DETAILED ACTION
This office action is in response to communication filed on January 26, 2026.
Response to Amendment
Amendments filed on January 26, 2026 have been entered.
The specification has been amended.
Claims 1 and 24-25 have been amended.
Claims 1-26 have been examined.
Response to Arguments
Applicant’s arguments, see Remarks (p. 10), filed on 01/26/2026, with respect to the objections to the specification have been fully considered. In view of the amendments to the specification addressing the informalities raised in the previous office action, the objections to the specification have been withdrawn.
Applicant’s arguments, see Remarks (p. 10-11), filed on 01/26/2026, with respect to the objections to the claims have been fully considered. In view of the amendments to the claims addressing the informalities raised in the previous office action, the objections to the claims have been withdrawn. However, upon further consideration, new claim objections are presented below to address additional informalities.
Applicant’s arguments, see Remarks (p. 11-15), filed on 01/26/2026, with respect to the rejections of claims 1-26 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicant argues (p. 12) that the claims as amended do not recite any mathematical formula or relationship, nor are they “practically performed in the human mind” and thus not directed to the mental process category. Specifically, the Examiner asserts that the limitations “determining a plurality of acoustic fingerprints” as “a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mathematical concepts to obtain additional information” … the Applicants point out that this limitation does not merely manipulate data. Instead, the acoustic fingerprints reflect physical measurements of acoustic signals from a hydrocarbon well that correspond to real-world phenomena (sound events caused by fluid flow in subsurface formations).
This argument is not persuasive.
The examiner submits that according to the Office guidance: “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the “mathematical concepts” grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word “calculating” in order to be considered a mathematical calculation. For example, a step of “determining” a variable or number using mathematical methods or “performing” a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation” (see MPEP 2106.04(a)(2)).
Following the analysis, the examiner submits that the argued limitation, when considered under the broadest reasonable interpretation in light of the specification (see [0040]-[0043]), falls under the mathematical concepts grouping, as indicated in the rejection.
Applicant also argues (p. 12-13) that the limitation “electronically clustering the plurality of acoustic fingerprints using a clustering algorithm” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation by mental processes and/or using mathematical concepts to obtain additional information.” Office Action, p. 12. However, the Applicants point out that this clustering step transforms physical acoustic measurements into actionable clusters that represent real-world hydrocarbon flow events, which are not purely mental processes. Rather, these steps are closely tied to a physical process in a specific technological field (hydrocarbon production). Nor can this limitation be considered “practically performed in the human mind” or with merely with “pen and paper.” Thus, the Applicants point out that the “acoustic output” includes physical waves being generated by physical equipment in a particular hydrocarbon well, and not just data. Therefore, the claimed steps cannot reasonably be considered a judicial exception because they involve technologically specific processing of physical signals from the real world, rather than abstract mathematical operations divorced from a practical application.
These arguments are not persuasive.
The examiner submits that, in addition to the previously cited guidance, the MPEP explains that:
“If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea” (see MPEP 2106.04(a)(2));
“Claims can recite a mental process even if they are claimed as being performed on a computer” (see MPEP 2106.04(a)(2));
“Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more” (see MPEP 2106.05(b)); and
“Below are examples of activities that the courts have found to be insignificant extra-solution activity: Mere Data Gathering … Selecting a particular data source or type of data to be manipulated” (see MPEP 2106.05(g)).
Based on these guidelines, the examiner submits that the argued feature corresponds to collecting data from a particular source/type for clustering purposes, when considered under the broadest reasonable interpretation in light of the specification (see [0044]-[0045], [0047]-[0057]), which falls under the mental processes and/or mathematical concepts groupings, as indicated in the rejection.
Applicant further argues (p. 13-14) that Applicants’ claims are specifically directed to a physical process - namely the process of hydrocarbon production. The claims recite a method of performing hydrocarbon extraction. The fact that the claim also includes steps directed to the manipulation of data to produce a similar acoustic event clusters does not change the recitation that the claim is directed to a physical process - hydrocarbon production. Thus, the Applicants’ claims are not directed to an abstract idea at all … The claims, as amended, now specifically recite “automatically changing an operational parameter of the hydrocarbon well in real-time based on the plurality of acoustic event clusters.” This is a specific type of response described and supported verbatim by the specification, and not a generalized “response” described in the Examiner’s rejection. The Applicants further assert that “automatically changing an operation operational parameter of the hydrocarbon well in real-time based on the plurality of acoustic event clusters” imposes a physical transformation of the well operation. In particular, this is a real-world action that materially alters well operation, directly impacting hydrocarbon production, safety, and reservoir management, as described in the specification.
These arguments are not persuasive.
The examiner submits that the argued feature does not limit the application of the judicial exception but instead covers substantially all practical applications of the judicial exception (e.g., it covers all possible operational parameters and all possible changes of all possible operational parameters that could be applied in the related field, see specification at [0069]), and according to the MPEP: “A transformation applied to a generically recited article or to any and all articles would likely not provide significantly more than the judicial exception” (see MPEP 2106.05(c)).
Furthermore, the examiner submits that as indicated in the MPEP: “In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement … Conversely, if the specification explicitly sets forth an improvement only in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine that the claim improves technology or a technical field” (see MPEP 2106.04(d)(1)).
Moreover, applicant argues (p. 14) that “A method of producing hydrocarbons from a subsurface region using acoustic output from a hydrocarbon well” combined with “producing or ceasing to produce the hydrocarbons based on the automatically changed operation parameter” establishes a technological environment and practical application. This is far more than a generic field-of-use limitation because, when considered together with the other limitations, involves real-time measurement, interpretation, and use of subsurface acoustic data for well operation control. For these reasons, particularly when properly considered as a whole claim, claim 1 is not “directed to” the asserted abstract ideas because the alleged abstract ideas are integrated into a practical application, satisfying Step 2A-Prong Two.
This argument is not persuasive.
The examiner submits that, as indicated in the rejection and above, the amended features “automatically changing an operational parameter of the hydrocarbon well in real-time based on the plurality of acoustic event clusters; and producing or ceasing to produce the hydrocarbons based on the automatically changed operation parameter” append a transformation at a high level of generality that covers substantially all practical applications of the judicial exception (e.g., what operational parameter is being change? How is it being changed? Producing or ceasing to produce hydrocarbons based on the change are the only two possible outputs of any application of the judicial exception; see specification at [0067]-[0069]; see also MPEP 2106.05(c)).
In addition, the examiner submits that as explained in the October 2019 Update: Subject Matter Eligibility: “… in Parker v. Flook, the Court found that the claim recited a mathematical formula. This determination was not altered by the fact that the math was being used to solve an engineering problem (i.e., updating an alarm limit during catalytic conversion processes)” (p. 3).
Applicant also argues (p. 14-15) that as amended, the claims include “changing an operation parameter of the hydrocarbon well based on the plurality of acoustic event clusters,” which is not “extra-solution activity” but a specific use of the generated event clusters to improve hydrocarbon production by changing a parameter specifically based on the acoustic event clusters. Therefore, particularly when considered as a whole, claims 1, 24, 25 are not “directed to” the asserted abstract idea because they integrate the asserted abstract idea into a specific practical application under Step 2A, Prong Two.
This argument is not persuasive.
The examiner submits that the rejection indicated the argued feature as appending a transformation at a high level of generality (see rejection below), and according to the current guidance: “It is noted that while the transformation of an article is an important clue, it is not a stand-alone test for eligibility … And if a claim fails the Alice/Mayo test (i.e., is directed to an exception at Step 2A and does not amount to significantly more than the exception in Step 2B), then the claim is ineligible even if it passes the M-or-T test. DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256, 113 USPQ2d 1097, 1104 (Fed. Cir. 2014) (“[I]n Mayo, the Supreme Court emphasized that satisfying the machine-or-transformation test, by itself, is not sufficient to render a claim patent-eligible, as not all transformations or machine implementations infuse an otherwise ineligible claim with an “inventive concept.”) … A transformation applied to a generically recited article or to any and all articles would likely not provide significantly more than the judicial exception” (see MPEP 2106.05(c)).
In addition, applicant argues (p. 15) that The invention as claimed analyzes measured acoustic signals and uses the analysis to automatically adjust well operations, which is a specific, tangible application that improves hydrocarbon production and well safety. Moreover, the Applicants respectfully point out that the operational control step is not generic; it requires processing acoustic fingerprints and clustered events to generate real- world operational changes, which cannot be performed purely mentally or generically. Therefore, the combination of real-world data acquisition, acoustic fingerprinting, clustering, and well operation adjustment constitutes a practical implementation that is also significantly more than the alleged abstract idea, under Step 2B. Thus, claim 1 recites an inventive process tied to a specific technology, not directed to any judicial exception, and thus meets the criteria for patent eligibility under 35 U.S.C. §101.
These arguments are not persuasive.
The examiner submits that the claimed invention, when considered as a whole under the broadest reasonable interpretation in light of the specification, recite data collection and data manipulation using mental processes and/or mathematical concepts (e.g., calculating amplitude/frequency of signals, filtering, normalization, outlier removal, smoothing, clustering, thresholding, see specification at [0040]-[0045], [0047]-[0057]; see also [0066] regarding an operator manually performing additional steps of the analysis) while generally linking the judicial exception to a field of use (i.e., producing hydrocarbons), appending extra-solution activities (e.g., mere data gathering by selecting a particular data source/type), and appending steps at a high level of generality which, under the current guidance and as explained above, is not eligible subject matter under 35 U.S.C. 101 (see Claim Rejections - 35 USC § 101 section below for detailed analysis).
Applicant’s arguments, see Remarks (p. 15-19), filed on 01/26/2026, with respect to the rejections of claims 1-26 under 35 U.S.C. 103 have been fully considered but are moot in view of new grounds of rejections.
Claim Objections
Claim 26 is objected to because of the following informalities:
Claim language should read “The method of claim 1, wherein the operational parameter is changed responsive to a notification of a specific sound event” in order to provide appropriate antecedence basis.
Appropriate correction 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 1-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Regarding claim 1, the examiner submits that under Step 1 of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (see also 2019 Revised Patent Subject Matter Eligibility Guidance) for evaluating claims for eligibility under 35 U.S.C. 101, the claim is to a process, which is one of the statutory categories of invention.
Continuing with the analysis, under Step 2A - Prong One of the test:
the limitation “determining a plurality of acoustic fingerprints of the acoustic output, wherein the plurality of acoustic fingerprints includes a corresponding acoustic fingerprint for each sound event of the plurality of sound events” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mathematical concepts to obtain additional information (i.e., a plurality of acoustic fingerprints; see specification at [0040]-[0043]). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated), the limitation in the context of the claim mainly refers to applying mathematical concepts to manipulate data to obtain additional information.
the limitation “electronically clustering the plurality of acoustic fingerprints, utilizing a clustering algorithm, to generate a plurality of acoustic event clusters, wherein each acoustic event cluster of the plurality of acoustic event clusters includes a corresponding fingerprint subset of the plurality of acoustic fingerprints, and further wherein each acoustic fingerprint in the corresponding fingerprint subset includes at least one similar acoustic property” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation by mental processes and/or using mathematical concepts to obtain additional information (i.e., a plurality of acoustic event clusters; see specification at [0044]-[0045], [0047]-[0057]). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated), the limitation in the context of the claim mainly refers to performing mental evaluations and/or applying mathematical concepts to manipulate data for clustering purposes.
Therefore, the claim recites a judicial exception under Step 2A - Prong One of the test.
Furthermore, under Step 2A - Prong Two of the test, this judicial exception is not integrated into a practical application. In particular, the additional elements recited in the claim:
“A method of producing hydrocarbons from a subsurface region using acoustic output from a hydrocarbon well” generally links the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h));
“receiving the acoustic output, wherein the acoustic output includes information regarding a plurality of sound events, and further wherein each sound event of the plurality of sound events includes at least one corresponding sound detected at the hydrocarbon well” adds extra-solution activities (e.g., mere data gathering, source/type of data to be manipulated) (see MPEP 2106.05(g)); and
“automatically changing an operational parameter of the hydrocarbon well in real-time based on the plurality of acoustic event clusters; and producing or ceasing to produce the hydrocarbons based on the automatically changed operation parameter” appends a transformation at a high level of generality (see specification at [0067]-[0069]; see also MPEP 2106.05(c)).
Accordingly, these additional elements, when considered individually and in combination, do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considering the claim as a whole. The claim is directed to a judicial exception under Step 2A of the test.
Additionally, under Step 2B of the test, the claim does not include additional elements that, when considered individually and in combination, are sufficient to amount to significantly more than the judicial exception because the additional elements:
generally link the use of the judicial exception to a particular technological environment or field of use (e.g., producing hydrocarbons by clustering acoustic data), which as indicated in the MPEP: “As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible “simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use.” Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application” (see MPEP 2106.05(h));
recite extra-solution activities (i.e., mere data gathering by selecting a particular data source/type to be manipulated) which amount to necessary data gathering without imposing meaningful limits on the claim, and as described in the MPEP: “The term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent” (see MPEP 2106.05(g)); and
append steps at a high level of generality such that substantially all practical applications of the judicial exception(s) are covered (i.e., automatically changing an operational parameter of the hydrocarbon well in real-time based on the plurality of acoustic event clusters; and producing or ceasing to produce the hydrocarbons based on the automatically changed operation parameter), which as indicated in the MPEP: “A transformation applied to a generically recited article or to any and all articles would likely not provide significantly more than the judicial exception” (see MPEP 2106.05(c)).
The claim, when considered as a whole, does not provide significantly more under Step 2B of the test.
The claim is not patent eligible.
Similarly, independent claims 24-25 are directed to a judicial exception (abstract idea) without significantly more as explained above with regards to claim 1.
With regards to the dependent claims they are also directed to the non-statutory subject matter because:
they just extend the abstract idea of the independent claim by additional limitations (Claims 10-12, 16-23), that under the broadest reasonable interpretation in light of the specification, cover performance of the limitations using mental processes and/or mathematical concepts, and
the additional elements recited in the dependent claims, when considered individually and in combination, refer to extra-solution activities (e.g., mere data gathering using a data type or source) or mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea, or append steps at a high level of generality such that substantially all practical applications of the judicial exception are covered (Claims 2-10, 13-15 and 26), which as indicated in the Office’s guidance does not integrate the judicial exception into a practical application (Step 2A – Prong Two) and/or does not provide significantly more (Step 2B) when considering the claimed invention as a whole.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
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.
Claims 1-3 and 6-25 are rejected under 35 U.S.C. 103 as being unpatentable over Lu (US 20180010443 A1), hereinafter ‘Lu, in view of Langnes (US 20190390546 A1), hereinafter ‘Langnes’.
Regarding claim 1.
Lu discloses:
A method (Fig. 5A) of producing hydrocarbons from a subsurface region using acoustic output from a hydrocarbon well (Figs. 1 and 8-9; [0022]-[0023], [0036]: a method for detecting, classifying and quantifying underground fluids, which implies the production of fluids such as oil or gas (see also [0002]), based on acoustic signals measured in a wellbore is presented, the method including creating classification and estimation rules based on training data), the method comprising:
receiving the acoustic output (Fig. 5A, items 502-506; [0025], [0036]: a fused signal (acoustic output) is obtained from acoustic signals detected by individual sensors in an array of sensors and used for analysis), wherein the acoustic output includes information regarding a plurality of sound events ([0031]-[0033]: fused signal includes information corresponding to one or more sources (sound event)), and further wherein each sound event of the plurality of sound events includes at least one corresponding sound detected at the hydrocarbon well ([0023]: pressure drops resulting from fluid flows are detected by sensors as acoustic signals (see also [0035]-[0037] regarding analyzing the acoustic source for identifying certain specified features of various types of flows or flow magnitudes));
determining a plurality of acoustic fingerprints of the acoustic output, wherein the plurality of acoustic fingerprints includes a corresponding acoustic fingerprint for each sound event of the plurality of sound events (Fig. 5A, item 510; [0031]-[0033], [0037]: information/features (acoustic fingerprints) such as acoustic-source energy level, amplitude or other fused-signal parameter as a function of the acoustic-source location is/are computed to determine classifiable information); and
electronically clustering the plurality of acoustic fingerprints, utilizing a clustering algorithm, to generate a plurality of acoustic event clusters, wherein each acoustic event cluster of the plurality of acoustic event clusters includes a corresponding fingerprint subset of the plurality of acoustic fingerprints, and further wherein each acoustic fingerprint in the corresponding fingerprint subset includes at least one similar acoustic property (Fig. 5A, items 512-516; [0037]-[0039]: classification rules (see Figs. 6E and 7C, [0041]-[0042]) are derived based on analysis of features using statistical classification and estimation methods (clustering algorithm) that determine classifiable information (at least one similar acoustic property, see also [0035]), the classification being performed by data-processing facility (see [0043])).
Lu does not explicitly disclose:
automatically changing an operational parameter of the hydrocarbon well in real-time based on the plurality of acoustic event clusters; and
producing or ceasing to produce the hydrocarbons based on the automatically changed operation parameter.
Langnes teaches:
“Disclosed herein is a new real time signal processing architecture that allows for the identification of various downhole events including gas influx detection, downhole leak detection, well-barrier integrity monitoring, fluid inflow, and the identification of in-well sand ingress zones in real time or near real time … Various sensors (e.g., distributed fiber optic acoustic sensors, etc.) can be used to obtain an acoustic sampling at various points along the wellbore. The acoustic sample can then be processed using signal processing architecture with various feature extraction techniques (e.g., spectral feature extraction techniques) to obtain a measure of one or more frequency domain features that enable selectively extracting the acoustic signals of interest from background noise and consequently aiding in improving the accuracy of the identification of the movement of fluids and/or solids (e.g., sand ingress locations, gas influx locations, constricted fluid flow locations, etc.) in real time” ([0027]: real-time acoustic sampling analysis is used to detect downhole events (see also [0043], [0066]; see further [0072] regarding signal classes)); and
“The ability to identify various events in the well bore may allow for various actions to be taken (remediation bore may allow for various actions to be taken (remediation can be shut in, production can be increased or decreased, and/or remedial measures can be taken in the wellbore, as appropriate based on the identified event(s)” ([0031]: based on events identification, various actions can be taken such as shut in or adjust production (see also [0125] regarding automatic changes; see also Lu at [0002], [0037], [0041]-[0043], [0046])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lu in view of Langnes to automatically change an operational parameter of the hydrocarbon well in real-time based on the plurality of acoustic event clusters; and to produce or cease to produce the hydrocarbons based on the automatically changed operation parameter, in order to detect flows for the purpose of characterizing the formation and hydrocarbon reservoirs and steer or adjust drilling operations, while making appropriate decisions to maintain well integrity, as discussed by Lu ([0002], [0043], [0046]).
Regarding claim 2.
Lu in view of Langnes discloses all the features of claim 1 as described above.
Lu does not explicitly disclose:
receiving the acoustic output includes receiving an acoustic data file representative of the acoustic output.
However, Lu teaches:
“The data-processing facility providing the computational functionality for processing and fusing the acoustic signals received by the individual sensors 808 and classifying and/or quantifying the detected flows based on the fused signals may be implemented by either one of the control and processing circuitry 812 or the data-processing system 814, or by both in combination. For example, in some embodiments, the control and processing circuitry 812 pre-processes the individual sensor signals (e.g., through signal conditioning, filtering, and/or noise cancellation) and transmits them to the surface data-processing system 814, where the fused signals and fused-signal parameter map are computed, flow-induced acoustic sources are detected and localized based thereon, and flow type and/or one or more quantitative flow parameters (such as the flow rate) are further determined for the detected acoustic source(s) based on the acoustic signature (s) of the associated fused signal (s) (e.g., relevant features extracted from the fused signal (s)) in conjunction with the classification or estimation rules” ([0043]: sensor signals are transmitted to data processing system for analysis; transmission may include storing signals in history files to facilitate data transmission (see also [0033] regarding historical logging data and [0048] regarding data-storage and data repositories; see further Langnes at [0064] and [0143] regarding storing data in memory)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lu in view of Langnes to configure the reception of the acoustic output including receiving an acoustic data file representative of the acoustic output, in order to facilitate data communication among different system components for improving data analysis while maintaining records of data acquisition.
Regarding claim 3.
Lu in view of Langnes discloses all the features of claim 1 as described above.
Lu further discloses:
receiving the acoustic output includes recording the acoustic output utilizing an acoustic monitoring system of the hydrocarbon well ([0023]-[0025], [0036]: a fused signal (acoustic output) is generated (recorded) from acoustic signals detected by individual sensors (Fig. 1, items 100) in an array of sensors (acoustic monitoring system) in a wellbore (Figs. 1 and 8-9) (see also [0033] regarding historical logging data and [0048] regarding data-storage and data repositories)).
Regarding claim 6.
Lu in view of Langnes discloses all the features of claim 3 as described above.
Lu further discloses:
the acoustic monitoring system includes a downhole acoustic sensor (Fig. 1, items 100) that is positioned within a wellbore of the hydrocarbon well (Fig. 1; [0023]: array of sensors is arranged in a wellbore producing hydrocarbons), and further wherein the recording includes utilizing the downhole acoustic sensor to detect the acoustic output ([0023]-[0025], [0036]: a fused signal (acoustic output) is generated (recorded) from acoustic signals detected by individual sensors in an array of sensors (acoustic monitoring system) in the wellbore (see also Figs. 8-9)).
Regarding claim 7.
Lu in view of Langnes discloses all the features of claim 6 as described above.
Lu further discloses:
the downhole acoustic sensor (Fig. 1, items 100) is positioned along a length of the wellbore of the hydrocarbon well (Fig. 1; [0023]: array of sensors is arranged linearly along the longitudinal axis of the wellbore producing hydrocarbons (see also Figs. 8-9)).
Regarding claim 8.
Lu in view of Langnes discloses all the features of claim 7 as described above.
Lu further discloses:
the downhole acoustic sensor includes a distributed acoustic sensor that extends along at least a fraction of the length of the wellbore ([0024]: acoustic sensors positioned in the wellbore include a distributed fiber-optic cable), and further wherein the recording includes utilizing the distributed acoustic sensor to detect the acoustic output ([0023]-[0025], [0036]: a fused signal (acoustic output) is generated (recorded) from acoustic signals detected by the distributed fiber-optic cable in the wellbore (see also [0044])).
Regarding claim 9.
Lu in view of Langnes discloses all the features of claim 8 as described above.
Lu further discloses:
the distributed acoustic sensor includes a fiber optic cable that extends along the fraction of the length of the wellbore ([0024]: acoustic sensors positioned in the wellbore include a distributed fiber-optic cable (see also [0044]).
Regarding claim 10.
Lu in view of Langnes discloses all the features of claim 8 as described above.
Lu further discloses:
the acoustic output includes a plurality of sounds, and further wherein the method includes determining a region of the distributed acoustic sensor utilized to detect each sound of the plurality of sounds ([0031]-[0033]: fused signal includes information corresponding to one or more sources (plurality of sounds); in the case of multiple sources, higher energy levels are shown at multiple locations (regions) which are used for analysis (see Fig. 4A indicating level energy and sensor location, see also [0044], [0047])).
Regarding claim 11.
Lu in view of Langnes discloses all the features of claim 10 as described above.
Lu further discloses:
determining a position, along the length of the wellbore, for each sound of the plurality of sounds based, at least in part, on the region of the distributed acoustic sensor utilized to detect each sound of the plurality of sounds ([0047]: to detect and localize acoustic sources, fused signals parameters, such as energy levels, are computed as a function of source location (see Fig. 4A indicating level energy and sensor location)).
Regarding claim 12.
Lu in view of Langnes discloses all the features of claim 11 as described above.
Lu further discloses:
tracking the position, along the length of the wellbore, for at least a tracked subset of the plurality of sounds ([0024]: spatiotemporal relations between signals received from the same source (at least a tracked subset of the plurality of sounds) at multiple sensors permit information about the source direction/location to be obtained (see also [0031]-[0033], [0045], [0047])).
Regarding claim 13.
Lu in view of Langnes discloses all the features of claim 6 as described above.
Lu further discloses:
the downhole acoustic sensor includes at least one discrete downhole acoustic sensor (Fig. 1, items 100; [0023]-[0024]: array of sensors is positioned in a wellbore producing hydrocarbons (see also Fig. 8, items 808 and Fig. 9, item 924)).
Regarding claim 14.
Lu in view of Langnes discloses all the features of claim 13 as described above.
Lu further discloses:
the at least one discrete downhole acoustic sensor includes at least one of at least one downhole microphone and at least one downhole vibration sensor ([0024]: acoustic sensors include hydrophones).
Regarding claim 15.
Lu in view of Langnes discloses all the features of claim 13 as described above.
Lu further discloses:
the at least one discrete downhole acoustic sensor includes a plurality of discrete downhole acoustic sensors (Fig. 1, items 100) spaced apart along at least a fraction of a length of the wellbore ([0023]-[0024]: array of sensors is arranged in a wellbore producing hydrocarbons (see also Fig. 8, items 808 and Fig. 9, item 924)).
Regarding claim 16.
Lu in view of Langnes discloses all the features of claim 1 as described above.
Lu further discloses:
determining the plurality of acoustic fingerprints includes establishing a plurality of discrete acoustic fingerprints from the acoustic output ([0031]-[0033], [0037]: information/features (acoustic fingerprints) such as acoustic-source energy level, amplitude or other fused-signal parameter as a function of the acoustic-source location is/are computed to determine classifiable information (a plurality of discrete acoustic fingerprints from the acoustic output)).
Regarding claim 17.
Lu in view of Langnes discloses all the features of claim 1 as described above.
Lu further discloses:
determining the plurality of acoustic fingerprints includes determining a plurality of amplitude fingerprints of an amplitude of the acoustic output as a function of time ([0031]-[0033], [0037]: information/features (acoustic fingerprints) such as acoustic-source energy level, amplitude or other fused-signal parameter as a function of the acoustic-source location is/are computed in time domain (see [0022], [0035]) to determine classifiable information).
Regarding claim 18.
Lu in view of Langnes discloses all the features of claim 1 as described above.
Lu further discloses:
determining the plurality of acoustic fingerprints includes determining a plurality of frequency fingerprints of a frequency of the acoustic output as a function of time ([0031]-[0033], [0037]: information/features (acoustic fingerprints) such as acoustic-source energy level, amplitude or other fused-signal parameter as a function of the acoustic-source location is/are computed in frequency domain (see [0022], [0035]) to determine classifiable information).
Regarding claim 19.
Lu in view of Langnes discloses all the features of claim 1 as described above.
Lu further does not explicitly disclose:
determining the plurality of acoustic fingerprints includes downsampling the acoustic output to generate the plurality of acoustic fingerprints.
However, Lu teaches:
“In some embodiments, the acoustic sensor array is operated in a fast logging speed (e.g., at as much as 60 feet per minute) to detect flows initially with coarse spatial resolution. Once one or more flows have been detected at certain depths, regions at those depths can be re-logged at a slower logging speed, or in stationary mode, to localize the flow(s) at a finer spatial resolution” ([0045]: acoustic sensor array can operate at different speeds such as at a fast logging speed for coarse spatial resolution (analogous to down-sampling acoustic output)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lu in view of Langnes to configure the determining of the plurality of acoustic fingerprints including downsampling the acoustic output to generate the plurality of acoustic fingerprints, in order to reduce computational costs when initially identifying sound events.
Regarding claim 20.
Lu in view of Langnes discloses all the features of claim 1 as described above.
Lu further discloses:
determining the plurality of acoustic fingerprints further includes at least one of:
(i) filtering the plurality of acoustic fingerprints;
(ii) normalizing the plurality of acoustic fingerprints;
(iii) removing at least one outlier from the plurality of acoustic fingerprints;
(iv) smoothing the plurality of acoustic fingerprints; and
(v) weighting the plurality of acoustic fingerprints ([0037]: features of fused signals are obtained and used to identify those having classifiable information; features that are not appropriate for clustering purposes are not used (filtered)).
Regarding claim 21.
Lu in view of Langnes discloses all the features of claim 1 as described above.
Lu further discloses:
electronically clustering the plurality of acoustic fingerprints includes grouping acoustic fingerprints of the plurality of acoustic fingerprints, which include the at least one similar acoustic property, within a same corresponding fingerprint subset ([0037]-[0039]: classification rules (see Figs. 6E and 7C, [0041]-[0042]) are derived based on analysis of features using statistical classification and estimation methods that determine classifiable information (at least one similar acoustic property)).
Regarding claim 22.
Lu in view of Langnes discloses all the features of claim 1 as described above.
Lu further discloses:
the clustering algorithm includes an agglomerative clustering algorithm ([0038]: hierarchical clustering (agglomerative clustering algorithm) is employed for classification).
Regarding claim 23.
Lu in view of Langnes discloses all the features of claim 1 as described above.
Lu further does not explicitly disclose:
the method further includes generating a trade-off relationship that relates a number of acoustic event clusters in the plurality of acoustic event clusters to a degree of similarity in the at least one similar acoustic property for the corresponding fingerprint subset included within each acoustic event cluster.
However, Lu teaches:
“From the fused signals, features are then extracted for an initially large-dimensional feature space (operation 510), which is thereafter collapsed, based on an assessment of the indicativeness of each feature of the flow type or flow parameter of interest, into a much lower-dimensional feature space - a process also referred to as feature-level fusion (operation 512). (Herein, one or more of the flow-scenario types may correspond to the absence of a flow.) Feature level fusion serves to remove potentially redundant information in the original feature space and retain only independent information. For classification tasks, the idea is to separate feature values in the fused feature space with respect to the types of flows. Here, we assume that different types of flows will result in different feature values. If two different types of flows have the same value for a certain feature, this feature is either not appropriate, or those two types of flows are inherently not classifiable based on the information contained in the received signals. In the former case, feature extraction and fusion (operations 510, 512) can be repeated to identify features that contain classifiable information. For flow-parameter (e.g., flow-rate) estimation, the idea is to identify features whose values are correlated with the flow parameter” ([0037]: feature space is reduced based on assessment of indicativeness (analogous to trade-off relationship) of each feature in order to identify classifiable information).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lu in view of Langnes to generate a trade-off relationship that relates a number of acoustic event clusters in the plurality of acoustic event clusters to a degree of similarity in the at least one similar acoustic property for the corresponding fingerprint subset included within each acoustic event cluster, in order to remove potentially redundant information while identifying features that contain classifiable information, as discussed by Lu ([0037]).
Regarding claim 24.
Lu discloses:
A hydrocarbon well (Figs. 1 and 8-9) comprising:
a wellbore (Fig. 8, item 804) that extends within a subsurface region ([0023], [0043], [0046]: wellbore extends within formation (Fig. 1, item 110; Fig. 9, item 910));
an acoustic monitoring system (Fig. 1, items 100; Fig. 8, items 808; Fig. 9, item 924) configured to monitor an acoustic output from the hydrocarbon well ([0023]-[0025], [0043], [0046]: a fused signal (acoustic output) is obtained from acoustic signals detected by individual sensors in an array of sensors and used for analysis (see also Fig. 11)); and
a controller (Fig. 8, items 812 and 814) programmed to ([0043], [0046]: control and processing circuitry and surface data-processing system are used for analysis (see also [0048])):
receive the acoustic output (Fig. 5A, items 502-506; [0025], [0036]: a fused signal (acoustic output) is obtained from acoustic signals detected by individual sensors in an array of sensors and used for analysis), wherein the acoustic output includes information regarding a plurality of sound events ([0031]-[0033]: fused signal includes information corresponding to one or more sources (sound event)), and further wherein each sound event of the plurality of sound events includes at least one corresponding sound detected at the hydrocarbon well ([0023]: pressure drops resulting from fluid flows are detected by sensors as acoustic signals (see also [0035]-[0037] regarding analyzing the acoustic source for identifying certain specified features of various types of flows or flow magnitudes));
determine a plurality of acoustic fingerprints of the acoustic output, wherein the plurality of acoustic fingerprints includes a corresponding acoustic fingerprint for each sound event of the plurality of sound events (Fig. 5A, item 510; [0031]-[0033], [0037]: information/features (acoustic fingerprints) such as acoustic-source energy level, amplitude or other fused-signal parameter as a function of the acoustic-source location is/are computed to determine classifiable information); and
electronically cluster the plurality of acoustic fingerprints, utilizing a clustering algorithm, to generate a plurality of acoustic event clusters, wherein each acoustic event cluster of the plurality of acoustic event clusters includes a corresponding fingerprint subset of the plurality of acoustic fingerprints, and further wherein each acoustic fingerprint in the corresponding fingerprint subset includes at least one similar acoustic property (Fig. 5A, items 512-516; [0037]-[0039]: classification rules (see Figs. 6E and 7C, [0041]-[0042]) are derived based on analysis of features using statistical classification and estimation methods (clustering algorithm) that determine classifiable information (at least one similar acoustic property, see also [0035]), the classification being performed by data-processing facility (see [0043])).
Lu does not explicitly disclose:
automatically change an operational parameter of the hydrocarbon well in real-time based on the plurality of acoustic event clusters.
Langnes teaches:
“Disclosed herein is a new real time signal processing architecture that allows for the identification of various downhole events including gas influx detection, downhole leak detection, well-barrier integrity monitoring, fluid inflow, and the identification of in-well sand ingress zones in real time or near real time … Various sensors (e.g., distributed fiber optic acoustic sensors, etc.) can be used to obtain an acoustic sampling at various points along the wellbore. The acoustic sample can then be processed using signal processing architecture with various feature extraction techniques (e.g., spectral feature extraction techniques) to obtain a measure of one or more frequency domain features that enable selectively extracting the acoustic signals of interest from background noise and consequently aiding in improving the accuracy of the identification of the movement of fluids and/or solids (e.g., sand ingress locations, gas influx locations, constricted fluid flow locations, etc.) in real time” ([0027]: real-time acoustic sampling analysis is used to detect downhole events (see also [0043], [0066]; see further [0072] regarding signal classes)); and
“The ability to identify various events in the well bore may allow for various actions to be taken (remediation bore may allow for various actions to be taken (remediation can be shut in, production can be increased or decreased, and/or remedial measures can be taken in the wellbore, as appropriate based on the identified event(s)” ([0031]: based on events identification, various actions can be taken such as shut in or adjust production (see also [0125] regarding automatic changes; see also Lu at [0002], [0037], [0041]-[0043], [0046])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lu in view of Langnes to automatically change an operational parameter of the hydrocarbon well in real-time based on the plurality of acoustic event clusters, in order to detect flows for the purpose of characterizing the formation and hydrocarbon reservoirs and steer or adjust drilling operations, while making appropriate decisions to maintain well integrity, as discussed by Lu ([0002], [0043], [0046]).
Regarding claim 25.
Lu discloses:
Non-transitory computer readable storage media (Fig. 11, item 1104 – ‘memory’) including computer-executable instructions that, when executed, direct a controller (Fig. 11, item 1102 – ‘processor(s)’) of the hydrocarbon well (Figs. 1 and 8-9; [0048]-[0049]: memory in computer system includes programs to perform the described methods (see [0022])) to:
receive an acoustic output (Fig. 5A, items 502-506; [0025], [0036]: a fused signal (acoustic output) is obtained from acoustic signals detected by individual sensors in an array of sensors and used for analysis), wherein the acoustic output includes information regarding a plurality of sound events ([0031]-[0033]: fused signal includes information corresponding to one or more sources (sound event)), and further wherein each sound event of the plurality of sound events includes at least one corresponding sound detected at the hydrocarbon well ([0023]: pressure drops resulting from fluid flows are detected by sensors as acoustic signals (see also [0035]-[0037] regarding analyzing the acoustic source for identifying certain specified features of various types of flows or flow magnitudes));
determine a plurality of acoustic fingerprints of the acoustic output, wherein the plurality of acoustic fingerprints includes a corresponding acoustic fingerprint for each sound event of the plurality of sound events (Fig. 5A, item 510; [0031]-[0033], [0037]: information/features (acoustic fingerprints) such as acoustic-source energy level, amplitude or other fused-signal parameter as a function of the acoustic-source location is/are computed to determine classifiable information); and
electronically cluster the plurality of acoustic fingerprints, utilizing a clustering algorithm, to generate a plurality of acoustic event clusters, wherein each acoustic event cluster of the plurality of acoustic event clusters includes a corresponding fingerprint subset of the plurality of acoustic fingerprints, and further wherein each acoustic fingerprint in the corresponding fingerprint subset includes at least one similar acoustic property (Fig. 5A, items 512-516; [0037]-[0039]: classification rules (see Figs. 6E and 7C, [0041]-[0042]) are derived based on analysis of features using statistical classification and estimation methods (clustering algorithm) that determine classifiable information (at least one similar acoustic property, see also [0035]), the classification being performed by data-processing facility (see [0043])).
Lu does not explicitly disclose:
automatically change an operational parameter of the hydrocarbon well in real-time based on the plurality of acoustic event clusters.
Langnes teaches:
“Disclosed herein is a new real time signal processing architecture that allows for the identification of various downhole events including gas influx detection, downhole leak detection, well-barrier integrity monitoring, fluid inflow, and the identification of in-well sand ingress zones in real time or near real time … Various sensors (e.g., distributed fiber optic acoustic sensors, etc.) can be used to obtain an acoustic sampling at various points along the wellbore. The acoustic sample can then be processed using signal processing architecture with various feature extraction techniques (e.g., spectral feature extraction techniques) to obtain a measure of one or more frequency domain features that enable selectively extracting the acoustic signals of interest from background noise and consequently aiding in improving the accuracy of the identification of the movement of fluids and/or solids (e.g., sand ingress locations, gas influx locations, constricted fluid flow locations, etc.) in real time” ([0027]: real-time acoustic sampling analysis is used to detect downhole events (see also [0043], [0066]; see further [0072] regarding signal classes)); and
“The ability to identify various events in the well bore may allow for various actions to be taken (remediation bore may allow for various actions to be taken (remediation can be shut in, production can be increased or decreased, and/or remedial measures can be taken in the wellbore, as appropriate based on the identified event(s)” ([0031]: based on events identification, various actions can be taken such as shut in or adjust production (see also [0125] regarding automatic changes; see also Lu at [0002], [0037], [0041]-[0043], [0046])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lu in view of Langnes to automatically change an operational parameter of the hydrocarbon well in real-time based on the plurality of acoustic event clusters, in order to detect flows for the purpose of characterizing the formation and hydrocarbon reservoirs and steer or adjust drilling operations, while making appropriate decisions to maintain well integrity, as discussed by Lu ([0002], [0043], [0046]).
Claims 4-5 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Lu, in view of Langnes, and in further view of Thompson (US 20190112912 A1), hereinafter ‘Thompson’.
Regarding claim 4.
Lu in view of Langnes discloses all the features of claim 3 as described above.
Lu does not explicitly discloses:
the acoustic monitoring system includes a surface acoustic sensor positioned on a surface region, and further wherein the recording includes utilizing the surface acoustic sensor to detect the acoustic output.
Thompson teaches:
“In some implementations, the one or more sensors may include one or more of fluid level sensors, voltage sensors, acoustic sensors, pressure sensors, temperature sensors, motion sensors, current sensors, impedance sensors, magnetic sensors, strain sensors, and/or other sensors. In some implementations, the one or more sensors include one or more hydrophones … In some implementations, a first accelerometer, a first hydrophone, and a first strain gage may be coupled to the hanger … and a second hydrophone, a third accelerometer, and a magnetometer may be coupled to the wellhead and/or the extraction equipment. In some implementations, the one or more sensors may be configured such that the response is an acoustic response, and the one or more processors may be configured such that casing structural integrity events are determined based on a speed of sound caused by the stimulus through one or more of the tubing, the casing, the liquid, or the gas” ([0009]: surface acoustic sensors are used to detect structural integrity of well components (see Fig. 1, items 18; [0004]-[0005], [0030], [0034]-[0035])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lu, in view of Langnes, and in further view of Thompson, to configure the acoustic monitoring system including a surface acoustic sensor positioned on a surface region, and the recording including utilizing the surface acoustic sensor to detect the acoustic output, in order to improve spatial data acquisition along different components in the system (e.g., surface or near-surface components) for accurate classification of events during drilling operations.
Regarding claim 5.
Lu in view of Langnes and Thompson discloses all the features of claim 4 as described above.
Lu does not explicitly discloses:
the surface acoustic sensor includes at least one of at least one surface microphone and at least one surface vibration sensor.
Thompson teaches:
“In some implementations, the one or more sensors may include one or more of fluid level sensors, voltage sensors, acoustic sensors, pressure sensors, temperature sensors, motion sensors, current sensors, impedance sensors, magnetic sensors, strain sensors, and/or other sensors. In some implementations, the one or more sensors include one or more hydrophones … In some implementations, a first accelerometer, a first hydrophone, and a first strain gage may be coupled to the hanger … and a second hydrophone, a third accelerometer, and a magnetometer may be coupled to the wellhead and/or the extraction equipment. In some implementations, the one or more sensors may be configured such that the response is an acoustic response, and the one or more processors may be configured such that casing structural integrity events are determined based on a speed of sound caused by the stimulus through one or more of the tubing, the casing, the liquid, or the gas” ([0009]: surface acoustic sensors used to detect structural integrity of well components include hydrophones (microphones) (see Fig. 1, items 18; [0004]-[0005], [0030], [0034]-[0035])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lu in view of Langnes and Thompson to configure the surface acoustic sensor including at least one of at least one surface microphone and at least one surface vibration sensor, in order to detect distant and low-level sounds occurring during drilling operations for improving classification of sounds events.
Regarding claim 26.
Lu in view of Langnes discloses all the features of claim 1 as described above.
Lu does not disclose:
the operation parameter is changed responsive to a notification of a specific sound event.
Thompson teaches:
“User interface 22 may be configured to facilitate delivery of casing structural integrity event notifications generated by processor 20 and/or other information to users. User interface 22 may be configured to receive entry and/or selection of information from users. User interface 22 may be configured to receive entry and/or selection of control inputs from users that facilitate operation of well 8 such that the users may adjust and/or cease the operation of well 8 if necessary, responsive to receiving casing structural integrity event notifications” ([0070]: casing structural integrity event notifications are delivered to users for adjusting well operations (see also [0004]-[0005], [0009], [0034]-[0035] regarding using acoustic sensors for determining structural integrity of well components)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lu, in view of Langnes, and in further view of Thompson, to change the operation parameter responsive to a notification of a specific sound event, in order to verify the analysis of well operations and take appropriate actions for ensuring well integrity and improve overall performance.
Conclusion
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/LINA CORDERO/Primary Examiner, Art Unit 2857