data via the above described modules and machine-learning models 205, the hybrid model 201 processes any data related to measured and/or determined drilling parameters (e.g., ROP and wear parameters) with fitness functions, as shown in act 424 of FIG. 4B and processes uncertain parameters, described above in regard to uncertainty quantification module 326 and FIG. 3A, via a parameter tuning process, as shown in act 426 of FIG. 4B."). The fitness function is described as being an objective function ((Jain, ¶77) " Processing the data related to measured and/or determined drilling parameters (e.g., ROP and wear parameters) with fitness functions ( e.g., error or objective functions) may include applying one or more fitness functions to the data to prediction errors in (i.e. differences between) reference solutions ( e.g. measured values of parameters being predicted) and model predicted values"). to determine the predicted ROP The hybrid model determines a predictive ROP model ((Jain, ¶36) " Furthermore, as is described in greater detail below, the prediction system 129 utilizes the hybrid model 201 to generate one or more ROP and wear predictive models for given earth-boring tools and planned drilling operations.") based on satisfying a value of The hybrid model evaluates the fitness function to determine if the error is within a tolerance and the hybrid model subsequently adjusts the coefficients in the hybrid model within the constraints (satisfying constraints) identified by the coefficient library ((Jain, ¶79) "For example, if the error determined via the fitness functions is greater than a tolerance ( or improvement in the error in successive iterations is greater than a tolerance), the hybrid model 201 utilizes an algorithm to adjust (e.g., tune) the coefficients in the hybrid model 201 within the constraints identified by the coefficient library and modules described above."). Constraints are identified by the modules, thereby indicating that a constraint can be applied to the value generated from the modules. [[one or more of top drive torque, weight-on-bit, fluid flow rate, stick-slip index, bit wear rate, stuck pipe probability index, and cuttings concentration in annulus; and]]
changing, automatically by the drilling system and during the drilling operation, a plurality of drilling parameters to [[adjust the first ROP to the predicted ROP]] in response to determining the predicted ROP The hybrid model predicts ROP ((Jain, ¶87) "The trained hybrid model 201 may then be utilized to simulate or predict ( e.g., estimate) ROP and wear models for a given set of input values (e.g., parameters) of an earth-boring tool and/or planned drilling operation."). Recommendations of drilling parameters are provided based on the hybrid predictive model ((Jain, ¶90) "Based on the real-time predictive ROP and wear models generated by the hybrid model 201, the hybrid model 201 may provide recommendations for drilling parameters, which may lead to real-time drilling parameters optimization."). The surface control unit is described as having instructions that cause the prediction system to provide recommendations of drilling parameters and subsequently utilize the recommendations in a drilling operation and perform other various functions during a planned drilling operation, thereby indicating that the change/utilization is performed automatically during the drilling operation ((Jain, ¶6) "The surface control unit may include a prediction system that includes at least one processor and at least one non-transitory computer-readable storage medium storing instructions thereon that, when executed by the at least one processor, cause the prediction system to: pre-train a plurality of modules individually within a hybrid physics and machine-learning model; train the plurality of modules together to develop the hybrid physics and machine-learning model based on input data; provide, via the hybrid physics and machine-learning model, a predictive model representing a rate of penetration of an earth-boring tool and wear of the earth-boring tool during a planned drilling operation, provide one or more recommendations of drilling parameters based on the predictive model, utilize the one or more recommendations in a drilling operation, receive real-time data from the drilling operation, retrain the hybrid physics and machine-learning model based on a combination of the input data and the real-time data; provide, via the retrained hybrid physics and machine-learning model, an updated predictive model of a rate of penetration of the earth-boring tool and wear of the earth-boring tool during a remainder of the planned drilling operation."). The recommendations are described as being used for operating at least a portion of drilling operation ((Jain, ¶7) " The method may include receiving real-time data from a drilling operation at a trained hybrid physics and machine-learning model, analyzing the real-time data via the hybrid physics and machine-learning model, providing, via the hybrid physics and machine-learning model and based at least partially on the analysis, a predictive model representing a rate of penetration of an earth-boring tool and wear of the earth-boring tool throughout at least part of a remainder of the drilling operation, providing one or more recommendations of drilling parameters based on the predictive model, and operating at least a portion of the drilling operation using the one or more recommendations of drilling parameters.")
wherein the plurality of drilling parameters comprises a weight-on-bit of the drill bit, a rotary speed of the drill bit, and a flow rate [[of the mud pump.]] ((Jain, ¶39) "Additionally, via the analysis and the trained hybrid model 201, the hybrid model 201 may provide predictions (e.g., simulations, models, values, etc.) related to drilling parameters such as, ( e.g., drilling operations that involve) for example, build-up-rates, tum rates, lateral ROP, unconfined compressive strength, walk rate, dog leg severity, confined compressive strength, contact forces, rib forces, bending moments, WOB, pressures, inclinations, azimuth, borehole trajectories, hole qualities, drilling torque, drilling vibrations, cutter damage (e.g., breakage, chipping, cracking, spalling, etc.), bit trip, gage and bit body wear, etc. In further embodiments, the hybrid model 201 may provide predictions (e.g., simulations, models, values, etc.) related to lithology parameters such as, ( e.g., drilling operations that involve) for example, rock types, rock strengths, grain/clast sizes, mineralogy, fabric, chemical properties, compositions, porosity, permeability, and/or texture of a subterranean formation to be drilled.") ((Jain ¶49) "For instance, the hybrid model 201 may utilize data, such as, surface data, data related to a well profile, a wellbore quality, adjustable kick off and stabilizers in the bottom-hole-assembly, mud type, flow rates of hydraulic fluids, string rotations per minutes, buckling, and/or vibrations to predict axial and torsional friction to be experienced by an earth-boring tool during a planned drill operation.")
Jain alone does not explicitly contemplate performing drilling in one hole section of a reservoir and obtaining an ROP for that first section to use in the prediction of a predicted ROP for a second hole section of the drilling operation. Nor does Jain contemplate determining a second hole section in the wellbore. However, Alali discloses performing a drilling operation in a first hole section of a wellbore and determining an ROP for the first hole section Real-time data is collected during a drilling operation ((Alali, Page 8, Col 2, ¶4) "The model produces a recommendation to adjust the drilling parameters based on the correlation between the model and real-time data, which is displayed directly to the driller."). The planned drilling operation for a well may comprise multiple formations ((Alali, Page 6, Col 1, ¶5) "The queries used to extract the dataset meet the above specifications and contain the following information: controllable dynamic parameters (WOB, RPM, GPM), geological formation tops (in depth domain), all drilling related NPT incidents, section casing points, and the planned and new well casing points and formation tops."); See also Alali Figure 8 depicting planned well having at least formation 1 and formation 3. During drilling ROP is collected as a relevant parameter ((Alali, Page 6, Col 1, ¶2) "Dynamic drilling data is less recorded, though relevant parameters like time, depth, measured depth, WOB, RPM, GPM, ROP, TRQ, and SPP are collected. This data is collected once every 5s. All data is stored in a centralized system and accessed through SQL queries.")
determining, by the drilling system, a second hole section in the wellbore for the drilling operation, wherein the second hole section corresponds to entering a new subsurface formation in the drilling operation, and wherein the second hole section is different from the first hole section; Static drilling data, including geological formation top location is reported into a system used for collecting drilling data via sensors ((Alali, Page 6, Col 1, ¶1) "The raw data used in this model is collected by physical sensors on the rig and sent to a remote operational center for monitoring and supervision. The collected drilling data is divided into static and dynamic sections. Static drilling data includes the bottom hole location, deviation, geological formation tops, NPT incidents, bit type and classification, and mud type. This information is reported and entered into the system twice a day as part of a daily drilling report.". A planned well may comprise multiple geological formations at different depths where the drilling occurs, including at least 2 formations ((Alali, Page 6, Col 2, ¶4) "After the data is cleaned, reconstructed, and stacked, it is calibrated based on the “planned well” forecast formation tops to represent the depth variation of geological formation within the same field, as shown in Fig. 8."); See also Alali Figure 8 depicting 3 distinct formations.
predicting ROP for the second hole section of the drilling operation A model, based on existing data of wells at specified sections corresponding to sections of a planned well, is used to predict controllable parameters associated with a highest ROP value for that given section ((Alali, Page 4, Col 2, ¶4) " The output of the model provides a recommendation of optimal controllable dynamic drilling parameters per field, hole section, and depth step.); ((Alali, Page 8, Col 1, ¶1) "Each of the selected models and the physical responses are fed into a heat-map at a rate of 0.5 ft. By the end of this cycle, we will have 20 points of RPM and WOB, with the resulting r ROP. A final model (WOB and RPM) is selected and amended for the following 10 ft, based on the highest ROP value..")
changing drilling parameters to adjust the first ROP to the predicted ROP after determining a recommended ROP. The current practice is to adjust the drilling parameters for an optimal ROP per section and the disclosed methodology replicates existing best drilling practices as such. Accordingly if the first ROP is obtained from measured values in a given section and used to make the predicted ROP, upon determination that sections have changed while advancing the drill bit, the adjustment to the optimal ROP provided by the model would inform an update of the measurable ROP ((Alali, Page 4, Col 1, ¶4) "Current ROP optimization practice is to select one value for each controllable dynamic drilling parameter and adjust it per section"); ((Alali, Page 4, Col 1, ¶5) "Existing data from the best performing offset wells (per field and hole section) were used to build a model that recommends these optimal parameters. This process should replicate existing best drilling practices and lead to optimum ROP, assuming the same drilling hardware is used"). The model produces optimal controllable parameters based on an identified ROP value ((Alali, Page 14, Col 1, ¶4) " The model produces a recommendation to adjust the drilling parameters based on the correlation between the model and real-time data, which is displayed directly to the driller.") ((Alali, Page 2, col 1, ¶1) "Controllable factors (weight on bit [WOB], rotary speed [RPM], pump flow rate [GPM]) can be altered quickly and manually by the driller, adjusting ROP in real-time without impacting overall operations (Gan et al., 2019a).") ; See also Alali Figure 9 depicting the output of the model as parameters associated with the highest ROP value.
Alali is analogous to the claimed invention because it is related to the same field of endeavor of applying hybrid modeling approaches for ROP optimizations. 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 teachings of Alali so as to define a workflow by which to employ the hybrid model disclosed by Jain because some teaching, suggestion, or motivation in the prior art references would have led one having skill to do so in order to arrive at the claimed invention. Jain teaches the utilization of sensors to obtain lithology information for the formation ((Jain, ¶31) " The sensors 140 may include one or more of sensors 140 that determine acceleration, weight on bit, torque, pressure, cutting element positions, rate of penetration, inclination, azimuth, formation lithology, etc."). Jain further teaches that the formation lithology is considered when making predictions for a formation to be drilled but does not particularly disclose how differences in formation sections (that would be distinguishable via the lithology data) necessarily influence the timing and updating of drilling parameters ((Jain, ¶39) " In further embodiments, the hybrid model 201 may provide predictions (e.g., simulations, models, values, etc.) related to lithology parameters such as, ( e.g., drilling operations that involve) for example, rock types, rock strengths, grain/clast sizes, mineralogy, fabric, chemical properties, compositions, porosity, permeability, and/or texture of a subterranean formation to be drilled");. However, Alali explicitly notes that current ROP optimization practice includes changing controllable dynamic parameters per each section of the geological formation ((Alali, Page 4, Col 1, ¶4) " Current ROP optimization practice is to select one value for each controllable dynamic drilling parameter and adjust it per section"). Alali further states that the hybrid methodology disclosed therein builds upon such practices to employ the changing of drilling parameters dynamically through the drilling process and including during changes to sections in the formation ((Alali, Page 14, Col 1, ¶3) " This system is transforming the drilling practices from one value of parameter per section to dynamic optimum parameterization."). Accordingly, because Jain suggests that the formation lithology is considered during the drilling process to inform predictions and because Alali explicitly notes that current drilling practices at least consider the change in section when modifying operating parameters, the combination would have accordingly been obvious so as to consider the transition between sections when determining subsequent drilling operation into new formations.
The proposed combination does not disclose explicitly; however the proposed combination in view of Mohaghegh discloses pump flow rate data for the mud pump; …pump flow rate …and flow rate of the mud pump A sensor is used to obtain information regarding mud flow rate ((Mohaghegh, ¶19) " …: receive real-time raw data from sensors monitoring the drilling operation and/or bore, wherein the real-time raw data comprises one or more items selected from the group consisting of: Logging While Drilling information (LWD), Measurement While Drilling information (MWD), mud weight, mud viscosity, mud yield point, mud flow rate, … "). The mud flow rate is described as coming from the mud pump ((Mohaghegh, ¶41) "The controller 20 can also control: the rotary device 23 to at least one of control the rotational speed of the drill string 3 and control the torque imposed on the drill string 13; the flow of mud from the mud pump 24; the amount of mud diverted by the flow diverter 25; and operation of the active vibration control device 26.")
Mohaghegh is analogous art in that in pertains to optimizing rate of penetration in drilling operations. 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 included a mud pump flow rate sensor into the system of Jain and used the sensor data as part of the ROP optimization methodology because some teaching, suggestion, or motivation would have led one having ordinary skill in the art to combine the prior art elements to arrive at the claimed invention. Jain discloses a system that utilizes mud and a pump for drilling fluid and sensors but does not explicitly disclose that a mud pump flow rate value is obtained via the sensors. Jain further discloses that hydraulic fluid flow rates are used by the hybrid model but doesn’t particularly disclose the hydraulic flow rate in terms of the pump. Mohaghegh discloses the use of a sensor to measure real time raw data including mud flow rate, wherein the mud flow is characterized as coming from the mud pump. Because Jain suggests that the ROP optimization uses hydraulic flow rates and suggests that the system employs sensing mechanisms, it would have accordingly been obvious to one having skill in the art to utilize a sensor so as to obtain the hydraulic flow rate and Mohaghegh provides an explicit implementation by which to do so.
The proposed combination does not explicitly disclose; however, the proposed combination in view of Wicks discloses a plurality of drilling parameters specified by a user, ((Wicks, ¶187) "As explained, a user attempts to select the inputs (ROP, WOB, torque, and DIFF_P set points and the three gain values) to elicit desired drilling behavior."). The proposed combination in view of Wicks further discloses user specified constraints ((Wicks, ¶184) " As to training, optimal drilling performance can be quantified through a reward function that considers tailored characteristics. For example, consider a reward function that favors fast, stable drilling that does not exceed user-defined constraints on drilling parameters that can include weight on bit (WOB), torque (TOR or TQA), and differential pressure (DeltaP or DIFF_P).")
Wicks is analogous art because it is related to the same field of endeavor of optimized ROP estimations for drilling operations. 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 user specified drilling parameters and user specified constraints into the methodology of the proposed combination because combining prior art elements according to known methods would yield predictable results. Jain discloses the utilization of drilling parameters and constraints but does not particularly reference them as being specified by a user. Jain further describes a system comprising a user interface by which a user can input information into the system but does not particularly disclose what information may be input to the system. Wicks notes that both drilling parameters and constraints can be specified by the user. By combining the user-specified parameters and constraints as disclosed by Wicks into the method of Jain, by enabling the inputs of these user-specified values through the user interface provided by Jain, one having skill in the art would realize the claimed invention. The combination of the prior art elements would yield the predictable results of having a baseline of operation established based on the user inputs. This baseline of information would enable the ROP optimization methodology in such a way that the ROP is optimized according to user preferences.
The proposed combination does not disclose; however, the proposed combination in view of Tsuchihashi discloses stuck pipe index and of the predicted stuck pipe index; and A 3D CNN is leveraged to determine a stuck pipe risk prediction ((Tsuchihashi, Page 552, ¶11) "The model is trained with the binary labeled data to output the probability of the early sign, namely the stuck risk. ")
Tsuchihashi is analogous art because it is related to the same field of endeavor of optimizing drilling operations using machine learning approaches. 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 utilized the machine learning prediction methodology to predict a stuck pipe occurrence risk value into the methodology of the proposed combination because some teaching, suggestion, or motivation would have led one having ordinary skill in the art to do so in order to arrive at the claimed invention. The proposed combination, particularly in view of Jain discloses the utilization of machine learning models as part of a hybrid model that is used to predict optimal ROP. Jain discloses utilizing machine learning models to account for the influence of accounted for factors during drilling operations ((Jain, ¶85) "Additionally, the hybrid model 201 may analyze and/or utilize the tuned data with one or more black-box machine-learning models and/or neural networks to predict changes in ROP and changes in wear due to the influence of unaccounted factors, as shown in act 440 of FIG. 4B. For example, the hybrid model 201 may analyze the tuned data with one or more black-box machine-learning models and/or neural networks to predict changes in ROP and changes in wear due to measured parameters such as bottom-hole assemblies, wellbore profile, vibrations, drilling crew, and rig, as well as unmeasured parameters such as wellbore quality."). Tsuchihashi notes that stuck pipe is one of the major drilling problems that accounts for nonproductive time in drilling operations ((Tsuchihashi, Page 551, ¶2) "Stuck is one of the major drilling problems that accounts for nonproductive time.") and notes that physics-based modeling for this phenomenon is often insufficient ((Tsuchihashi, Page 551, ¶4) " Conventionally, when detecting an anomaly during drilling operation, alerts are generated when there is an anomalous deviation between the measured value and predicted value (Salminen et al. 2017). Predicted value is often calculated from preliminary simulation by a physics-based model, which requires geological survey in advance. However, the conventional method raises many false alarms due to the insufficient accuracy of geological information. Unrau et al. (2017) claim that this fosters an environment in which drillers can become desensitized to alarms. As a result, drillers often rely on their experience and intuition instead of the alarm system, which occasionally results in incidents where meaningful alarms are ignored. It could be concluded that the current alarm systems require higher reliability. "). Tschihashi provides a methodology that demonstrates an effective solution to predict the risk of stuck pipe occurrence using a deep neural network approach. Accordingly, it would have been obvious to one having ordinary skill in the art to apply the machine learning approach taught by Tschihashi into the system that leverages machine learning models to predict ROP by Jain because this methodology has been demonstrated to be effective. Furthermore, Alali as referenced above notes that high ROP correlates to stuck pipe incidents and further notes that ROP optimizations should minimize the occurrence of NPT causes such as stuck pipe ((Alali, Page 1, Col 2 ¶1) "Higher ROP values are commonly considered good and result in faster delivery of product, thereby saving time and money. However, exceedingly high ROPs may compromise the ability of a drilling fluid to transport and suspend drilled cuttings to the surface, resulting in poor hole cleaning and impacting wellbore integrity. In extreme cases, this can lead to instability, stuck-pipe incidents, or faster wearing of the drill bit (Akgun 2002). These events lead to a reduction or complete stop in drilling, resulting in costly NPT. To overcome this limitation, a rigorous ROP optimization plan should minimize any non-productive time (NPT)-inciting events."). Accordingly, in light of these findings, it would have been obvious to combine the prior art references so as to account for stuck pipe incidents with regard for ROP optimization.
The proposed combination does not disclose; however, the proposed combination in view of Mitchell discloses based on a drag formula, A torque and drag model is described which includes formulas for drag ((Mitchell, page 63, Col 1, ¶Drag calculations) soft string model drag equation torque and drag model…"")
Mitchell is analogous art in that is it related to the same field of endeavor of modeling drilling operations. 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 implemented the drag and torque model as the physics model for the drag and torque module of the methodology disclosed by Jain because combining prior art elements according to known methods would have yielded predictable results. Jain discloses the use of a drag and torque module for physics-based modeling but does not particularly disclose which model is to be implemented. Mitchell discloses that commonly used torque and drag models are used in drillstring modeling and further provides a particular implementation of a model that includes drag formulas as part of the model. By applying the torque and drag model of Mitchell as the torque and drag module disclosed by Jain, one having skill in the art would arrive at the claimed invention and subsequently have a particular drag equation by which to base the drag prediction off of in the torque and drag module.
The proposed combination in view of Jain does not disclose, however the proposed combination in view of Madasu discloses based on satisfying a value of one or more of top drive torque, weight-on-bit, fluid flow rate, stick-slip index, bit wear rate, stuck pipe probability index, and cuttings concentration in annulus; and An objective function to optimize ROP is subject to constraints ((Madasu, ¶54). “To account for nonlinearity and/or noise in the real-time or drilling rate time series data (e.g., from sensors 309), the objective function generated by neural network model 306 for defining the response value of the operating variable may be subject to a set of nonlinear constraints 410.”). Constraints may be derived from models ((Madasu, ¶54; Figure 4, item 410), “Nonlinear constraints 410 may be derived from data models representing different aspects of the drilling operation that may be associated with certain values of the controllable parameters and that may impact the response value of the operating variable to change over the course of the drilling operation.”). Thresholds may be set for the WOB as a constraint ((Madasu, ¶55), "Torque and drag model 412 may therefore provide a threshold on the WOB to avoid excessive wear that can lead to failure of the drill bit or other components of the drilling assembly attached to the end of the drill string." ). Fluid flow rate may be set as a maximum fluid injection or pumping rate constraint ((Madasu ¶55), “Thus, drilling fluid model 416 may provide a maximum fluid injection or pumping rate at which debris filled fluid can be removed from the wellbore”). Bit wear rate may include a threshold/constraint of excessive wear that can lead to failure of the drill bit ((Madasu, ¶55), “Torque and drag model 412 may therefore provide a threshold on the WOB to avoid excessive wear that can lead to failure of the drill bit or other components of the drilling assembly attached to the end of the drill string.”). Cuttings concentration in annulus may be constrained by the maximum amount of debris that can be removed from the wellbore ((Madasu, ¶55 “The ROP of the drill bit may be limited by the maximum amount of debris that can be removed from the wellbore by fluid injection or pumping over a given period of time. Thus, drilling fluid model 416 may provide a maximum fluid injection or pumping rate at which debris filled fluid can be removed from the wellbore.”).
Madasu is analogous art in that it pertains to ROP optimization for drilling applications and leverages machine learning methods and optimization functions to achieve the optimal ROP. 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 particular constraints disclosed by Madasu into the objective function disclosed by Jain because combining prior art elements according to known methods would yield predictable results. Objective functions for optimizations are known in the art to be subject to constraints as part of the optimization. Jain provides an objective function by which to optimize the ROP value and notes that the fitting function (objective function) may be subject to constraints but does not particularly disclose what the constraints may be. Madasu provides explicit examples as to the constraints that can be applied to an objective function for ROP optimization, wherein the constraints are related to equipment which may be physically constrained in order to perform properly. Accordingly, it would have been obvious to combine to prior art references because imparting constraints on the objective function with consideration to the equipment so as to ensure equipment and process parameters stay within the expected ranges of the drilling operation.
Regarding claim 8, the limitations are substantially similar to that recited in Claim 1. The claim recites additional elements that are distinguished from claim 1 which are disclosed by Jain. A system comprising: ((Jain, ¶27) " Some embodiments of the present disclosure include a bit rate of penetration and wear prediction system (hereinafter "prediction system") for drilling optimization during pre-well planning as well as real-time drilling.")
a processor; and ((Jain, ¶6) " The surface control unit may include a prediction system that includes at least one processor and at least one non-transitory computer-readable storage medium storing instructions thereon that, when executed by the at least one processor, cause the prediction system to:")
a memory coupled to the processor and storing instructions, wherein the instructions, when executed by the processor, are configured to perform a method comprising: ((Jain, ¶94) " In one or more embodiments, the processor 602 includes hardware for executing instructions, such as those making up a computer program. As an example, and not by way of limitation, to execute instructions, the processor 602 may retrieve (or fetch) the instructions from an internal register, an internal cache, the memory 604, or the storage device 606 and decode and execute them.")
The remaining limitations performing a drilling operation in a first hole section of a wellbore using a drilling system comprising a plurality of sensors, a drill string, a drill bit, and a mud pump, wherein the plurality of sensors determine rotary speed data for the drill bit, weight-on-bit data for the drill bit, and pump flow rate data for the mud pump;
determining, while advancing the drill bit during the drilling operation based on a plurality of drilling parameters specified by a user, a first rate of penetration (ROP) for the first hole section;
determining a predicted ROP for the second hole section of the drilling operation using the rotary speed data, the weight-on-bit data, the pump flow rate data, and a black box function,
wherein the black box function comprises a physics-based model and a deep learning model,
wherein the physics-based model determines a predicted drag value of drilling equipment for the predicted ROP based on a drag formula,
wherein the deep learning model determines a predicted stuck pipe index for the drilling operation for the predicted ROP,
wherein the black box function uses an objective function to determine the predicted ROP based on satisfying a first user specified constraint of the predicted drag value and a second user specified constraint of the predicted stuck pipe index; and
wherein the black box function uses the objective function to determine the predicted ROP based on satisfying a value of one or more of top drive torque, weight-on-bit, fluid flow rate, stick slip index, bit wear rate, stuck pipe probability index, and cuttings concentration in annulus; and
changing, automatically during the drilling operation, a plurality of drilling parameters to adjust the first ROP to the predicted ROP in response to determining the predicted ROP,
wherein the plurality of drilling parameters comprises a weight-on-bit of the drill bit, a rotary speed of the drill bit, and a flow rate of the mud pump are substantially similar to that recited in claim 1 and are rejected under the same rationale.
Regarding claim 15, the limitations are substantially similar to that recited in Claim 1. The claim recites additional elements that are distinguished from claim 1 which are disclosed by Jain. A wellsite comprising: ((Jain, ¶29) " FIG. 1 is a schematic diagram of an example of a drilling system 100 that may utilize the apparatuses and methods disclosed herein for drilling boreholes")
a drilling system ((Jain, ¶29) " FIG. 1 is a schematic diagram of an example of a drilling system 100 that may utilize the apparatuses and methods disclosed herein for drilling boreholes") comprising a plurality of sensors, a drill string, a drill bit, and a mud pump, wherein the plurality of sensors determine rotary speed data for the drill bit, weight- on-bit data for the drill bit, and pump flow rate data for the mud pump;
a control system connected to the drilling system((Jain, ¶6) " The earth-boring tool system may include a drilling assembly for drilling a wellbore and a surface control unit operably coupled to the drilling assembly."), wherein the control system comprises a processor and a memory, and wherein the memory comprises a program configured to perform a method comprising: ((Jain, ¶6) " The surface control unit may include a prediction system that includes at least one processor and at least one non-transitory computer-readable storage medium storing instructions thereon that, when executed by the at least one processor, cause the prediction system to…")
The remaining limitations performing a drilling operation in a first hole section of a wellbore using the drilling system;
determining, while advancing the drill bit during the drilling operation based on a plurality of drilling parameters specified by a user, a first rate of penetration (ROP) for the first hole section;
determining a second hole section in the wellbore for the drilling operation, wherein the second hole section corresponds to entering a new subsurface formation in the drilling operation, and wherein the second hole section is different from the first hole section;
determining a predicted ROP for the second hole section of the drilling operation using the rotary speed data, the weight-on-bit data, the pump flow rate data, and a black box function,
wherein the black box function comprises a physics-based model and a deep learning model,
wherein the physics-based model determines a predicted drag value of drilling equipment for the predicted ROP based on a drag formula,
wherein the deep learning model determines a predicted stuck pipe index for the drilling operation for the predicted ROP,
wherein the black box function uses an objective function to determine the predicted ROP based on satisfying a first user specified constraint of the predicted drag value and a second user specified constraint of the predicted stuck pipe index; and
wherein the black box function uses the objective function to determine the predicted ROP based on satisfying a value of one or more of top drive torque, weight-on-bit, fluid flow rate, stick slip index, bit wear rate, stuck pipe probability index, and cuttings concentration in annulus; and
changing, automatically during the drilling operation, a plurality of drilling parameters to adjust the first ROP to the predicted ROP in response to determining the predicted ROP,
wherein the plurality of drilling parameters comprises a weight-on-bit of the drill bit, a rotary speed of the drill bit, and a flow rate of the mud pump are substantially similar to that recited in claim 1 and are therefore rejected under the same rationale.
Conclusion
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.
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/E.G.L./Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
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