DETAILED ACTION
This office action is in response to communication filed on February 18, 2026.
Response to Amendment
Amendments filed on February 18, 2026 have been entered.
The specification has been amended.
Claims 1-7 have been amended.
Claims 1-7 have been examined.
Response to Arguments
Applicant’s arguments, see Remarks (p. 10), filed on 02/18/2026, 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addressing the informalities raised in the previous office action, the objections to the specification have been withdrawn.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), filed on 02/18/2026, 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to the claims addressing the informalities raised in the previous office action, the objections to the claims have been withdrawn.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 objections to the claims are presented below to address additional informalities.
Applicant’s arguments, see Remarks (p. 7-9), filed on 02/18/2026, with respect to the rejection of claims 1-7 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicant argues (p. 8) that the features of generating at least one prediction signals by processing at least one output signals, wherein the at least one prediction signals correspond to visually perceivable graphics-based signal displayable as a three-dimensional (3D) productivity volume for identifying at least one sweet spot location for the placement of the structure are not practically performed in the human mind. Specifically, it is at least not reasonable to assert that a human mind is capable of generating a prediction signal corresponding to a visually perceivable graphics-based signal displayable as a three-dimensional (3D) productivity volume. Therefore, the claimed invention should not be directed to a judicial exception.
This argument is not persuasive.
The examiner submits that the limitation “the processing portion is configured to process the at least one output signals to generate at least one prediction signals” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mathematical concepts to manipulate data and obtain additional information (i.e., at least one prediction signals; see specification at [0003], [0042], [0065], [0072]-[0073], [0086], [0091]; see analysis below for additional details).
Regarding the limitation “wherein the at least one prediction signals correspond to visually perceivable graphics-based signal displayable as a three-dimensional (3D) productivity volume for identifying at least one sweet spot location for the placement of the structure, thereby providing consideration for optimization of future structure placement and/or improvement of production of the structure”, the examiner submits that these features refer to extra-solution activities (e.g., displaying the predicted signal while appending an intended use - for identifying at least one sweet spot location for the placement of the structure, thereby providing consideration for optimization of future structure placement and/or improvement of production of the structure) (e.g., see MPEP 2106.04(d)(2): “If the limitation does not actually provide a treatment or prophylaxis, e.g., it is merely an intended use of the claimed invention or a field of use limitation, then it cannot integrate a judicial exception under the “treatment or prophylaxis” consideration. For example, a step of “prescribing a topical steroid to a patient with eczema” is not a positive limitation because it does not require that the steroid actually be used by or on the patient, and a recitation that a claimed product is a “pharmaceutical composition” or that a “feed dispenser is operable to dispense a mineral supplement” are not affirmative limitations because they are merely indicating how the claimed invention might be used”; see also MPEP 2106.05(h)).
Applicant also argues (p. 8) that the features of generating at least one prediction signals by processing at least one output signals, wherein the at least one prediction signals correspond to visually perceivable graphics-based signal displayable as a three-dimensional (3D) productivity volume for identifying at least one sweet spot location for the placement of the structure, as recited in amended claim 1, can provide a solution to an existing problem … By generating at least one prediction signals by processing at least one output signals, wherein the at least one prediction signals correspond to visually perceivable graphics-based signal displayable as a three-dimensional (3D) productivity volume for identifying at least one sweet spot location for the placement of the structure, the claimed invention can provide consideration for the system to optimize future structure placement and/or improve of production of the structure. Therefore, the claimed invention provides an improvement over existing system where optimization and/or design based on G&G and/or previous data may not be comprehensive/efficient.
This argument is not persuasive.
The examiner submits that, as indicated above, the argued features recite a judicial exception while also appending extra-solution activities and/or generally linking the use of a judicial exception to a particular technological environment, field of use or intended use, and as indicated in the MPEP: “Examples of limitations that the courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception include: … vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)” (see MPEP 2106.05(h)).
Also, 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’s arguments, see Remarks (p. 9-10), filed on 02/18/2026, with respect to the rejection of claims 1-7 under 35 U.S.C. 102(a)(1), 35 U.S.C. 102(a)(2) and 35 U.S.C. 103 have been fully considered but are moot in view of new grounds of rejection.
Applicant argues (p. 9) that Anderson appears to be entirely silent on at least the features of generating at least one prediction signals …
This argument is not persuasive.
The examiner submits that Anderson discloses the argued features by describing that the data, which includes production data and which is normalized, is processed by a machine learning optimizer to predict future results and prescribe actions to improve performance (see [0052], see rejection below for further details).
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim language “a processing portion communicatively coupled to the input portion, wherein the processing portion is configured to process the at least one output signals to generate at least one prediction signals,” should read “a processing portion communicatively coupled to the input portion, wherein the processing portion is configured to process the at least one output signal to generate at least one prediction signal,” in order to provide appropriate antecedence basis and clarify the recited subject matter.
Claim language “wherein the at least one prediction signals correspond to visually perceivable graphics-based signal displayable as a three-dimensional (3D) productivity volume for identifying at least one sweet spot location for the placement of the structure, thereby providing consideration for optimization of future structure placement and/or improvement of production of the structure” should read “wherein the at least one prediction signal correspond to a visually perceivable graphics-based signal displayable as a three-dimensional (3D) productivity volume for identifying at least one sweet spot location for [[the]] placement of the structure, thereby providing consideration for optimization of future structure placement and/or improvement of production of the structure” in order to provide appropriate antecedence basis and clarify the recited subject matter.
Appropriate correction is required.
Claim 2 is objected to because of the following informalities:
Claim language should read:
“The system of claim 1 further comprising:
a third module coupled to the second module, the at least one output signal being communicable to the third module from the second module for further transmission from the system to at least one device coupled to the system,
wherein the at least one output signal is receivable by the at least one device for machine-learning based processing” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 3 is objected to because of the following informalities:
Claim language should read:
“The system of claim 1, wherein the at least one output signal is based solely on normalization of the at least one reference signal based on the G&G based data” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 4 is objected to because of the following informalities:
Claim language should read:
“The system of claim 1, wherein the at least one output signal is based solely on normalization of the at least one reference signal based on the completion data” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 5 is objected to because of the following informalities:
Claim language should read:
“The system of claim 1, wherein the at least one reference signal includes production data associable with the structure” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 6 is objected to because of the following informalities:
Claim language should read:
“The system of claim 1, wherein the structure corresponds to a completed oil-well” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 7 is objected to because of the following informalities:
Claim language “processing, by a processing portion communicatively coupled to the input portion, the at least one output signals to generate at least one prediction signals” should read “processing, by a processing portion communicatively coupled to the input portion, the at least one output signal to generate at least one prediction signal” in order to provide appropriate antecedence basis and clarify the recited subject matter.
Claim language “wherein the at least one prediction signals correspond to visually perceivable graphics-based signal displayable as a three-dimensional (3D) productivity volume for identifying at least one sweet spot location for the placement of the structure, thereby providing consideration for optimization of future placement and/or improvement of production of the structure” should read “wherein the at least one prediction signal correspond to a visually perceivable graphics-based signal displayable as a three-dimensional (3D) productivity volume for identifying at least one sweet spot location for [[the]] placement of the structure, thereby providing consideration for optimization of future placement and/or improvement of production of the structure” in order to provide appropriate antecedence basis and clarify the recited subject matter.
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 1-7 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 machine/manufacture, which is one of the statutory categories of invention.
Continuing with the analysis, under Step 2A - Prong One of the test:
the limitation
“the second module configurable to process the at least one input signal and the at least one reference signal by manner of at least one of:
normalizing the at least one reference signal based on the G&G based data, and
normalizing the at least one reference signal based on the completion data,
to produce at least one output signal corresponding to at least one normalized reference signal”
is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mathematical concepts (i.e., normalization) to manipulate data and obtain a result (i.e., at least one output signal corresponding to at least one normalized reference signal; see specification at [0010]-[0012], [0016], [0027]-[0028], [0041], [0048], [0051]-[0058], [0084]-[0085]). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated), the particular technological environment or field of use, and the generic computer elements (i.e., second module, see specification at [0041], [0046], [0048]), the limitation in the context of the claim mainly refers to applying mathematical concepts to transform data.
the limitation “the processing portion is configured to process the at least one output signals to generate at least one prediction signals” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mathematical concepts to manipulate data and obtain additional information (i.e., at least one prediction signals; see specification at [0003], [0042], [0065], [0072]-[0073], [0086], [0091]). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated), the particular technological environment or field of use, and the generic computer elements/implementation (i.e., processing portion, see specification at [0065]), the limitation in the context of the claim mainly refers to applying mathematical concepts to transform data.
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 system comprising:
one or more apparatuses comprising:
a first module configurable to:
at least one of receive at least one input signal and generate the at least one input signal, the at least one input signal including:
geology and geophysics (G&G) based data; and
completion data associable with a structure,
at least one of receive at least one reference signal and generate the at least one reference signal; and
a second module coupled to the first module, and
one or more devices communicatively coupled to the one or more apparatuses comprising:
an input portion configured to receive the at least one output signal;
a processing portion communicatively coupled to the input portion, wherein,
wherein the at least one prediction signals correspond to visually perceivable graphics-based signal displayable as a three-dimensional (3D) productivity volume for identifying at least one sweet spot location for the placement of the structure, thereby providing consideration for optimization of future structure placement and/or improvement of production of the structure”
add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see specification at [0041], [0046]-[0050], [0060]-[0068]) (see MPEP 2106.05(f)); and
generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)) and/or add extra-solution activities (e.g., mere data gathering/outputting for intended use, source/type of data to be manipulated) (see MPEP 2106.05(g)).
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:
append generic computer components (see specification at [0041], [0046]-[0050], [0060]-[0068]) used to facilitate the application of the abstract idea (i.e., mere computer implementation), which as indicated in the MPEP: “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more” (see MPEP 2106.05(f), item 2) and “Courts have held computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking)” (see MPEP 2106.05(d), section II);
generally link the use of the judicial exception to a particular technological environment or field of use (e.g., geology, geophysics), 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” (see MPEP 2106.05(h)); and
recite extra-solution activities (i.e., mere data gathering/outputting for intended use by selecting a particular data source/type to be manipulated) using elements specified at a high level of generality, which as indicated in the MPEP: “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 provide significantly more” (see MPEP 2106.05(b), section III).
The claim, when considered as a whole, does not provide significantly more under Step 2B of the test.
Based on the analysis, the claim is not patent eligible.
Similarly, independent claim 7 is 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 claims by additional limitations (Claims 3-4), that under the broadest reasonable interpretation in light of the specification, cover performance of the limitations using mathematical concepts, and
the additional elements recited in the dependent claims, when considered individually and in combination, add extra-solution activities (e.g., mere data gathering/transmission using a data type or source) and/or append generic computer components (Claims 2 and 5-6), 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).
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 1-7 are rejected under 35 U.S.C. 103 as being unpatentable over Anderson (US 20170364795 A1), hereinafter ‘Anderson’, in view of Roth (US 20190361146 A1), hereinafter ‘Roth’.
Regarding claim 1.
Anderson discloses:
A system (Figs. 1-2, item 1000 – “PALM system”; [0051]: A Petroleum Analytics Learning Machine (or PALM) system is used for maximizing production from wells (see [0002])) comprising:
one or more apparatuses (Figs. 1-2; [0003]-[0004]: PALM system comprises multiple subsystems and components) comprising:
a first module (Figs. 1-2, item 1300 – “SID”; [0004], [0051]: a System Integration Database (SID) is part of the PALM system) configurable to:
at least one of receive at least one input signal and generate the at least one input signal, the at least one input signal including: geology and geophysics (G&G) based data; and completion data associable with a structure ([0004]-[0005], [0011]: the System Integration Database (SID) receives data from field devices, the data including geology, geophysics and completion data (see also [0061], [0063])),
at least one of receive at least one reference signal and generate the at least one reference signal ([0004]-[0005], [0011]: the System Integration Database (SID) receives data from field devices, the data including production data (see also [0064])); and
a second module (Figs. 1-2, item 1300 – ‘SID’; [0004], [0051]: the System Integration Database (SID) is part of the PALM system), the second module configurable to process the at least one input signal and the at least one reference signal by manner of at least one of: normalizing the at least one reference signal based on the G&G based data, and normalizing the at least one reference signal based on the completion data, to produce at least one output signal corresponding to at least one normalized reference signal ([0005], [0011]: data including production data is retrieved, compared and combined into a uniform data repository using normalization based on unique identifiers which include geology, geophysics and completion data (see also [0072] regarding production data being normalized by flow days and for perforated lateral length)), and
one or more devices (Figs. 1-2, item 1100 – “PALM processor”) communicatively coupled to the one or more apparatuses ([0051]: PALM processor is coupled to other subsystems and components in the PALM system) comprising:
an input portion configured to receive the at least one output signal (Fig. 2; [0052]: data from SID is received by PALM processor, which implies an input portion in the PALM processor to receive data from SID);
a processing portion (Fig. 2, item 1400 – “PALM machine learning optimizer”) communicatively coupled to the input portion (Fig. 2; [0052]: data from SID is received by PALM processor for analysis by the PALM machine learning optimizer, which implies communication coupling among the PALM processor components or portions), wherein the processing portion is configured to process the at least one output signals to generate at least one prediction signals ([0052], [0060]: PALM processor uses the machine learning optimizer to predict future results and prescribe actions to improve performance based on the normalized production data (see [0005], [0011], [0056], [0083]-[0084])).
Anderson does not explicitly disclose (see italic text):
a second module coupled to the first module; and
wherein the at least one prediction signals correspond to visually perceivable graphics-based signal displayable as a three-dimensional (3D) productivity volume for identifying at least one sweet spot location for the placement of the structure, thereby providing consideration for optimization of future structure placement and/or improvement of production of the structure.
Roth teaches:
“In particular, the memory 128 may store instructions for execution by the processor that, when executed, cause the processor to receive data from one or more sources, normalize the data as necessary, make a prediction based on the data, compare the prediction to actual results, and adjust a prediction algorithm that was used to make the prediction based on the comparison so that future predictions are made using the adjusted prediction algorithm. The instructions thus allow the processor not only to make predictions based on available data, but also to utilize machine learning to improve future predictions based on a comparison of past predictions to actual results” ([0128]: a processor (analogous to second module) receives data from data sources (analogous to coupled to the first module), the processing including normalization of data);
“A method of predicting well production according to yet another embodiment of the present disclosure comprises: receiving at a processor, via a network interface and from a plurality of information storage sources, received information about a plurality of wells in a defined geographic area, the received information comprising well location data, fracking data, production test data, completion data, production data, and directional survey data; detecting, with the processor, gaps within the received information, each gap corresponding to a missing data point; generating, with the processor, a predicted data point corresponding to each missing data point using a mapping-set based machine learning technique; substituting, with the processor, the gaps with the corresponding predicted data points to yield quality-controlled received information; generating, with the processor, for each well in the plurality of wells and based on the quality-controlled received information, a plurality of attributes; generating, with the processor and based on the quality-controlled received information and the plurality of attributes, and with reference to a set of rules defining characteristics of geologic layers or formations, a structural model corresponding to the defined geographic area; analyzing, with the processor, the structural model to yield a result comprising one or more of (i) an optimal design for a new well at a specified location within the defined geographic area; (ii) an optimal number of new wells for the defined geographic area to maximize production from the defined geographic area; and (iii) a predicted performance of a new well at a specified location within the defined geographic area and having a specified design; and transmitting, from the processor, instructions for displaying a graphical depiction of the result” ([0015]: predicting well production includes collecting data including completion and production data, and analyzing these data for generating a structural model (3D model, see [0016], [0211], [0224]) to yield optimal design for a new well, optimal number of new wells to maximize production and a predicted performance of the new well (see also Abstract, Figs. 69-71, [0292], [0294]-[0295])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Anderson in view of Roth, to incorporate the second module coupled to the first module, and to incorporate the at least one prediction signals corresponding to visually perceivable graphics-based signal displayable as a three-dimensional (3D) productivity volume for identifying at least one sweet spot location for the placement of the structure, thereby providing consideration for optimization of future structure placement and/or improvement of production of the structure, in order to provide a robust and flexible system that accesses/stores different field information using a first component (i.e., first module) and performs analysis of the data using a second component (i.e., second module), while also allowing users to prioritize the expenditure of resources on wells that are most likely to be the most productive, as discussed by Roth (abstract).
Regarding claim 2.
Anderson in view of Roth discloses all the features of claim 1 as described above.
Anderson further discloses:
a third module (Figs. 1-2, item 1400 – “PALM Machine Learning Optimizer”; [0051]: the machine learning optimizer is part of the PALM system) coupled to the second module (see Figs. 1-2), the at least one output signal being communicable to the third module from the second module ([0005]: normalized data is processed to identify importance weights (see also Abstract, [0053])) for further transmission from the apparatus to at least one device (Figs. 1-2, item 1200 – “MAP Subsystems”; [0004], [0051]: machine analytics products subsystems retrieve integrated data from the SID (see Fig. 2 implying data transmission going through PALM Machine Learning Optimizer)) coupled to the apparatus (see Figs. 1-2),
wherein the at least one output signal is receivable by the at least one device for machine-learning based processing ([0004]: MAP subsystems perform machine learning on the integrated data for production optimization).
Regarding claim 3.
Anderson in view of Roth discloses all the features of claim 1 as described above.
Anderson further discloses:
the at least one output signal is based solely on normalization of the at least one reference signal based on the G&G based data ([0011]: data including production data is retrieved, compared and combined into a uniform data repository using normalization based on at least one unique identifier which includes geology and geophysics data, which implies using only geology and geophysics data for normalization).
Regarding claim 4.
Anderson in view of Roth discloses all the features of claim 1 as described above.
Anderson further discloses:
the at least one output signal is based solely on normalization of the at least one reference signal based on the completion data ([0011]: data including production data is retrieved, compared and combined into a uniform data repository using normalization based on at least one unique identifier which includes completion data, which implies using only completion data for normalization).
Regarding claim 5.
Anderson in view of Roth discloses all the features of claim 1 as described above.
Anderson further discloses:
the at least one reference signal includes production data associable with the structure ([0005], [0009], [0011]: the System Integration Database (SID) receives data from field devices, the data including production data from wells (see also [0002] and [0064])).
Regarding claim 6.
Anderson in view of Roth discloses all the features of claim 1 as described above.
Anderson further discloses:
the structure corresponds to a completed oil-well ([0005], [0009], [0011]: the System Integration Database (SID) receives data from field devices, the data including production data from oil wells (see [0002]), which implies the wells to be completed (see also [0022], [0064], [0077] and [0083])).
Regarding claim 7.
Anderson discloses:
A processing method suitable for analytics based on at least one of geology and geophysics (G&G) based data and completion data ([0002], [0011]: a Petroleum Analytics Learning Machine (or PALM) method is used for maximizing production from wells based on geology, geophysics and completion data (see also [0061], [0063])), the processing method comprising:
at least one of generating and receiving, by a first module (Figs. 1-2, item 1300 – “SID”; [0004], [0051]: a System Integration Database (SID) is part of the PALM system) of one or more apparatuses (Figs. 1-2; [0003]-[0004]: PALM system comprises multiple subsystems and components), at least one input signal, the at least one input signal including at least one of: the geology and geophysics (G&G) based data; and the completion data associable with a structure ([0004]-[0005], [0011]: a System Integration Database (SID) receives data from field devices, the data including geology, geophysics and completion data (see also [0061], [0063]));
at least one of generating and receiving, by the first module, at least one reference signal ([0004]-[0005], [0011]: the System Integration Database (SID) receives data from field devices, the data including production data (see also [0064]));
processing, by a second module (Figs. 1-2, item 1300 – ‘SID’; [0004], [0051]: the System Integration Database (SID) is part of the PALM system) of the one or more apparatuses, the at least one input signal and the at least one reference signal by manner of at least one of: normalizing the at least one reference signal based on the G&G based data, and normalizing the at least one reference signal based on the completion data, to produce at least one output signal corresponding to at least one normalized reference signal ([0005], [0011]: data including production data is retrieved, compared and combined into a uniform data repository using normalization based on unique identifiers which include geology, geophysics and completion data (see also [0072] regarding production data being normalized by flow days and for perforated lateral length)),
receiving, by an input portion of one or more devices (Figs. 1-2, item 1100 – “PALM processor”) communicatively coupled to the one or more apparatuses ([0051]: PALM processor is coupled to other subsystems and components in the PALM system), the at least one output signal (Fig. 2; [0052]: data from SID is received by PALM processor, which implies an input portion in the PALM processor to receive data from SID); and
processing, by a processing portion (Fig. 2, item 1400 – “PALM machine learning optimizer”) communicatively coupled to the input portion (Fig. 2; [0052]: data from SID is received by PALM processor for analysis by the PALM machine learning optimizer, which implies communication coupling among the PALM processor components or portions), the at least one output signals to generate at least one prediction signals ([0052], [0060]: PALM processor uses the machine learning optimizer to predict future results and prescribe actions to improve performance based on the normalized production data (see [0005], [0011], [0056], [0083]-[0084])).
Anderson does not explicitly disclose (see italic text):
wherein the at least one prediction signals correspond to visually perceivable graphics-based signal displayable as a three-dimensional (3D) productivity volume for identifying at least one sweet spot location for the placement of the structure, thereby providing consideration for optimization of future placement and/or improvement of production of the structure.
Roth teaches:
“A method of predicting well production according to yet another embodiment of the present disclosure comprises: receiving at a processor, via a network interface and from a plurality of information storage sources, received information about a plurality of wells in a defined geographic area, the received information comprising well location data, fracking data, production test data, completion data, production data, and directional survey data; detecting, with the processor, gaps within the received information, each gap corresponding to a missing data point; generating, with the processor, a predicted data point corresponding to each missing data point using a mapping-set based machine learning technique; substituting, with the processor, the gaps with the corresponding predicted data points to yield quality-controlled received information; generating, with the processor, for each well in the plurality of wells and based on the quality-controlled received information, a plurality of attributes; generating, with the processor and based on the quality-controlled received information and the plurality of attributes, and with reference to a set of rules defining characteristics of geologic layers or formations, a structural model corresponding to the defined geographic area; analyzing, with the processor, the structural model to yield a result comprising one or more of (i) an optimal design for a new well at a specified location within the defined geographic area; (ii) an optimal number of new wells for the defined geographic area to maximize production from the defined geographic area; and (iii) a predicted performance of a new well at a specified location within the defined geographic area and having a specified design; and transmitting, from the processor, instructions for displaying a graphical depiction of the result” ([0015]: predicting well production includes collecting data including completion and production data, and analyzing these data for generating a structural model (3D model, see [0016], [0211], [0224]) to yield optimal design for a new well, optimal number of new wells to maximize production and a predicted performance of the new well (see also Abstract, Figs. 69-71, [0292], [0294]-[0295])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Anderson in view of Roth, to incorporate the at least one prediction signals corresponding to visually perceivable graphics-based signal displayable as a three-dimensional (3D) productivity volume for identifying at least one sweet spot location for the placement of the structure, thereby providing consideration for optimization of future placement and/or improvement of production of the structure, in order to allow users to prioritize the expenditure of resources on wells that are most likely to be the most productive, as discussed by Roth (abstract).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hou; Lianhua et al., US 20200211126 A1, PREDICTION METHOD FOR SHALE OIL AND GAS SWEET SPOT REGION, COMPUTER DEVICE AND COMPUTER READABLE STORAGE MEDIUM
Reference discloses predicting shale oil and gas sweet spots based on obtaining oil and gas content, fluidity and compressibility parameters and a prediction model.
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).
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/LINA CORDERO/Primary Examiner, Art Unit 2857
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