DETAILED ACTION
This Office Action is in response to the remarks submitted on 02/13/2026. Claims 1, 4, 7, 12, 15, 17 are amended. Claims 1-17 are pending.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/10/2026 has been entered.
Response to Arguments
The Applicant’s arguments regarding the rejection of above claims have been fully considered.
In reference to Applicant’s arguments about:
Drawing objections.
Examiner’s response:
Objections to Drawings are withdrawn in view of Replacement Sheet submitted on 02/13/2026.
In reference to Applicant’s arguments about:
35 USC 101 rejections.
Examiner’s response:
Applicant’s arguments regarding the 101 rejections for claims 1-11, 13-14 and 16, as being directed to a judicial exception without significantly more, are fully considered but are not persuasive. Applicant asserts that the claims (specifically independent claims 1, 4 and 7) recite additional elements directed to an improvement in the training of computer models for predicting pressure, however, Examiner respectfully disagrees. The broadest reasonable interpretation (BRI) of the claim limitations pursuant to the amended claim language is as follows: a model is trained on DAS and DTS data across different depths, then the model is used to further calculate or predict pressure along optical fiber cables using new data acquired. Following the guidelines for determining if a limitation amounts to a judicial exception, Examiner understands that predicting or calculating pressure in the cables based on the data acquired amounts to a mathematical calculation being performed according to the mathematical relationships of the data being acquired. The use of a computer model using a machine learning prediction algorithm is interpreted as mere instructions to implement an abstract idea (the mathematical calculation of the pressure) on a computer under MPEP 2106.05(f).
In addition, independent claims merely recite this computer model being trained with the acquired data (historical data) to further calculate pressures based on new acquired data; however, this training is being recited at a high level of generality. No specific steps are included in these independent claims on how the training is being achieved to be considered as an improvement to the technology. Examiner respectfully would like to point out that dependent claims 12, 15 and 17 have not being rejected under 35 USC 101, as they were considered to include a particular and specific arrangement of the model as being part of an ensemble of a plurality of predictive models being executed in parallel and whose outputs are combined and averaged to form a model prediction of the pressure. This specific arrangement provides an improvement as it delivers low variance and better accuracy, therefore, Examiner suggests Applicant to include these limitations into independent claims to withdraw the 35 USC 101 rejections.
For these reasons, rejections are still maintained.
In reference to Applicant’s arguments about:
35 USC 103 Rejections.
Examiner’s response:
A. Claim 1; and B. Claims 4 and 7:
Applicant’s arguments have been fully considered but are not persuasive. Applicant’s main argument is directed to independent claims 1, 4 and 7, specifically stating that the prior art Cerrahoglu uses a first measure to train one model and a second measure to train another different model, however, Examiner respectfully disagrees. As it can be seen at the mapping of this prior art in the rejection, Cerrahoglu teaches at [0174]: “The local or reference DTS data (e.g., first set of measurements) can be utilized as detailed hereinabove along with the DAS measurements (e.g., the second set of data) to train one or more event models”. Therefore, based on the broadest reasonable interpretation, both measurements are used to train the same model, or models, in order to predict data in the same or another well.
Claims 2-3, 5-6, and 8-9 Are Patentable over Cerrahoglu and Jin
Applicant’s arguments have been fully considered but are not persuasive. As explained above, Examiner understands that the prediction of the pressure using a computer model is taught by Cerrahoglu, therefore, Jin is only brought to cure the specific deficiencies of these dependent claims only, being the low-frequency data. Jin explains that the use of low frequency data, such as frequency data in the band of <1 Hz, preferably <0.1 Hz, or even <0.05 Hz, contain information that can provide critical information on cross well fluid communication. Therefore, in view of this critical information, a person having ordinary skill in the art would be motivated to use this critical information to train the computer models to optimize the prediction.
Claims 11, 14 and 16 are patentable over Cerrahoglu and Madasu
Applicant’s arguments have been fully considered but are not persuasive. Examiners noticed that part of these limitations are now included at independent claims, in particular, the use of data across different depths. Examiner understands that the prediction of the pressure using a computer model is taught by Cerrahoglu, therefore, Madasu is only brought to cure the specific deficiencies of these dependent claims only, being the use of data across different depths. Madasu explains the treatment of wellbores using data from different portions, including different depths. Therefore, in view of this useful information of using data across different portions, a person having ordinary skill in the art would be motivated to characterize the stimulation treatment operation along different portions of the wellbore.
Claims 12, 15 and 17 are patentable over Cerrahoglu in view of Hearty.
In regard to the dependent claims 12, 15 and 17; using the art Hearty, examiner would like to point out that this additional prior art is only brought to cure the specific deficiencies of these dependent claims only.
Claims 10 and 13 are patentable over Cerrahoglu in view of Jaaskelainen.
In regard to the dependent claims 10 and 13; using the art Jaaskelainen, examiner would like to point out that this additional prior art is only brought to cure the specific deficiencies of these dependent claims only.
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 7-9, 16-17 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.
Independent Claim 7 recites in the last limitation:
“instructions for processing DAS and DTS data acquired after the model has been trained as inputs to the trained computer model and predicting pressure distributed along said one or more optical fiber cables by processing the acquired post-model training DAS data and DTS as inputs to the trained computer model”.
It seems the language at the beginning of this limitation is repeated at the end of the limitation, as it recites the same action; therefore, this lack of clarity renders the claim indefinite. For purposes of examination, Examiner will interpret this limitation as “instructions for processing DAS and DTS data acquired after the model has been trained as inputs to the trained computer model and predicting pressure distributed along said one or more optical fiber cables Clarification is required.
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 1-11, 13-14, 16 stand rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 analysis:
In the instant case, claims 1-3, 10-11 are directed to a system, claims 4-6, 13-14 are directed to a method, and 7-9, 16 to a non transitory computer readable medium. Thus, these claims fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Step 2A analysis:
Based on the claims being determined to be within the four categories and/or an amended version of it (Step 1), it must be determined if the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), in this case the claims fall within the judicial exception of an abstract idea. Specifically, the abstract ideas of Mathematical Concepts (including mathematical relationships, formulas, and/or calculations).
Step 2A: Prong 1 analysis:
The independent claims 1, 4 and 7 (as being analogous in scope) recite:
(Claim 1):
“predict pressure distributed along said one or more optical fiber cables at the different depths based at least in part on acquired post-model-training DAS and DTS data” – based on the broadest reasonable interpretation in light of the specification, this limitation corresponds to calculating pressure in the cables based on the data acquired, which amounts to a mathematical calculation based on mathematical algorithms, being abstract ideas.
(Claim 4):
“predicting pressure distributed along said one or more optical fiber cables based at least in part on acquired post-model-training DAS and DTS data” – based on the broadest reasonable interpretation in light of the specification, this limitation corresponds to calculating pressure in the cables based on the data acquired, which amounts to a mathematical calculation based on mathematical algorithms, being abstract ideas.
(Claim 7):
“predicting pressure distributed along said one or more optical fiber cables”– based on the broadest reasonable interpretation in light of the specification, this limitation corresponds to calculating pressure in the cables based on the data acquired, which amounts to a mathematical calculation based on mathematical algorithms, being abstract ideas.
Step 2A: Prong 2 analysis:
This judicial exception is not integrated into a practical application because it only recites these additional elements:
(Claim 1):
“a processor configured to perform a ML pressure prediction algorithm, the ML pressure prediction algorithm performing a process comprising” - this limitation recites a processor at a high level of generality, interpreted as generic computer components merely used as a tool to perform a mathematical algorithm to calculate pressure, which amounts to mere instructions to apply an exception under MPEP 2106.05(f);
“acquiring DAS data and DTS data from optical signals carried on one or more optical fiber cables across different depths”- this limitation amounts to acquiring data, which is mere data gathering, considered a pre-solution activity (data gathering) which is an insignificant extra solution activity (see 2106.05(g) (3));
“training a computer model to predict pressure at the different depths based on the acquired DAS and DTS data being supplied as inputs to the computer model and the computer model using machine learning to derive a relationship between the pressure and the acquired DAS and DTS data”- this limitation recites training a model at a high level of generality for the purposes of calculating the pressures, therefore this is interpreted as mere instructions to implement an abstract idea on a computer, under MPEP 2106.05(f));
“after the computer model has been trained, acquiring post-model-training DAS data and DTS data from optical signals carried on said one or more optical fiber cables”- this limitation amounts to mere data gathering, considered a pre-solution activity (data gathering) which is an insignificant extra solution activity (see 2106.05(g) (3));
“processing the acquired post-model-training DAS and DTS data as inputs to the trained computer model…”- this limitation amounts to mere data gathering/ input to the model, being considered a pre-solution activity (data gathering) which is an insignificant extra solution activity (see 2106.05(g) (3)). Further, using the data as inputs to the model is interpreted as mere instructions to implement an abstract idea on a computer under MPEP 2106.05(f));
“a memory device in communication with the processor” - this limitation recites generic computer components (see MPEP 2106.05(b)).
(Claim 4):
“acquiring DAS data and DTS data from optical signals carried on one or more optical fiber cables across different depths”- this limitation amounts to acquiring data, which is mere data gathering, considered a pre-solution activity (data gathering) which is an insignificant extra solution activity (see 2106.05(g) (3));
“training a computer model to predict pressure at the different depths based on the acquired DAS and DTS data being supplied as inputs to the computer model and the computer model using machine learning to derive a relationship between the pressure and the acquired DAS and DTS data”- this limitation recites training a model at a high level of generality for the purposes of calculating the pressures, therefore this is interpreted as mere instructions to implement an abstract idea on a computer, under MPEP 2106.05(f));
“after the computer model has been trained, acquiring post-model-training DAS data and DTS data from optical signals carried on said one or more optical fiber cables”- this limitation amounts to mere data gathering, considered a pre-solution activity (data gathering) which is an insignificant extra solution activity (see 2106.05(g) (3));
“processing the acquired post-model-training DAS and DTS data as inputs to the trained computer model…”- this limitation amounts to mere data gathering/ input to the model, being considered a pre-solution activity (data gathering) which is an insignificant extra solution activity (see 2106.05(g) (3)). Further, using the data as inputs to the model is interpreted as mere instructions to implement an abstract idea on a computer under MPEP 2106.05(f)).
(Claim 7):
“one or more processors for predicting distributed pressure based at least in part on distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) data” - this limitation recites a processor at a high level of generality, interpreted as generic computer components merely used as a tool to perform an existing process (mere instruction to apply an exception under MPEP 2106.05(f));
“instructions for DAS data and DTS data from optical signals carried on one or more optical fiber cables across different depths”- this limitation amounts to mere data gathering, considered a pre-solution activity (data gathering) which is an insignificant extra solution activity (see 2106.05(g) (3));
“instructions for training a computer model to predict pressure at the different depths based on the acquired DAS and DTS data being supplied as inputs to the computer model and the computer model using machine learning to derive a relationship between the pressure and the acquired DAS and DTS data”- this limitation recites training a model at a high level of generality for the purposes of calculating the pressures, therefore this is interpreted as mere instructions to implement an abstract idea on a computer, under MPEP 2106.05(f));
“instructions for acquiring post-model-training DAS data and DTS data from optical signals carried on said one or more optical fiber cables after the computer model has been trained”- this limitation amounts to mere data gathering, considered a pre-solution activity (data gathering) which is an insignificant extra solution activity (see 2106.05(g) (3));
“instructions for processing DAS and DTS data acquired after the model has been trained as inputs to the trained computer model…by processing the acquired post-model training DAS data and DTS as inputs to the trained model”- this limitation amounts to mere data gathering/ input to the model, being considered a pre-solution activity (data gathering) which is an insignificant extra solution activity (see 2106.05(g) (3)). Further, using the data as inputs to the model is interpreted as mere instructions to implement an abstract idea on a computer under MPEP 2106.05(f)).
Step 2B analysis:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements recited at Claims 1, 4 and 7 above amount to no more than insignificant extra solution activities, and mere instructions to apply an exception and generic computer components.
Moreover, re-evaluation of any additional elements or combination of elements that were insignificant extra-solution activities at Claims 1, 4 and 7 are needed to determine if they are further considered well-understood, routine and conventional limitations:
(Claim 1):
“a processor configured to perform a ML pressure prediction algorithm, the ML pressure prediction algorithm performing a process comprising” - this limitation recites a processor at a high level of generality, interpreted as generic computer components merely used as a tool to perform a mathematical algorithm to calculate pressure, which amounts to mere instructions to apply an exception under MPEP 2106.05(f);
“acquiring DAS data and DTS data from optical signals carried on one or more optical fiber cables across different depths”- this limitation amounts to receiving data over a network, which is well understood, routine and conventional under MPEP 2106.05 d II(i): “Receiving or transmitting data over a network, e.g., using the Internet to gather data”;
“training a computer model to predict pressure at the different depths based on the acquired DAS and DTS data being supplied as inputs to the computer model and the computer model using machine learning to derive a relationship between the pressure and the acquired DAS and DTS data”- this limitation recites training a model at a high level of generality for the purposes of calculating the pressures, therefore this is interpreted as mere instructions to implement an abstract idea on a computer, under MPEP 2106.05(f));
“after the computer model has been trained, acquiring post-model-training DAS data and DTS data from optical signals carried on said one or more optical fiber cables”- this limitation amounts to receiving data over a network, which is well understood, routine and conventional under MPEP 2106.05 d II(i): “Receiving or transmitting data over a network, e.g., using the Internet to gather data”;
“processing the acquired post-model-training DAS and DTS data as inputs to the trained computer model…”- this limitation amounts to receiving data over a network, which is well understood, routine and conventional under MPEP 2106.05 d II(i): “Receiving or transmitting data over a network, e.g., using the Internet to gather data”. Further, using the data as inputs to the model is interpreted as mere instructions to implement an abstract idea on a computer under MPEP 2106.05(f));
“a memory device in communication with the processor” - this limitation recites generic computer components (see MPEP 2106.05(b)).
(Claim 4):
“acquiring DAS data and DTS data from optical signals carried on one or more optical fiber cables across different depths”- this limitation amounts to receiving data over a network, which is well understood, routine and conventional under MPEP 2106.05 d II(i): “Receiving or transmitting data over a network, e.g., using the Internet to gather data”;
“training a computer model to predict pressure at the different depths based on the acquired DAS and DTS data being supplied as inputs to the computer model and the computer model using machine learning to derive a relationship between the pressure and the acquired DAS and DTS data”- this limitation recites training a model at a high level of generality for the purposes of calculating the pressures, therefore this is interpreted as mere instructions to implement an abstract idea on a computer, under MPEP 2106.05(f));
“after the computer model has been trained, acquiring post-model-training DAS data and DTS data from optical signals carried on said one or more optical fiber cables”- this limitation amounts to receiving data over a network, which is well understood, routine and conventional under MPEP 2106.05 d II(i): “Receiving or transmitting data over a network, e.g., using the Internet to gather data”;
“processing the acquired post-model-training DAS and DTS data as inputs to the trained computer model…”- this limitation amounts to receiving data over a network, which is well understood, routine and conventional under MPEP 2106.05 d II(i): “Receiving or transmitting data over a network, e.g., using the Internet to gather data”. Further, using the data as inputs to the model is interpreted as mere instructions to implement an abstract idea on a computer under MPEP 2106.05(f)).
(Claim 7):
“one or more processors for predicting distributed pressure based at least in part on distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) data” - this limitation recites a processor at a high level of generality, interpreted as generic computer components merely used as a tool to perform an existing process (mere instruction to apply an exception under MPEP 2106.05(f));
“instructions for DAS data and DTS data from optical signals carried on one or more optical fiber cables across different depths”- this limitation amounts to receiving data over a network, which is well understood, routine and conventional under MPEP 2106.05 d II(i): “Receiving or transmitting data over a network, e.g., using the Internet to gather data”;
“instructions for training a computer model to predict pressure at the different depths based on the acquired DAS and DTS data being supplied as inputs to the computer model and the computer model using machine learning to derive a relationship between the pressure and the acquired DAS and DTS data”- this limitation recites training a model at a high level of generality for the purposes of calculating the pressures, therefore this is interpreted as mere instructions to implement an abstract idea on a computer, under MPEP 2106.05(f));
“instructions for acquiring post-model-training DAS data and DTS data from optical signals carried on said one or more optical fiber cables after the computer model has been trained”- this limitation amounts to receiving data over a network, which is well understood, routine and conventional under MPEP 2106.05 d II(i): “Receiving or transmitting data over a network, e.g., using the Internet to gather data”;
“instructions for processing DAS and DTS data acquired after the model has been trained as inputs to the trained computer model…by processing the acquired post-model training DAS data and DTS as inputs to the trained model”- this limitation amounts to receiving data over a network, which is well understood, routine and conventional under MPEP 2106.05 d II(i): “Receiving or transmitting data over a network, e.g., using the Internet to gather data”. Further, using the data as inputs to the model is interpreted as mere instructions to implement an abstract idea on a computer under MPEP 2106.05(f)).
Dependent claims 2-3, 5-6, 8-11, 13-14, 16 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The claims recite further embellishment of the judicial exception.
Claim 2: this claim recites further embellishment about the data acquired being low-frequency distributed acoustic sensing data, which merely indicates a field of use or technological environment in which to apply a judicial exception, in this case to the technology of distributed pressure measurement at well-scale conditions. Claims 5 and 8 are analogous claim and therefore rejected in the same rationale.
Claim 3: this claim recites further embellishment about the low-frequency distributed acoustic sensing data, which merely indicates a field of use or technological environment in which to apply a judicial exception, in this case to the technology of distributed pressure measurement at well-scale conditions. Claims 6 and 9 are analogous claims and therefore rejected in the same rationale.
Claim 10: this claim recites further embellishment about the data acquired being produced by backscattered light signals, which merely indicates a field of use or technological environment in which to apply a judicial exception, in this case to the technology of distributed pressure measurement at well-scale conditions. Claim 13 is analogous and therefore rejected in the same rationale.
Claim 11: this claim recites the analysis and/or evaluation for comparing a relationship between data and pressure values, which amounts to observing the data, evaluating it and judging it; being mental processes and abstract ideas. The use of the trained model for the data correlation/characterization is interpreted as mere instructions to implement an abstract idea on a computer under MPEP 2106.05(f)). Claims 14 and 16 are analogous claims and therefore rejected in the same rationale.
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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 4, 7, 11, 14 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Cerrahoglu et al (WO 2021/254633- hereinafter Cerrahoglu) in view of Madasu et al (US Pub. No. 2019/0345803 - hereinafter Madasu).
Referring to Claim 1, Cerrahoglu teaches a machine learning (ML) system for predicting distributed pressure based at least in part on distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) data (see Cerrahoglu at [0041]: “Fiber optic distributed temperature sensors (DTS) and fiber optic distributed acoustic sensors (DAS) can capture distributed temperature sensing and acoustic signals, respectively, resulting from downhole events, such as wellbore events (e.g., gas flow, hydrocarbon liquid flow, water flow, mixed flow, leaks, overburden movement, and the like), as well as other background events” and “In some embodiments, temperature features and acoustic features can each be used with a model (e.g., a machine learning model such as a multivariate model, neural network, etc.) to provide for detection, identification, and/or determination of the extents of various events”), the ML system comprising:
a processor configured to perform a ML pressure prediction algorithm, the ML pressure prediction algorithm performing a process (see Cerrahoglu at [0175]: “The computer system 680 includes a processor 682”) comprising:
acquiring DAS data and DTS data from optical signals carried on one or more optical fiber cables across different depths (see Cerrahoglu at [0174]: “By way of example, in aspects, the first set of measurements comprises DTS data and the second set of measurements comprises DAS data. The local or reference DTS data (e.g., first set of measurements) can be utilized as detailed hereinabove along with the DAS measurements (e.g., the second set of data) to train one or more event models”. Therefore, Cerrahoglu teaches the utilization of DAS and DTS data to train one or more event models, therefore it is reasonably interpreted that the DAS and DTS data are acquired. However, Cerrahoglu fails to teach specifically DAS and DTS data across different depths);
training a computer model to predict pressure at the different depths based on the acquired DAS and DTS data being supplied as inputs to the computer model and the computer model using machine learning to derive a relationship between the pressure and the acquired DAS and DTS data (see Cerrahoglu at [0174]: “By way of example, in aspects, the first set of measurements comprises DTS data and the second set of measurements comprises DAS data. The local or reference DTS data ( e.g., first set of measurements) can be utilized as detailed hereinabove along with the DAS measurements (e.g., the second set of data) to train one or more event models. Once trained, the one or more trained event models can subsequently be utilized in the same or another well to predict and/or validate data. For example, in aspects, the DAS/DTS data are utilized as detailed herein to generate synthetic thermal profiles ( e.g., predicted DTS data in another well bore) and/or synthetic pressure data”. Therefore, Cerrahoglu teaches the utilization of DAS and DTS data to train one or more event models which are further used as an example for predicting pressure data, therefore, this is reasonably interpreted as analogous to the claimed limitation. However, Cerrahoglu fails to teach predicting pressure at different depths);
after the computer model has been trained, acquiring post-model-training DAS data and DTS data from optical signals carried on said one or more optical fiber cables (see Cerrahoglu at [0174]: “Once trained, the one or more trained event models can subsequently be utilized in the same or another well to predict and/or validate data. For example, in aspects, the DAS/DTS data are utilized as detailed herein to generate synthetic thermal profiles ( e.g., predicted DTS data in another well bore) and/or synthetic pressure data (e.g., DPS) data in the same or another well. The synthetic or predicted data can be utilized to cross check data obtained via another means or sensor”. Previously, at [0074]: “In some embodiments, a plurality of fibers 162 are present within the wellbore, and the DAS system can be coupled to a first optical fiber and the DTS system can be coupled to a second, different, optical fiber”. Therefore, it can be seen that Cerrahoglu teaches that the DAS and DTS are coupled to optical fiber cables; and later on Cerrahoglu teaches using the trained event models to predict pressure data and to further cross check data obtained from another sensor, therefore, this is reasonably interpreted as analogous to the claimed limitation); and
processing the acquired post-model-training DAS and DTS data as inputs to the trained computer model; and predicting pressure distributed along said one or more optical fiber cables at the different depths based at least in part on acquired post-model-training DAS and DTS data (see Cerrahoglu at [0174]: “For example, in aspects, the DAS/DTS data are utilized as detailed herein to generate synthetic thermal profiles ( e.g., predicted DTS data in another well bore) and/or synthetic pressure data (e.g., DPS) data in the same or another well” and “Alternatively or additionally, DPS data predicted from the trained event models in combination with the rock properties can be utilized to cross check pressure measurements from one or more in situ pressure sensors. One of skill in the art and with the help of this disclosure will understand that the herein disclosed system and method can be utilized to predict a variety of wellbore sensor data, which can be utilized in many ways to enhance wellbore management, planning, and production”. Therefore, since Cerrahoglu teaches the prediction of pressure data based on DAS/DTS data used for training the models, and Cerrahoglu even discloses a motivation rationale for utilizing this technology for the purposes of enhancing wellbore management, planning and production; Examiner interprets these teachings as being analogous to the claimed limitation. However, Cerrahoglu fails to teach predicting pressure at different depths); and
a memory device in communication with the processor (see Cerrahoglu at [0175]: “a processor 682 (which may be referred to as a central processor unit or CPU) that is in communication with memory devices including secondary storage 684”).
However, Cerrahoglu fails to teach specifically DAS and DTS data across different depths.
Madasu teaches, in an analogous system, DAS and DTS data across different depths (see Madasu at [0036]: “the inputs of the fully-coupled diversion model may include one or more wellbore treatment inputs and one or more formation inputs. The wellbore treatment inputs may be used to characterize the stimulation treatment operation along different portions of the wellbore 202 within the formation 204. In addition to the aforementioned flow rate and pressure at the inlet 250 of the wellbore 202, other wellbore treatment inputs may include, but are not limited to, an amount of diverter pumped into the wellbore 202 (e.g., according to the baseline diverter pumping schedule), the wellbore pressure at the injection points 210 and 220, a wellbore depth”… “In one or more embodiments, one or more of the wellbore treatment inputs of the fully-coupled diversion model in this example may be determined using various wellbore models along with real-time DAS and DTS measurements”. Therefore, since Madasu recites the relationship between DAS, DTS and pressure measurements at different portions of the wellbore, this is interpreted as different depths of the wellbore).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Cerrahoglu with the above teachings of Madasu by using DAS and DTS data to train models to predict pressure data, as taught by Cerrahoglu, wherein the relationship between DAS, DTS and pressure is characterized at different depths, as taught by Madasu. The modification would have been obvious because one of ordinary skill in the art would be motivated to characterize the stimulation treatment operation along different portions of the wellbore (as suggested by Madasu at [0036]).
Referring to independent Claim 4 and Claim 7, they are rejected on the same basis as independent claim 1 since they are analogous claims.
Referring to Claim 11, Cerrahoglu teaches the ML system of claim 1, however, fails to teach wherein the trained model characterizes a relationship between DAS and DTS data to pressure values at different depths within a wellbore.
Madasu teaches, in an analogous system, wherein the trained model characterizes a relationship between DAS and DTS data to pressure values at different depths within a wellbore (see Madasu at [0036]: “the inputs of the fully-coupled diversion model may include one or more wellbore treatment inputs and one or more formation inputs. The wellbore treatment inputs may be used to characterize the stimulation treatment operation along different portions of the wellbore 202 within the formation 204. In addition to the aforementioned flow rate and pressure at the inlet 250 of the wellbore 202, other wellbore treatment inputs may include, but are not limited to, an amount of diverter pumped into the wellbore 202 (e.g., according to the baseline diverter pumping schedule), the wellbore pressure at the injection points 210 and 220, a wellbore depth”… “In one or more embodiments, one or more of the wellbore treatment inputs of the fully-coupled diversion model in this example may be determined using various wellbore models along with real-time DAS and DTS measurements”. Therefore, since Madasu recites the relationship between DAS, DTS and pressure measurements at different portions of the wellbore, this is interpreted as different depths of the wellbore).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Cerrahoglu with the above teachings of Madasu by using DAS and DTS data to train models to predict pressure data, as taught by Cerrahoglu, wherein the relationship between DAS, DTS and pressure is characterized at different depths, as taught by Madasu. The modification would have been obvious because one of ordinary skill in the art would be motivated to characterize the stimulation treatment operation along different portions of the wellbore (as suggested by Madasu at [0036]).
Referring to dependent Claim 14 and Claim 16, they are rejected on the same basis as dependent claim 11 since they are analogous claims.
Claims 2-3, 5-6, 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Cerrahoglu et al (WO 2021/254633- hereinafter Cerrahoglu) in view of Madasu et al (US Pub. No. 2019/0345803 - hereinafter Madasu) and further in view of Jin et al (US Patent No. 11,193,367 - hereinafter Jin).
Referring to Claim 2, the combination of Cerrahoglu and Madasu teaches the ML system of claim 1, however, fails to teach wherein the DAS data used to train the model and the DAS data used by the model to predict pressure is low-frequency (LF) DAS data.
Jin teaches, in an analogous system, wherein the DAS data used to train the model and the DAS data used by the model to predict pressure is low-frequency (LF) DAS data (see Jin at Abstract: “A method of assessing cross-well interference and/or optimizing hydrocarbon production from a reservoir by obtaining low frequency DAS and DTS data and pressure data from a monitor well” and at Column 2: lines 61-64: “In this study, we demonstrate that DAS data in the low-frequency band (<1 Hz, preferably <0.1 Hz, or even <0.05 Hz) contain information that can provide critical information on cross well fluid communication”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Cerrahoglu and Madasu with the above teachings of Jin by using DAS and DTS data to train models to predict pressure data, as taught by the combination of Cerrahoglu and Madasu, wherein the DAS data used is low-frequency DAS data, as taught by Jin. The modification would have been obvious because one of ordinary skill in the art would be motivated to optimizing hydrocarbon production from a reservoir by obtaining low frequency DAS and DTS data as it contains information that can provide critical information on cross well fluid communication (as suggested by Jin at Col. 2: lines 61-64).
Referring to Claim 3, the combination of Cerrahoglu, Madasu and Jin teaches the ML system of claim 2, wherein the LF DAS data corresponds to DAS frequency components less than or equal to 2 Hertz (Hz) in frequency (see Jin at Abstract: “A method of assessing cross-well interference and/or optimizing hydrocarbon production from a reservoir by obtaining low frequency DAS and DTS data and pressure data from a monitor well” and at Column 2: lines 61-64: “In this study, we demonstrate that DAS data in the low-frequency band (<1 Hz, preferably <0.1 Hz, or even <0.05 Hz) contain information that can provide critical information on cross well fluid communication”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Cerrahoglu and Madasu with the above teachings of Jin by using DAS and DTS data to train models to predict pressure data, as taught by the combination of Cerrahoglu and Madasu, wherein the DAS data used is low-frequency DAS data, as taught by Jin. The modification would have been obvious because one of ordinary skill in the art would be motivated to optimizing hydrocarbon production from a reservoir by obtaining low frequency DAS and DTS data as it contains information that can provide critical information on cross well fluid communication (as suggested by Jin at Col. 2: lines 61-64).
Referring to dependent Claim 5 and Claim 8, they are rejected on the same basis as dependent claim 2 since they are analogous claims.
Referring to dependent Claim 6 and Claim 9, they are rejected on the same basis as dependent claim 3 since they are analogous claims.
Claims 12, 15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Cerrahoglu et al (WO 2021/254633- hereinafter Cerrahoglu) in view of Madasu et al (US Pub. No. 2019/0345803 - hereinafter Madasu) and further in view of Hearty et al (US Pub. No. 2022/0172213 - hereinafter Hearty).
Referring to Claim 12, the combination of Cerrahoglu and Madasu teaches the ML system of claim 1, however, fails to teach wherein the model is part of an ensemble of a plurality of individual predictive models that are executed in parallel and whose outputs are combined and averaged to form a model prediction of the pressure.
Hearty teaches, in an analogous system, wherein the model is part of an ensemble of a plurality of individual predictive models that are executed in parallel and whose outputs are combined and averaged to form a model prediction of the pressure (see Hearty at [0038]: “In some embodiments, parallel ensemble learning methods, such as a random forest, are utilized by the score blending software 330 to generate a blended score and reduce error through techniques such as bootstrap aggregation (or bagging)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Cerrahoglu and Madasu with the above teachings of Hearty by using DAS and DTS data to train models to predict pressure data, as taught by the combination of Cerrahoglu and Madasu, wherein the models are executed in parallel in an ensemble structure, as taught by Hearty. The modification would have been obvious because one of ordinary skill in the art would be motivated to generate a blended score and reduce error, thereby improving the prediction (as suggested by Hearty at [0038]).
Referring to dependent Claim 15 and Claim 17, they are rejected on the same basis as dependent claim 12 since they are analogous claims.
Claims 10 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Cerrahoglu et al (WO 2021/254633- hereinafter Cerrahoglu) in view of Madasu et al (US Pub. No. 2019/0345803 - hereinafter Madasu) and further in view of Jaaskelainen et al (US Pub. No. 2019/0204192 - hereinafter Jaaskelainen).
Referring to Claim 10, the combination of Cerrahoglu and Madasu teaches the ML system of claim 1, however, fails to teach further comprising a transmitter configured to transmit laser pulses into the one or more optical fiber cables, wherein backscattered light signals from the transmitted laser pulses produce the acquired DAS and DTS data.
Jaaskelainen teaches, in an analogous system, further comprising transmitting laser pulses into the one or more optical fiber cables, wherein backscattered light signals from the transmitted laser pulses produce the acquired DAS and DTS data (see Jaaskelainen at [0022]: “The signals from the DTS and DAS which are at different frequencies pass through the FBG section 112 and are only very weakly reflected back into the optical fiber 110 by the low reflectance termination section 114. The backscattered light and the reflected light that return back down the optical fiber 110, then pass through the circulator 108 and are directed to the optic and optoelectronics unit 116. The optic and optoelectronics unit 116 separates out unwanted optical frequencies and associated signals and provides the required optical signal for each of the plurality of sensing principles (not shown)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Cerrahoglu and Madasu with the above teachings of Jaaskelainen by using DAS and DTS data to train models to predict pressure data, as taught by the combination of Cerrahoglu and Madasu, wherein backscattered light signals from the transmitted laser pulses produce the acquired DAS and DTS data, as taught by Jaaskelainen. The modification would have been obvious because one of ordinary skill in the art would be motivated to separate out unwanted optical frequencies and associated signals and provides the required optical signal (as suggested by Jaaskelainen at [0022]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUIS A SITIRICHE whose telephone number is (571)270-1316. The examiner can normally be reached M-F 9am-6pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Yi can be reached at (571) 270-7519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/LUIS A SITIRICHE/Primary Examiner, Art Unit 2126
Prosecution Insights