DETAILED ACTION
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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on June 21, 2023 and March 5, 2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the Examiner.
Drawings
The drawings are objected to under 37 CFR 1.83(a) because they fail to show multiple components and steps as described in the specification. Figure 5 should show the appropriate components or steps rather than mere labels. Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing, see MPEP § 608.02(d).
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 15, 16, and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 15 recites the limitation “the validated non-vertical well,” which has insufficient antecedent basis in the claims. Claim 1, on which this claim depends, recites “a validated corrected trajectory” for a non-vertical well. However, the claim does not recite the non-vertical well itself being validated. For the purposes of compact prosecution, this limitation will be interpreted as “the validated corrected trajectory for the selected non-vertical well.”
Regarding Claims 16 and 18, the claims require the limitations of Claim 15, on which these claims depend, and the claims are rejected under 35 U.S.C 112(b) for the same reasons.
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.
Claim(s) 1-28 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mental processes and/or mathematical concepts without significantly more.
The following is an analysis of independent Claim 1 based on the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
Step 1, Statutory Category:
Yes: Claims 1-25 are directed to a machine.
Step 2A Prong I, judicial Exception:
The Examiner submits that the foregoing claim limitations constitute mental processes and/or mathematical concepts, given their broadest reasonable interpretation. Abstract ideas are bolded.
Claim 1 recites the limitations:
1. A method for processing seismic data relating to a reservoir zone comprising a plurality of substantially vertical wells having well-defined trajectories and one or more non-vertical wells; the method comprising:
a) obtaining a trained neural network, having been trained to infer well data from seismic data and having been trained on training well data relating only to wells having well-defined trajectories;
b) selecting a non-vertical well of said one or more non-vertical wells, said selected non-vertical well having an associated logged trajectory;
c) using said trained neural network to invert seismic data relating to said reservoir zone, to obtain inversion well data;
d) comparing said inversion well data to observed well data relating to the selected non-vertical well;
e) determining, based on said comparison, one or more candidate corrected trajectories for the selected non-vertical well, wherein the one or more candidate corrected trajectories are corrected with respect to the logged trajectory; and
f) validating said one of said one or more candidate corrected trajectories to determine a validated corrected trajectory for the selected non-vertical well.
The limitations selecting a non-vertical well, invert seismic data, comparing said inversion well data, determining […] one or more candidate corrected trajectories, and validating said one of said one or more candidate corrected trajectories are abstract ideas because they are directed to mental processes, observations, evaluations, judgements, and opinions. A user can perform the mental judgement of selecting a non-vertical well, the mental judgement of comparing well data, and the mental evaluations of determining candidate corrected trajectories and validating trajectories.
Step 2A Prong II, Integration into a Practical Application:
Claim 1 recites the following additional claim limitations outside the abstract idea which only present general fields of use, mere instructions to apply an exception, and/or insignificant extra-solution activity:
A method for processing seismic data relating to a reservoir zone comprising a plurality of substantially vertical wells having well-defined trajectories and one or more non-vertical wells (general field of use, see MPEP § 2106.05(h)).
a) obtaining a trained neural network (insignificant extra-solution activity of data gathering, see MPEP § 2106.05(g)).
having been trained to infer well data from seismic data and having been trained on training well data relating only to wells having well-defined trajectories (general field of use, see MPEP § 2106.05(h)).
said selected non-vertical well having an associated logged trajectory (general field of use, see MPEP § 2106.05(h)).
ADDITIONAL ELEMENTS:
Claim 1 recites the following additional elements:
“neural network” is a high level recitation of generic computer components, computer elements used as a tool, and represent mere instructions to apply the abstract idea on a computer as in MPEP § 2106.05(f). Therefore, the claim does not integrate the recited abstract ideas into a practical application.
Step 2B, Significantly More:
When considered individually or in combination, the additional limitations and elements of Claim 1 do not amount to significantly more than the judicial exceptions for the same reasons above as to why the additional limitations do not integrate the abstract idea into a practical application.
The additional element “neural network” reciting generic computer components as mere instructions to apply on a computer per MPEP § 2106.05(f) are carried over and do not provide significantly more than the abstract idea. The examiner also notes that the specification does not define the structures of the additional elements in any way that could be used to integrate the abstract idea into a practical application.
The additional limitations identified as mere instructions to apply an exception, insignificant extra-solution activity, or general field of use above are carried over and also do not provide significantly more than the abstract idea. See MPEP § 2106.04(d) referencing MPEP § 2106.05(f), MPEP § 2106.05(g), and MPEP § 2106.05(h).
The insignificant extra solution activity of obtaining a trained neural network is considered to be further well understood, routine and conventional, see MPEP § 2106.05(d)(II); “The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity […] i. Receiving or transmitting data over a network […] iv. Storing and retrieving information in memory.”
Considering the claim limitations in combination and the claims as a whole does not change this conclusion, and Claim 1 is ineligible under 35 U.S.C 101. The Examiner suggests expressly reciting the step of training the claimed neural network, instead of merely obtaining and using said neural network, to provide a practical application.
Regarding Claim 2, the claim recites A method as claimed in claim 1, wherein said observed well data comprises well data logged during drilling of the selected non-vertical well, and said logged trajectory comprises the trajectory logged during said drilling; this limitation is considered to merely link the judicial exception to a particular field of use and/or technological environment under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(h).
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, Claim 2 is ineligible under 35 U.S.C 101.
Regarding Claim 3, the claim would be eligible under 35 U.S.C 101 if rewritten into independent form, including the limitations of Claim 1, on which this claim depends. Specifically, the explicitly recited step of retraining the neural network using training data relating to said training subset and the non-vertical well having a trajectory defined by the selected candidate corrected trajectory is not a mental process or mathematical concept, and provides a practical use that is not a field of use, mere instructions to apply an exception, or insignificant extra-solution activity that is well-understood, routine conventional activity.
Regarding Claims 4 and 5, the claims depend from Claim 3 and would be eligible under 35 U.S.C 101 for the same reasons.
Regarding Claim 6, the claim recites A method as claimed in claim 1, wherein said d) comprises determining as a candidate corrected trajectory, a trajectory which maximizes correlation of said inversion well data to observed well data; this limitation is considered to constitute additional mental processes under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). A user can perform the mental evaluation of determining a candidate trajectory that maximizes correlation between inversion and observed well data. A user may use pen and paper to calculate a maximal candidate trajectory.
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations are considered to constitute additional mental processes under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, Claim 6 is ineligible under 35 U.S.C 101.
Regarding Claim 7, the claim recites A method as claimed in claim 1,wherein steps d) and e) are performed per trajectory portion of the selected non- vertical well; this limitation is considered to merely link the judicial exception to a particular field of use and/or technological environment under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(h).
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, Claim 7 is ineligible under 35 U.S.C 101.
Regarding Claim 8, the claim recites A method as claimed in claim 7, wherein each of said portions comprises a respective region of uncertainty; this limitation is considered to merely link the judicial exception to a particular field of use and/or technological environment under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(h).
and each candidate corrected trajectory is determined within the respective region of uncertainty for each of said portions; this limitation is considered to constitute additional mental processes under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). A user can perform the mental evaluation of determining a candidate trajectory based on uncertainty. A user may use pen and paper to determine the candidate trajectory.
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations are considered to constitute additional mental processes under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, Claim 8 is ineligible under 35 U.S.C 101.
Regarding Claim 9, the claim recites A method as claimed in claim 7, wherein step c) is performed individually per portion, treating each portion as a vertical well; this limitation is considered to merely link the judicial exception to a particular field of use and/or technological environment under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(h).
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, Claim 9 is ineligible under 35 U.S.C 101.
Regarding Claim 10, the claim recites A method as claimed in claim 1,wherein step e) is performed subject to one or more constraints, such that said one or more candidate corrected trajectories respect said constraints; this limitation is considered to constitute additional mental processes under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). A user can perform the mental evaluation of determining a candidate trajectory based on constraints. A user may use pen and paper to determine the candidate trajectory.
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations are considered to constitute additional mental processes under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, Claim 10 is ineligible under 35 U.S.C 101.
Regarding Claim 11, the claim recites A method as claimed in claim 10, wherein said one or more constraints comprise one or more drilling constraints based on drilling physics; this limitation is considered to merely link the judicial exception to a particular field of use and/or technological environment under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(h).
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, Claim 11 is ineligible under 35 U.S.C 101.
Regarding Claim 12, the claim recites A method as claimed in claim 10, wherein said one or more constraints define a maximum curvature and/or deviation angle along the trajectory; this limitation is considered to merely link the judicial exception to a particular field of use and/or technological environment under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(h).
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, Claim 12 is ineligible under 35 U.S.C 101.
Regarding Claim 13, the claim recites A method as claimed in claim 1 wherein said training well data in a first training of said neural network relates only to said plurality of substantially vertical wells; this limitation is considered to merely link the judicial exception to a particular field of use and/or technological environment under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(h).
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, Claim 13 is ineligible under 35 U.S.C 101.
Regarding Claim 14, the claim recites A method as claimed in claim 1, comprising performing steps b) to f) iteratively for each of said non-vertical wells; this limitation is considered to be insignificant extra-solution activity under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(g). The insignificant extra-solution activity is further well-understood, routine conventional activity under step 2B of the abstract idea analysis, see MPEP § 2106.05(d)(II); “The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity […] ii. Performing repetitive calculations.”
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, Claim 14 is ineligible under 35 U.S.C 101.
Regarding Claim 15, the claim recites A method as claimed in claim 14, wherein, for each iteration, the neural network is retrained with training data comprising data relating to the validated non-vertical well as validated in that iteration; this limitation is considered to merely link the judicial exception to a particular field of use and/or technological environment under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(h).
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, Claim 15 is ineligible under 35 U.S.C 101.
Regarding Claim 16, the claim recites A method as claimed in claim 15, comprising using the neural network trained at the final iteration to perform an inversion on further seismic data relating to said reservoir zone to obtain further well data relating to said reservoir zone; this limitation is considered to be mere instructions to apply the identified mental evaluation of performing an inversion on a computer under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(f).
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, Claim 16 is ineligible under 35 U.S.C 101.
Regarding Claim 17, the claim would be eligible under 35 U.S.C 101 if rewritten into independent form, including the limitations of Claim 1, on which this claim depends. Specifically, the explicitly recited step of retraining the neural network on all validated non-vertical wells is not a mental process or mathematical concept, and provides a practical use that is not a field of use, mere instructions to apply an exception, or insignificant extra-solution activity that is well-understood, routine conventional activity.
Regarding Claim 18, the claim recites A method as claimed in claim 16, comprising optimizing a production strategy to produce hydrocarbon from said reservoir zone based on said further well data; this limitation is considered to be mere instructions to apply an exception under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(f).
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, Claim 18 is ineligible under 35 U.S.C 101.
Regarding Claim 19, the claim recites A method as claimed in claim 1, wherein said seismic data comprises high frequency content up to 3 times the frequency of a seismic wavelet emitted in a subsoil to obtain said seismic data; this limitation is considered to merely link the judicial exception to a particular field of use and/or technological environment under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(h).
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, Claim 19 is ineligible under 35 U.S.C 101.
Regarding Claim 20, the claim recites A method as claimed in claim 1,wherein said seismic data comprises pre- stack seismic data; this limitation is considered to merely link the judicial exception to a particular field of use and/or technological environment under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(h).
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, Claim 20 is ineligible under 35 U.S.C 101.
Regarding Claim 21, the claim would be eligible under 35 U.S.C 101 if rewritten into independent form, including the limitations of Claim 1, on which this claim depends. Specifically, the explicitly recited step of training said neural network based on training data relating to only said substantially vertical wells to obtain said trained neural network is not a mental process or mathematical concept, and provides a practical use that is not a field of use, mere instructions to apply an exception, or insignificant extra-solution activity that is well-understood, routine conventional activity.
Regarding Claims 22-25, the claims depend from Claim 21 and would be eligible under 35 U.S.C 101 for the same reasons.
The following is an analysis of independent Claim 26 based on the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
Step 1, Statutory Category:
No: Claim 26 is not directed to a patent eligible statutory category.
Claim 26 is directed to “A computer program.” Under step 1 of the 35 U.S.C 101 analysis determining statutory category, the claim does not fall within at least one of the four categories of patent eligible subject matter, see MPEP § 2106.03. The claim is directed to a product lacking a physical or tangible structure in the form of an organizational structure, such as a computer program per se (often referred to as “software per se”). “Computer program” could be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. Therefore, the claim is ineligible under 35 U.S.C 101. Applicant may amend the claim to “A non-transitory computer readable storage medium comprising a computer program…” to ensure that the claim is eligible under step 1.
The remaining limitations of Claim 26 recites substantially similar material to Claim 1, and the claim is ineligible under 35 U.S.C 101 for the same reasons. The additional elements “computer program,” “computer readable instructions,” and “computer apparatus” represent mere instructions to apply the abstract idea on a computer as in MPEP § 2106.05(f); thus, they do not provide a practical application or significantly more, and the claim is ineligible under 35 U.S.C 101.
The following is an analysis of independent Claim 27 based on the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
Step 1, Statutory Category:
No: Claim 27 is not directed to a patent eligible statutory category.
Claim 27 is directed to “A computer program carrier.” Under step 1 of the 35 U.S.C 101 analysis determining statutory category, the claim does not fall within at least one of the four categories of patent eligible subject matter, see MPEP § 2106.03. The claim is directed to a product lacking a physical or tangible structure in the form of an organizational structure, such as a computer program per se (often referred to as “software per se”). “Computer program carrier” could be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. Therefore, the claim is ineligible under 35 U.S.C 101. Applicant may amend the claim to “A non-transitory computer program carrier comprising a computer program…” to ensure that the claim is eligible under step 1.
The remaining limitations of Claim 27 recites substantially similar material to Claim 1, and the claim is ineligible under 35 U.S.C 101 for the same reasons. The additional elements “computer program carrier” and “computer program” represent mere instructions to apply the abstract idea on a computer as in MPEP § 2106.05(f); thus, they do not provide a practical application or significantly more, and the claim is ineligible under 35 U.S.C 101.
Regarding Claim 27, the claim recites substantially similar material to Claim 1, and the claim is ineligible under 35 U.S.C 101 for the same reasons. The additional elements “processing apparatus,” “processor,” and “computer program carrier” represent mere instructions to apply the abstract idea on a computer as in MPEP § 2106.05(f); thus, they do not provide a practical application or significantly more, and the claim is ineligible under 35 U.S.C 101.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Song et al. (U.S. Pub. No. 2019/0390542 A1), hereinafter Song, in view of Keskes (U.S. Pub. No. 2016/0291179 A1), hereinafter Keskes.
Regarding Claim 1, Song teaches A method for processing seismic data relating to a reservoir zone comprising a plurality of substantially vertical wells having well-defined trajectories and one or more non-vertical wells (“The present disclosure relates generally to hydrocarbon exploration and production, and particularly, to geosteering inversion for directional drilling of wellbores during downhole operations for hydrocarbon exploration and production.”) (e.g., paragraph [0001]).
b) selecting a non-vertical well of said one or more non-vertical wells, said selected non-vertical well having an associated logged trajectory (“In one or more embodiments, well planner 210 includes a data manager 212, an inversion modeler 214, and a well path controller 216. Data manager 212 may be used to obtain information relating to downhole operations being performed at a well site [...] Such information may include real-time measurements of formation properties collected by a downhole tool ( e.g., downhole tool 132A of FIG. 1A, as described above) as the well bore is drilled along the path.”) (e.g., paragraph [0041]).
d) comparing said inversion well data to observed well data relating to the selected non-vertical well (“In block 502, a plurality of initial models for the multi-layer DTBB inversion may be generated by randomly sampling different sets of formation parameters, as described above [...] In block 506, the predicted responses from each model are compared and refined with actual measurements of the formation properties as collected by the downhole tool during the downhole operation along the path of the wellbore through the formation.”) (e.g., paragraphs [0058] and [0061]).
e) determining, based on said comparison, one or more candidate corrected trajectories for the selected non-vertical well (“In block 508, at least one of the models may be selected as an inversion model, based on the comparison and at least one selection criterion, as described above [...] Process 400 then proceeds to block 412, which includes adjusting the path of the wellbore for performing the one or more subsequent stages of the downhole operation, based on results of the inversion using the selected inversion model.” Determining initial inversion models and selecting one of the models, on which an adjusted path is based, is analogous to determining one or more candidate corrected trajectories, wherein the adjusted path is a corrected trajectory.) (e.g., paragraphs [0061] and [0064]).
wherein the one or more candidate corrected trajectories are corrected with respect to the logged trajectory (“In block 506, the predicted responses from each model are compared and refined with actual measurements of the formation properties as collected by the downhole tool during the downhole operation along the path of the wellbore through the formation.” Refining a predicted response based on actual measurements is interpreted as correcting a trajectory with respect to a logged trajectory, wherein the actual measurements are a logged trajectory, and the adjusted path is based on the predicted response.) (e.g., paragraph [0061]).
and f) validating said one of said one or more candidate corrected trajectories to determine a validated corrected trajectory for the selected non-vertical well (“In step 324, the method 300 determines if the drilling operations have reached a reservoir and if the actual well trajectory is optimized.” Determining if the actual well trajectory is optimized is interpreted as validating a corrected trajectory).
However, Song does not appear to specifically teach the method comprising: a) obtaining a trained neural network, having been trained to infer well data from seismic data and having been trained on training well data relating only to wells having well-defined trajectories and c) using said trained neural network to invert seismic data relating to said reservoir zone, to obtain inversion well data.
On the other hand, Keskes, which relates similarly to oil field operations, does teach a) obtaining a trained neural network, having been trained to infer well data from seismic data (“FIG. 4a illustrates training of a neural network based on seismic signal data and filtered well data in one embodiment according to the invention.”) (e.g., paragraph [0101]).
and having been trained on training well data relating only to wells having well-defined trajectories (“It is thus possible to carry out training of the neural network 404 using, as input data, sub-portions of the pre-stack signal received at the wellhead 451t and associated with the known well data on the well 451.”) (e.g., paragraph [0114]).
c) using said trained neural network to invert seismic data relating to said reservoir zone, to obtain inversion well data (“As such, the neural network can return, as an output, well data ( or geological information) associated with the "cleaned" input signal.”) (e.g., paragraph [0134]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the Applicant's claimed invention to combine Song with Keskes. The claimed invention is considered to be merely combining prior art elements according to known methods to yield predictable results, see MPEP § 2143(I)(A). Song teaches a method for adjusting the path of a wellbore based on candidate inversion models. However, Song does not specifically teach wherein the inversion models are trained neural networks, training said neural networks, or validating said neural networks. On the other hand, Keskes, which relates similarly to wellbore operations, does teach a neural network for inverting seismic data, training said neural network, and validating said neural network. While Song does not specifically teach a neural network, Song does teach a system comprising an inversion modeler used to generate inversion models (e.g., paragraph [0041]); Keskes provides a specific implementation for inversion models using a neural network. In combination, the neural network training and neural network of Keskes merely perform the same functions as they do separately. As both Song and Keskes relate to hydrocarbon wellbore operations (e.g., Song, paragraph [0001]; Keskes, paragraph [0002]), one of ordinary skill in the art could have combined the elements as claimed according to known methods, and one of ordinary skill in the art would have recognized the results of the combination as predictable. Therefore, it would have been obvious to a person of ordinary skill in the art to combine the inversion model guided geosteering of Song with the neural network inversion models of Keskes.
Regarding Claim 2, Song in view of Keskes teaches A method as claimed in claim 1. Song further teaches wherein said observed well data comprises well data logged during drilling of the selected non-vertical well and said logged trajectory comprises the trajectory logged during said drilling (“Such information may include real-time measurements of formation properties collected by a downhole tool (e.g., downhole tool 132A of FIG. 1A, as described above) as the well bore is drilled along the path.” The real-time measurements are interpreted as well data and trajectory logged during drilling.) (e.g., paragraph [0041]).
Regarding Claim 3, Song in view of Keskes teaches A method as claimed in claim 2. Song further teaches wherein said validating step comprises: selecting a candidate corrected trajectory of said one or more candidate corrected trajectories (“In block 508, at least one of the models may be selected as an inversion model, based on the comparison and at least one selection criterion, as described above [...] Process 400 then proceeds to block 412, which includes adjusting the path of the wellbore for performing the one or more subsequent stages of the downhole operation, based on results of the inversion using the selected inversion model.” Determining initial inversion models and selecting one of the models, on which an adjusted path is based, is analogous to selecting one or more candidate corrected trajectories, wherein the adjusted path is a corrected trajectory.) (e.g., paragraphs [0061] and [0064]).
However, Song does not teach selecting a training subset of at least said plurality of substantially vertical wells, and a validation subset of at least said plurality of substantially vertical wells, the training subset being different to said validation subset; retraining the neural network using training data relating to said training subset and the non-vertical well having a trajectory defined by the selected candidate corrected trajectory; using the retrained neural network to predict well data relating to the validation subset; comparing the predicted well data to known well data for the validation subset.
On the other hand, Keskes further teaches selecting a training subset of at least said plurality of substantially vertical wells, and a validation subset of at least said plurality of substantially vertical wells, the training subset being different to said validation subset (“training can use previously determined sub-portions as input variables and the processed well data set as an output variable ( or target variable). Only a subset of these sub-portions ( e.g. 70%) can be used for training this neural network. The other sub-portions ( e.g. 30%) are then used as validation variables in order to quantify the precision and the error rate of the neural network.”) (e.g., paragraph [0130]).
retraining the neural network using training data relating to said training subset and the non-vertical well having a trajectory defined by the selected candidate corrected trajectory (“It is then possible to carry out training (step 608) of a blank or partially trained neural network.” Training a partially trained neural network is interpreted as retraining a neural network.) (e.g., paragraph [0130]).
using the retrained neural network to predict well data relating to the validation subset (“It is also possible to envisage a set of input and output values suitable for validating the neural network and/or computing the error of this network: this is referred to as the "validation set".”) (e.g., paragraph [0105]).
comparing the predicted well data to known well data for the validation subset (“The other sub-portions (e.g. 30%) are then used as validation variables in order to quantify the precision and the error rate of the neural network.”) (e.g., paragraph [0130]).
Regarding Claim 4, Song in view of Keskes teaches A method as claimed in claim 3. Song further teaches wherein the method comprises: performing said validating step for each of said one or more candidate corrected trajectories (“In block 506, the predicted responses from each model are compared and refined with actual measurements of the formation properties”) (e.g., paragraph [0061]).
and selecting, as said validated corrected trajectory, the candidate corrected trajectory for which the predicted well data is most similar to the known well data in said step of comparing the predicted well data to known well data for the validation subset (“In block 408, at least one of the plurality of initial models is selected as an inversion model, based on the comparison and at least one selection criterion. As described above, the selection criterion may be a misfit threshold, where only those initial models for which the misfit value is below the misfit threshold are selected.” Selecting a model which has the lowest misfit, on which an adjusted path is based, is interpreted as selecting a candidate trajectory for which predicted well data is most similar to well data for a validation subset.) (e.g., paragraph [0057]).
Regarding Claim 5, Song in view of Keskes teaches A method as claimed in claim 3. Keskes further teaches wherein the training subset and validation subset are non-overlapping (“training can use previously determined sub-portions as input variables and the processed well data set as an output variable ( or target variable). Only a subset of these sub-portions (e.g. 70%) can be used for training this neural network. The other sub-portions (e.g. 30%) are then used as validation variables in order to quantify the precision and the error rate of the neural network.”) (e.g., paragraph [0130]).
Regarding Claim 6, Song in view of Keskes teaches A method as claimed in claim 1. Song further teaches wherein said d) comprises determining as a candidate corrected trajectory, a trajectory which maximizes correlation of said inversion well data to observed well data (“In block 408, at least one of the plurality of initial models is selected as an inversion model, based on the comparison and at least one selection criterion. As described above, the selection criterion may be a misfit threshold, where only those initial models for which the misfit value is below the misfit threshold are selected […] Process 400 then proceeds to block 412, which includes adjusting the path of the wellbore for performing the one or more subsequent stages of the downhole operation, based on results of the inversion using the selected inversion model.”) (e.g., paragraphs [0057] and [0064]).
Regarding Claim 7, Song in view of Keskes teaches A method as claimed in claim 1. Song further teaches wherein steps d) and e) are performed per trajectory portion of the selected non- vertical well (“The selected models may be further refined with the inversion performed at each stage of the operation so as to better approximate the formation properties for additional layers of the formation.”) (e.g., paragraph [0022]).
Regarding Claim 8, Song in view of Keskes teaches A method as claimed in claim 7. Song further teaches wherein each of said portions comprises a respective region of uncertainty and each candidate corrected trajectory is determined within the respective region of uncertainty for each of said portions (“Returning to process 400 of FIG. 4, the selected inversion model(s) may be used in block 410 to perform inversion for one or more subsequent stages of the downhole operation along the path of the wellbore [...] In block 414, the one or more subsequent stages of the downhole operation may be performed based on the adjusted path of the well bore through the subsurface formation.” Each state of the downhole operation is interpreted as a region of uncertainty, wherein an adjusted path may be determined for each stage.) (e.g., paragraphs [0063] and [0064]).
Regarding Claim 9, Song in view of Keskes teaches A method as claimed in claim 7. Song further teaches wherein step c) is performed individually per portion, treating each portion as a vertical well (“Each operating interval may be, for example, a different range of depth or time over which a portion of the wellbore is drilled along the planned path.”) (e.g., paragraph [0018]).
Regarding Claim 10, Song in view of Keskes teaches A method as claimed in claim 1. Song further teaches wherein step e) is performed subject to one or more constraints, such that said one or more candidate corrected trajectories respect said constraints (“The selection criterion may be, for example, a misfit threshold used to select only those initial models that produce a predicted response that matches or fits the actual response within a given error tolerance. Thus, any initial models 224 having a misfit at or above a certain misfit threshold may be disqualified and removed from the set of models selected for performing inversion.” The selection criterion are interpreted as constraints.) (e.g., paragraph [0044]).
Regarding Claim 11, Song in view of Keskes teaches A method as claimed in claim 10. Song further teaches wherein said one or more constraints comprise one or more drilling constraints based on drilling physics (“In addition to the misfit threshold, other selection criteria may be used to qualify the set of initial models that are selected as inversion models for performing DTBB inversion during the downhole operation. For example, such another selection criterion may be a particular formation parameter of interest, e.g., resistivity or resistivity contrast.” The parameters of interest are interpreted as drilling constraints based on drilling physics.) (e.g., paragraph [0045]).
Regarding Claim 12, Song in view of Keskes teaches A method as claimed in claim 10. Song further teaches wherein said one or more constraints define a maximum curvature and/or deviation angle along the trajectory (“The selection criterion may be, for example, a misfit threshold used to select only those initial models that produce a predicted response that matches or fits the actual response within a given error tolerance. Thus, any initial models 224 having a misfit at or above a certain misfit threshold may be disqualified and removed from the set of models selected for performing inversion.” The response error tolerance is interpreted as comprising a deviation angle along a trajectory.) (e.g., paragraph [0044]).
Regarding Claim 13, Song in view of Keskes teaches A method as claimed in claim 1. Song further teaches wherein said training well data in a first training of said neural network relates only to said plurality of substantially vertical wells (“Further, even though a figure may depict a vertical wellbore, unless indicated otherwise, it should be understood by one of ordinary skill in the art that the apparatus according to the present disclosure is equally well suited for use in wellbores having other orientations including horizontal wellbores, deviated or slanted wellbores, multilateral well bores or the like.” The methods disclosed in Song and Keskes would have been recognized by one of ordinary skill in the art as suitable for predicting responses of vertical wells.) (e.g., paragraph [0017]).
Regarding Claim 14, Song in view of Keskes teaches A method as claimed in claim 1. Song further teaches the method further comprising performing steps b) to f) iteratively for each of said non-vertical wells (“The selected models may be further refined with the inversion performed at each stage of the operation so as to better approximate the formation properties for additional layers of the formation.” Further refining is interpreted as iteratively selecting a well and comparing predicted and observed inversion data, wherein the different stages may correspond to different wells.) (e.g., paragraph [022]).
Regarding Claim 15, Song in view of Keskes teaches A method as claimed in claim 14. Song further teaches wherein, for each iteration, the neural network is retrained with training data comprising data relating to the validated non-vertical well as validated in that iteration (“In block 506, the predicted responses from each model are compared and refined with actual measurements of the formation properties as collected by the downhole tool during the downhole operation along the path of the wellbore through the formation.” Refining the predicted responses may comprise the training of Keskes, wherein the refining is performed using the actual measurements, further wherein the actual measurements are interpreted as data relating to validated trajectories.) (e.g., paragraph [0061]).
Regarding Claim 16, Song in view of Keskes teaches A method as claimed in claim 15. Keskes further teaches the method comprising using the neural network trained at the final iteration to perform an inversion on further seismic data relating to said reservoir zone to obtain further well data relating to said reservoir zone (“It is then possible to carry out training (step 608) of a blank or partially trained neural network. This training can use previously determined sub-portions as input variables and the processed well data set as an output variable (or target variable) [...] As such, the neural network can return, as an output, well data (or geological information) associated with the "cleaned" input signal.”) (e.g., paragraphs [0131] and [0134]).
Regarding Claim 17, Song in view of Keskes teaches A method as claimed in claim 14. Keskes further teaches the method comprising a final step of retraining the neural network on all validated non-vertical wells (“The other sub-portions (e.g. 30%) are then used as validation variables in order to quantify the precision and the error rate of the neural network.”) (e.g., paragraph [0130]).
and using the retrained neural network to perform an inversion on further seismic data relating to said reservoir zone to obtain further well data relating to said reservoir zone (“As such, the neural network can return, as an output, well data (or geological information) associated with the "cleaned" input signal.”) (e.g., paragraph [0134]).
Regarding Claim 18, Song in view of Keskes teaches A method as claimed in claim 16. Song further teaches the method comprising optimizing a production strategy to produce hydrocarbon from said reservoir zone based on said further well data (“As downhole operating conditions may continually change over the course of the operation, the operator may use the interface provided by computer 144 to react to such changes in real time by adjusting selected drilling parameters in order to increase and/or maintain drilling efficiency and thereby, optimize the drilling operation.”) (e.g., paragraph [0035]).
Regarding Claim 19, Song in view of Keskes teaches A method as claimed in claim 1. Keskes further teaches wherein said seismic data comprises high frequency content up to 3 times the frequency of a seismic wavelet emitted in a subsoil to obtain said seismic data (“Furthermore, the neural network accounts for the entire signal received, including the "high-frequency" information previously considered as noise to be removed from the computations.”) (e.g., paragraph [0110]).
Regarding Claim 20, Song in view of Keskes teaches A method as claimed in claim 1. Keskes further teaches wherein said seismic data comprises pre- stack seismic data (“There are numerous methods for determining, on the basis of the geophone records, the trajectory of the wavelets (e.g. seismic migration). These methods generally supply seismic images based on "pre-stack" signals or on "stack" signals.”) (e.g., paragraph [0073]).
Regarding Claim 21, Song in view of Keskes teaches A method as claimed in claim 1. Keskes further teaches the method comprising an initial step of training said neural network based on training data relating to only said substantially vertical wells to obtain said trained neural network (“For example, the set of figures described may appear to indicate that the drill holes are vertical ( or at least linear).”) (e.g., paragraph [0147]).
Regarding Claim 22, Song in view of Keskes teaches A method as claimed in claim 21. Keskes further teaches wherein said initial step and any other training or retraining step comprises: receiving said seismic data comprising at least one seismic signal derived from the emission of a seismic wavelet in a subsoil (“receiving at least one second seismic signal derived from the emission of a seismic wavelet in a subsoil;”) (e.g., Claim 1).
identifying at least one portion of said at least one seismic signal corresponding to reflections of the seismic wavelet in the reservoir zone (“identifying at least one portion of said at least one second seismic signal corresponding to reflections of the seismic wavelet in a reservoir one of said subsoil;”) (e.g., Claim 1).
determining a length of the seismic wavelet (“determining it length of the seismic wavelet;”) (e.g., Claim 1).
receiving well data corresponding to said identified reservoir zone (“receiving well data corresponding to said identified reservoir zone;”) (e.g., Claim 1).
training the neural network using: a plurality of sub-portions of said at least one portion as input variables, said sub- portions of the portion having a length dependent on the length of the seismic wavelet determined, and at least one piece of well data, or geological information corresponding to said well data, as a target variable (“training a neural network using: a plurality of sub-portions of said at least one portion as input variables, said sub-portions of the portion having a length dependent on the length of the seismic wavelet determined, and at least one second piece of geological information according to said well data as the target variable;”) (e.g., Claim 1).
Regarding Claim 23, Song in view of Keskes teaches A method as claimed in claim 22. Keskes further teaches wherein the at least one seismic signal comprises a plurality of pre-stack seismic signals (“There are numerous methods for determining, on the basis of the geophone records, the trajectory of the wavelets (e.g. seismic migration). These methods generally supply seismic images based on "pre-stack" signals or on "stack" signals.”) (e.g., paragraph [0073]).
Regarding Claim 24, Song in view of Keskes teaches A method as claimed in claim 22. Keskes further teaches wherein the wavelet length is determined according to an autocorrelation calculation of said at least one portion (“For each portion of signals previously identified, it is also possible to compute an autocorrelation (step 603) of this portion so as to estimate the length of the seismic wavelet.”) (e.g., paragraph [0125]).
Regarding Claim 25, Song in view of Keskes teaches A method as claimed in claim 2. Keskes further teaches wherein the length of the sub-portions is between 1 and 2 times the length of the seismic wavelet determined (“The method according to claim 1, wherein the length of the sub-portions is between 0.5 and two times the length of the seismic wavelet determined.”) (e.g., Claim 5).
Regarding Claim 26, Song teaches A computer program comprising computer readable instructions which, when run on suitable computer apparatus (“These functions described above can be implemented in digital electronic circuitry, in computer software, firmware or hardware. The techniques can be implemented using one or more computer program products.”) (e.g., paragraph [0072]).
The remaining limitations of Claim 26 recite substantially similar limitations to Claim 1, and the claim is rejected under 35 U.S.C 103 for the same reasons.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the Applicant's claimed invention to combine Song with Keskes for the same reasons as in Claim 1.
Regarding Claim 27, Song teaches A computer program carrier (“These functions described above can be implemented in digital electronic circuitry, in computer software, firmware or hardware. The techniques can be implemented using one or more computer program products.”) (e.g., paragraph [0072]).
The remaining limitations of Claim 27 recite substantially similar limitations to Claim 1, and the claim is rejected under 35 U.S.C 103 for the same reasons.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the Applicant's claimed invention to combine Song with Keskes for the same reasons as in Claim 1.
Regarding Claim 28, Song teaches A processing apparatus comprising: a processor (“These functions described above can be implemented in digital electronic circuitry, in computer software, firmware or hardware. The techniques can be implemented using one or more computer program products.”) (e.g., paragraph [0072]).
The remaining limitations of Claim 28 recite substantially similar limitations to Claim 1, and the claim is rejected under 35 U.S.C 103 for the same reasons.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the Applicant's claimed invention to combine Song with Keskes for the same reasons as in Claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Das et al. (Das, Vishal, Ahinoam Pollack, Uri Wollner, and Tapan Mukerji. "Convolutional neural network for seismic impedance inversion." Geophysics 84, no. 6 (2019): R869-R880.) teaches a convolutional neural network for seismic inversion.
Jouini et al. (Jouini, Mohamed Soufiane, and Noomane Keskes. "Numerical estimation of rock properties and textural facies classification of core samples using X-Ray Computed Tomography images." Applied Mathematical Modelling 41 (2017): 562-581.) teaches a method for classifying X-Ray computed Tomography images using a neural network.
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/K.H.T./ Examiner, Art Unit 2189
/REHANA PERVEEN/ Supervisory Patent Examiner, Art Unit 2189
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