DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
The following is a non-final, first office action in response to the communication filed on 01/24/2023. Claims 1—20 are currently pending.
Priority
The Applicant’s claim for benefit of US Provisional Patent Application 63/302,322 filed on 01/24/2022, has been received and acknowledged.
Information Disclosure Statement
Information Disclosure Statement received 10/19/2023 and 02/23/2026 have been reviewed and considered.
Drawings
Figures 5—9 are objected to for including excessive shading in some portions of the drawings while lacking clear/crisp lines in other portions of the drawings. The current rendering of FIGs. 5—9 is borderline illegible to fully illegible in some sections due to the shading a lack of clear lines. The manner in which the figures will render in publication will likely compound the legibility/clarity issue currently present in FIGs. 5—9. As such, new corrected drawings in compliance with 37 CFR 1.121(d) are required in this application due to the above mentioned legibility issue. Applicant is advised to employ the services of a competent patent draftsperson outside the Office, as the U.S. Patent and Trademark Office no longer prepares new drawings. The corrected drawings are required in reply to the Office action to avoid abandonment of the application. The requirement for corrected drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3, 9, and 12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3, which depends from claim 1, recites the limitation “training a machine learning model based on historical static data and historical dynamic data.” Claim 4, which also depends from claim 1 recites the limitation “training a machine learning model based on the static data and the dynamic data.” Under the broadest reasonable interpretation, any data capable of being used to train a machine learning model implicitly constitutes “historical” data insofar as the data has to exist before it can be used to train a model. Moreover, neither the claim limitations nor the Specification require that the term “historical… data” has a more specific interpretation than “data that already exists.” As such, any available data which is capable of being used to generate a model would implicitly be classified as “historical.” For the foregoing reasons the metes and bounds of the terms “historical static data” and “historical dynamic data” in view of the terms “static data” and “dynamic data” are unclear thereby rendering claim 3 indefinite. Claim 12 recites substantially similar limitations and is rejected under 35 U.S.C. 112(b) for the same reasons as provided with respect to claim 3.
Claim 9 recites the limitation “wherein the static data and the dynamic data respectively are consecutive data to historical static data and historical dynamic data.” The foregoing limitation is indefinite because the relationship between the static data and the historical static data is unclear. Moreover, in view of claim 9 being directed to a method, the manner in which the static/dynamic data is consecutive to the historical static/dynamic data is unclear in the limitation as claimed.
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—20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 of the USPTO’s eligibility analysis entails considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter.
Claims 1, 10, and 18 are directed to a method (process), a system (machine or manufacture), and a system (machine or manufacture), respectively. As such, the claims are directed to statutory categories of invention.
If the claim recites a statutory category of invention, the claim requires further analysis in Step 2A. Step 2A of the 2019 Revised Patent SUBJECT Matter Eligibility Guidance is a two-prong inquiry. In Prong One, examiners evaluate whether the claim recites a judicial exception
Claims 1, 10, and 18 recite the limitations of, or substantially similar to: “receiving static data of a reservoir from a spatio-temporal database” (e.g., a mental process); “receiving dynamic data of the reservoir from a spatio-temporal database” (e.g., a mental process); “processing the static data and the dynamic data to identify centroids of wells in the reservoir” (e.g., a mental process and/or mathematical concept); “generating polygons based on the centroids of the well” (e.g., a mental process and/or mathematical concept); and “generating a reservoir model based on at least one of the polygons, static data, or dynamic data” (e.g., a mental process and/or mathematical concept).
Under the broadest reasonable interpretation, the above identified limitations are directed to processes which are either performable in a human mind and/or are directed to a mathematical concept. For example, a human mind is capable of receiving dynamic and static data from a database if the data is presented on a screen. A human mind, with or without the benefit of a mathematical concept, is capable of processing and generating locational data such as well centroids and drainage areas. Finally, a human mind, with or without the benefit of a mathematical concept, is capable of generating a reservoir model, where a reservoir model also constitutes an abstract idea. Examiner submits that nothing in the claim element precludes the aforementioned steps from practically being performed in the human mind, or by a human using pen and paper and/or a mathematical concept. Furthermore, the mere recitation of generic computing elements and/or sensors does not take the claim out of the mental process grouping. Thus the claim recites an abstract idea.
If the claim recites a judicial exception (i.e., an abstract idea enumerated in Section I of the 2019 Revised Patent Subject Matter Eligibility Guidance, a law of nature, or a natural phenomenon), the claim requires further analysis in Prong Two. In Prong Two, examiners evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
Claims 1, 10, and 18 recite the additional element of “a spatio-temporal database” (e.g., extra-solution activity);
Claim 10 further recites “non-transitory computer readable media” (e.g., merely using a computer to perform the method is equivalent to reciting “apply it”); and
Claim 18 further recites “a storage… a processor” (e.g., mere recitation of a generic computer to perform the method is equivalent to reciting “apply it”).
The above identified limitations constitute additional elements. However, for the reasons identified above and further discussed below, the additional elements do not impose any meaningful limits on practicing the abstract idea. As such, the above identified additional elements do not integrate the above identified judicial exceptions into a practical application.
If the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception, and requires further analysis under Step 2B to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).
As identified above, claims 1, 10 and 18 recite the additional element of “a spatio-temporal database,” which constitutes court-identified insignificant extra-solution activity as discussed in MPEP 2106.05(g). For example, the MPEP states “[b]elow are examples of activities that the courts have found to be insignificant extra-solution activity: Selecting a particular data source or type of data to be manipulated: i. Limiting a database index to XML tags, Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d at 1328-29, 121 USPQ2d at 1937;… iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).” (MPEP 2106.05(g), Section 3). As such, the mere recitation of a database which is limited to one or more data types amounts to insignificant extra-solution activity and cannot provide for a practical application of the above identified judicial exceptions.
Claim 10 recites the additional element of “non-transitory computer readable media” which is equivalent to a limitation stating that the judicial exception is applied using generic computing components. The recitation of generic computing components which are merely used to apply the judicial exception cannot provide for a practical application of the judicial exceptions. Moreover, such limitations are equivalent to reciting “apply it,” as discussed in MPEP 2106.05(f) which states “[u]se 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 integrate a judicial exception into a practical application or provide significantly more.” (MPEP 2106.05(f)), Section 2). As such, limitations directed to the inclusion of generic computing components does not show integration of the judicial exception into a practical application.
Claim 18 recites the additional elements of “a storage… a processor” which is equivalent to a limitation stating that the identified judicial exceptions of claim 18 are applied using generic computing components. The recitation of generic computing components which are merely used to apply the judicial exception cannot provide for a practical application of the judicial exceptions. Moreover, such limitations are equivalent to reciting “apply it,” as discussed in MPEP 2106.05(f) which states “[u]se 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 integrate a judicial exception into a practical application or provide significantly more.” (MPEP 2106.05(f)), Section 2). As such, limitations directed to the inclusion of generic computing components does not show integration of the judicial exception into a practical application.
Thus, even when viewed as an ordered combination, nothing in the claims add significantly more (i.e., an inventive concept) to the abstract idea.
Claims 2, 11, and 19 recite the limitation “inputting the static data and the dynamic data into a machine learning model,” which is directed to a mathematical concept and therefore directed to an abstract idea. Since claim 2 does not recite any additional elements which integrate the identified abstract ideas of claims 1 and 2 into a practical application, claim 2 is directed to an abstract idea without significantly more.
Claims 3, 4, 12, 13, and 20 are directed to training a machine learning model using generically recited datasets. Examiner notes that training a machine learning model constitutes an additional element; however, the limitations directed to the training are recited at such a high level of generality such that the limitations do not impose any meaningful limits on practicing the abstract idea. Moreover, generically recited machine learning training steps amount to mere instructions to apply the judicial exception. To this end the MPEP states “examiners may consider the following: (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words ‘apply it'. See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016);… In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.” (MPEP 2106.05(f), Section 1). As such, while the limitations of claims 3, 4, 12, 13, and 20 are directed to additional elements, the additional elements do not provide for a practical application of the judicial exception.
The limitations of claims 5—9 and 14—17 are directed to further defining the type of data included in a dataset, where the dataset is either provided as input to the model or is used to generate the model. For example, the claims recite the following limitations, or limitations substantially similar thereto, directed to the data: “wherein the spatio-temporal database associates the static data and the dynamic data with a point in time”; “the spatio-temporal database includes data of the reservoir from a plurality of points in time”; “the spatio-temporal database includes a simulation grid and a polygon grid”; “wherein the static data and dynamic data include both field data and data obtained in a simulation of the reservoir”; and “the static data and the dynamic data respectively are consecutive data to historical static data and historical dynamic data.”
Limitations which further define a dataset, where the dataset is used to perform an abstract idea, constitutes court-identified insignificant extra-solution activity as discussed in MPEP 2106.05(g). For example, the MPEP states “[b]elow are examples of activities that the courts have found to be insignificant extra-solution activity: Selecting a particular data source or type of data to be manipulated: i. Limiting a database index to XML tags, Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d at 1328-29, 121 USPQ2d at 1937;… iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).” (MPEP 2106.05(g), Section 3). As such, limitations which further define the dataset used to perform the abstract idea cannot provide for a practical application of the above identified judicial exceptions.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1—20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Published US Patent Application to Benhallam et al., hereinafter “Benhallam,” (US 20190325331 A1).
Regarding claim 1, Benhallam discloses [a] method for reservoir modeling, the method comprising: receiving static data of a reservoir (para. [0051], “[t]he communications module 104 may receive data from a hydrocarbon extraction site 130 (e.g. from sensors running within or around the wells 131). The data may include petrophysical data 135, geological data 136, engineering data 137 or other data related to the production of hydrocarbons at the site 130.”; para. [0118], “the data accessing module 107 may access petrophysical log data 135, geological log data 136, and/or engineering data 137 related to the hydrocarbon extraction site 130. This data may be received directly from the site, or may be retrieved from a data store such as data store 140.”) from a spatio-temporal database (data store 140 includes petrophysical log data 135, geological log data 136, engineering data 137, historical completion data 141, and/or production data 142);
receiving dynamic data of the reservoir (para. [0051], “[t]he communications module 104 may receive data from a hydrocarbon extraction site 130 (e.g. from sensors running within or around the wells 131). The data may include petrophysical data 135, geological data 136, engineering data 137 or other data related to the production of hydrocarbons at the site 130.”; para. [0118], “the data accessing module 107 may access petrophysical log data 135, geological log data 136, and/or engineering data 137 related to the hydrocarbon extraction site 130. This data may be received directly from the site, or may be retrieved from a data store such as data store 140.”) from a spatio-temporal database (data store 140 includes petrophysical log data 135, geological log data 136, engineering data 137, historical completion data 141, and/or production data 142);
processing the static data and the dynamic data to identify centroids of wells in the reservoir (para. [0010], “[t]he computer system also includes a drainage analyzer configured to identify zones of the hydrocarbon extraction site that include drained portions that have been drained by existing wells on the site, along with a 3D map generator configured to generate a relative probability of success (RPOS) 3D map that ranks the identified zones of the site according to zone-mappable attributes associated with productive hydrocarbon wells.”; para. [0033], “the computer system analyzes isolated and connected intervals of the hydrocarbon wells to determine, according to the geological map, which portions of the hydrocarbon wells have been drained by existing completions.”; para. [0088], “[a] 3D spacing analysis module 1201 flags no-go cells based on spacing to avoid collisions with existing wells. 3. A volumetric production analysis module 1202 identifies cells drained by existing wells.” The existing well locations are required input order to determine the drainage shapes around the wells.);
generating polygons (drainage grid) based on the centroids of the well (para. [0116], “[s]till further, analyzing isolated and connected intervals of the hydrocarbon wells to identify drainage may include creating a drainage grid. Within this drainage grid, each formation uses the production volume or estimated ultimate recovery allocated to the formation in each hydrocarbon well.” See also the citations to para. [0010], [0033], and [0088] as provided above. The system of Benhallam includes a drainage analyzer 109 which identifies the spatial areas and/or volumes around the wells which have been drained/depleted. Determining a drainage area on a cell-by-cell basis (e.g., see para. [0088]) for a gridded reservoir/geospatial model is equivalent to generating a polygon), and
generating a reservoir model based on at least one of the polygons, static data, or dynamic data, wherein the reservoir model (see methods 1700 and 1900 of FIGs. 17 and 19. As depicted in the workflow, the static and dynamic data as identified above is taken as an input to identify the drainage information where the static data, dynamic data, and drainage information is used to forecast well production in order to identify recompletion opportunities or underdeveloped areas where new drilling can occur) includes reservoir performance (e.g., interference analysis at step 1970 of FIG. 19), field development (e.g., selecting and analyzing candidate wells described throughout method 1900 including steps 1950 and 1960 of FIG. 19), production metrics (e.g., production forecasts), and operation metrics (calculating geologic uncertainty 118 at step 1750 of method 1700; para. [0013], “[t]he uncertainty calculation module 117 may then calculate a level of geologic uncertainty 118 relative to the forecasted production results 114 and determined drainage 110 (1750). The geologic uncertainty levels 118 indicate levels of risk 113. The risk level accounts for various unknowns in the drilling process including how much material is actually remaining, how many of the wells are interconnected, how many different rock formations exist, different pressures, the presence of other materials, etc. Many factors may make placing a well in a given zone 134 more or less risky in terms of ultimate production from the well. In some cases, calculating the level of geologic uncertainty 118… may include identifying structural risks, mapping risks, petrophysical risks, saturation risks or other types of risk.”).
Regarding claim 2, Benhallam discloses wherein processing the static data and the dynamic data includes inputting the static data (geological data listed in log-based attributes and geo-engineering attributes as described in Table 3 of para. [0098]) and the dynamic data (production-related attributes listed in geo-engineering attributes and the target attributes) into a machine learning model (para. [0098], “Artificial Neural Networks (ANN's) are used to develop a model that relates the production performance to a series of data attributes including petrophysical interpretations, geological properties, and various engineering attributes. The training set used to train the model include past completion intervals, and the training attributes are derived or calculated specifically over these intervals.”).
Regarding claim 3, Benhallam discloses training a machine learning model (para. [0098], “Artificial Neural Networks (ANN's) are used to develop a model that relates the production performance to a series of data attributes including petrophysical interpretations, geological properties, and various engineering attributes. The training set used to train the model include past completion intervals, and the training attributes are derived or calculated specifically over these intervals.”) based on historical static data (geological data listed in log-based attributes and geo-engineering attributes as described in Table 3 of para. [0098]) and historical dynamic data (production-related attributes listed in geo-engineering attributes and the target attributes) Examiner notes that, under the broadest reasonable interpretation, any data which is capable of being used to train a machine learning model must inherently exist prior to training the machine learning model. Therefore, such data constitutes “historical,” data relative to the machine learning model insofar as the data must pre-date the model).
Regarding claim 4, Benhallam discloses training a machine learning model (para. [0098], “Artificial Neural Networks (ANN's) are used to develop a model that relates the production performance to a series of data attributes including petrophysical interpretations, geological properties, and various engineering attributes. The training set used to train the model include past completion intervals, and the training attributes are derived or calculated specifically over these intervals.”) based on the static data (geological data listed in log-based attributes and geo-engineering attributes as described in Table 3 of para. [0098]) and the dynamic data (production-related attributes listed in geo-engineering attributes and the target attributes).
Regarding claim 5, Benhallam discloses wherein the static data and the dynamic data are stored in the spatio-temporal database (as noted in claim 1, data store 140 includes petrophysical log data 135, geological log data 136, engineering data 137, historical completion data 141, and/or production data 142. These datasets constitute both static data and dynamic data as discussed in claim 1), and wherein the spatio-temporal database associates the static data and the dynamic data with a point in time (production data 142, which is implicitly understood to be time-series data, is associated with both 1.) a point in time and 2.) the well from which is was produced. Likewise, any geological data/static data collected from the well is associated with the well. Furthermore, the geological data is associated with a point in time by way of the mutual association between the well and the production data. Additionally, the geological data is implicitly associated with the point in time at which it is collected).
Regarding claim 6, Benhallam discloses wherein the spatio-temporal database includes data of the reservoir from a plurality of points in time (para. [0096], “[s]tep 1205 forecasts production for each of the identified and validated targets using a range of different techniques. These include statistical methods that leverage spatial and temporal neighborhood fluid data, machine learning techniques like supervised neural networks, analytical models specifically designed for horizontal wells, and simulation models.” As noted in claim 1, data store 140 includes petrophysical log data 135, geological log data 136, engineering data 137, historical completion data 141, and/or production data 142. The production data includes production rates which constitute reservoir data associated with points in time.).
Regarding claim 7, Benhallam discloses wherein the spatio-temporal database includes a simulation grid (para. [0035], “[t]he computer system accesses geological, petrophysical or engineering data related to a hydrocarbon extraction site, and analyzes the accessed data to identify well placement grid cells in the hydrocarbon extraction site that are fit for placing new wells according to well placement constraints.”) and a polygon grid (para. [0116], “[s]till further, analyzing isolated and connected intervals of the hydrocarbon wells to identify drainage may include creating a drainage grid. Within this drainage grid, each formation uses the production volume or estimated ultimate recovery allocated to the formation in each hydrocarbon well.” As depicted in FIG. 1, all of the data modeled in computer system 101 may be stored in Data Store 140 and all of the data stored in Data Store 140 may be included in the model), wherein the simulation grid is associated with the static and dynamic data (as identified in claim 1, the static and dynamic data includes the geological, petrophysical, and engineering data. As identified above in para. [0035], this data is modeled in a grid in order to generate a geo-reservoir model which is used to identify new drilling locations), and wherein the polygon grid is associated with at least one of drainage areas or irrigation areas of the reservoir (see citations to para. [0010], [0033], [0088], and [0116]).
Regarding claim 8, Benhallam discloses wherein the static data and dynamic data include both field data (para. [0058], “well surveys, well logs, well formation tops, completion and perforation data, production data, fluid contacts data, static geologic models”) and data obtained in a simulation of the reservoir (para. [0057], “[t]he platform implements inter-disciplinary field data including well surveys, well logs, well formation tops, completion and perforation data, production data, fluid contacts data, static geologic models, simulation models, and other types of data.”).
Regarding claim 9, and as addressed above in the rejection under 35 U.S.C. 112(b), Examiner notes the claim limitations do not succeed in differentiating the static/dynamic data from the historical static/dynamic data. Moreover, under the broadest reasonable interpretation, any data which has already been measured, witnessed, determined, realized, known, or stored, constitutes historical data. As such, and given the broad scope in which the datasets are claimed, any arbitrary time point selected within the dataset of Benhallam (e.g., including production data which implicitly includes time-series data) may be used as a datum to separate the data which reads on the limitations of claim 9.
Regarding claim 10, Benhallam discloses [o]ne or more non-transitory computer readable media storing instructions, the instructions, when executed by a computing system (para. [0050], “[t]he computer architecture 100 includes a computer system 101. The computer system 101 includes at least one processor 102 and at least some system memory 103. The computer system 101 may be any type of local or distributed computer system, including a cloud computer system. The computer system 101 includes modules for performing a variety of different functions. The program modules may be hardware-based, software-based, or a combination thereof. Each program module uses computing hardware and/or software to perform functions including those defined herein below.”), causing the computing system to:
receive static data of a reservoir (para. [0051], “[t]he communications module 104 may receive data from a hydrocarbon extraction site 130 (e.g. from sensors running within or around the wells 131). The data may include petrophysical data 135, geological data 136, engineering data 137 or other data related to the production of hydrocarbons at the site 130.”; para. [0118], “the data accessing module 107 may access petrophysical log data 135, geological log data 136, and/or engineering data 137 related to the hydrocarbon extraction site 130. This data may be received directly from the site, or may be retrieved from a data store such as data store 140.”) from a spatio-temporal database (data store 140 includes petrophysical log data 135, geological log data 136, engineering data 137, historical completion data 141, and/or production data 142);
receive dynamic data of the reservoir (para. [0051], “[t]he communications module 104 may receive data from a hydrocarbon extraction site 130 (e.g. from sensors running within or around the wells 131). The data may include petrophysical data 135, geological data 136, engineering data 137 or other data related to the production of hydrocarbons at the site 130.”; para. [0118], “the data accessing module 107 may access petrophysical log data 135, geological log data 136, and/or engineering data 137 related to the hydrocarbon extraction site 130. This data may be received directly from the site, or may be retrieved from a data store such as data store 140.”) from a spatio-temporal database (data store 140 includes petrophysical log data 135, geological log data 136, engineering data 137, historical completion data 141, and/or production data 142);
process the static data and the dynamic data to identify centroids of wells in the reservoir (para. [0010], “[t]he computer system also includes a drainage analyzer configured to identify zones of the hydrocarbon extraction site that include drained portions that have been drained by existing wells on the site, along with a 3D map generator configured to generate a relative probability of success (RPOS) 3D map that ranks the identified zones of the site according to zone-mappable attributes associated with productive hydrocarbon wells.”; para. [0033], “the computer system analyzes isolated and connected intervals of the hydrocarbon wells to determine, according to the geological map, which portions of the hydrocarbon wells have been drained by existing completions.”; para. [0088], “[a] 3D spacing analysis module 1201 flags no-go cells based on spacing to avoid collisions with existing wells. 3. A volumetric production analysis module 1202 identifies cells drained by existing wells.” The existing well locations are required input order to determine the drainage shapes around the wells.);
generate polygons (drainage grid) based on the centroids of the wells in the reservoir (para. [0116], “[s]till further, analyzing isolated and connected intervals of the hydrocarbon wells to identify drainage may include creating a drainage grid. Within this drainage grid, each formation uses the production volume or estimated ultimate recovery allocated to the formation in each hydrocarbon well.” See also the citations to para. [0010], [0033], and [0088] as provided above. The system of Benhallam includes a drainage analyzer 109 which identifies the spatial areas and/or volumes around the wells which have been drained/depleted. Determining a drainage area on a cell-by-cell basis (e.g., see para. [0088]) for a gridded reservoir/geospatial model is equivalent to generating a polygon); and
generate a reservoir model based on at least one of the polygons, the static data, or the dynamic data, wherein the reservoir model (see methods 1700 and 1900 of FIGs. 17 and 19. As depicted in the workflow, the static and dynamic data as identified above is taken as an input to identify the drainage information where the static data, dynamic data, and drainage information is used to forecast well production in order to identify recompletion opportunities or underdeveloped areas where new drilling can occur) includes reservoir performance (e.g., interference analysis at step 1970 of FIG. 19), field development (e.g., selecting and analyzing candidate wells described throughout method 1900 including steps 1950 and 1960 of FIG. 19), production metrics (e.g., production forecasts), and operation metrics (calculating geologic uncertainty 118 at step 1750 of method 1700; para. [0013], “[t]he uncertainty calculation module 117 may then calculate a level of geologic uncertainty 118 relative to the forecasted production results 114 and determined drainage 110 (1750). The geologic uncertainty levels 118 indicate levels of risk 113. The risk level accounts for various unknowns in the drilling process including how much material is actually remaining, how many of the wells are interconnected, how many different rock formations exist, different pressures, the presence of other materials, etc. Many factors may make placing a well in a given zone 134 more or less risky in terms of ultimate production from the well. In some cases, calculating the level of geologic uncertainty 118… may include identifying structural risks, mapping risks, petrophysical risks, saturation risks or other types of risk.”).
Regarding claim 11, Benhallam discloses processing the static data and the dynamic data includes inputting the static data (geological data listed in log-based attributes and geo-engineering attributes as described in Table 3 of para. [0098]) and the dynamic data (production-related attributes listed in geo-engineering attributes and the target attributes) into a machine learning model (para. [0098], “Artificial Neural Networks (ANN's) are used to develop a model that relates the production performance to a series of data attributes including petrophysical interpretations, geological properties, and various engineering attributes. The training set used to train the model include past completion intervals, and the training attributes are derived or calculated specifically over these intervals.”)
Regarding claim 12, Benhallam discloses train a machine learning model (para. [0098], “Artificial Neural Networks (ANN's) are used to develop a model that relates the production performance to a series of data attributes including petrophysical interpretations, geological properties, and various engineering attributes. The training set used to train the model include past completion intervals, and the training attributes are derived or calculated specifically over these intervals.”) based on historical static data (geological data listed in log-based attributes and geo-engineering attributes as described in Table 3 of para. [0098]) and historical dynamic data (production-related attributes listed in geo-engineering attributes and the target attributes) Examiner notes that, under the broadest reasonable interpretation, any data which is capable of being used to train a machine learning model must inherently exist prior to training the machine learning model. Therefore, such data constitutes “historical,” data relative to the machine learning model insofar as the data must pre-date the model).
Regarding claim 13, Benhallam discloses train a machine learning model (para. [0098], “Artificial Neural Networks (ANN's) are used to develop a model that relates the production performance to a series of data attributes including petrophysical interpretations, geological properties, and various engineering attributes. The training set used to train the model include past completion intervals, and the training attributes are derived or calculated specifically over these intervals.”) based on the static data (geological data listed in log-based attributes and geo-engineering attributes as described in Table 3 of para. [0098]) and the dynamic data (production-related attributes listed in geo-engineering attributes and the target attributes).
Regarding claim 14, Benhallam discloses both the static data and the dynamic data are stored in the spatio-temporal database (as noted in claim 11, data store 140 includes petrophysical log data 135, geological log data 136, engineering data 137, historical completion data 141, and/or production data 142. These datasets constitute both static data and dynamic data as discussed in claim 11).
Regarding claim 15, Benhallam discloses wherein the spatio-temporal database includes data of the reservoir from a plurality of points in time (para. [0096], “[s]tep 1205 forecasts production for each of the identified and validated targets using a range of different techniques. These include statistical methods that leverage spatial and temporal neighborhood fluid data, machine learning techniques like supervised neural networks, analytical models specifically designed for horizontal wells, and simulation models.” As noted in claim 1, data store 140 includes petrophysical log data 135, geological log data 136, engineering data 137, historical completion data 141, and/or production data 142. The production data includes production rates which constitute reservoir data associated with points in time.).
Regarding claim 16, Benhallam discloses the spatio-temporal database includes a simulation grid (para. [0035], “[t]he computer system accesses geological, petrophysical or engineering data related to a hydrocarbon extraction site, and analyzes the accessed data to identify well placement grid cells in the hydrocarbon extraction site that are fit for placing new wells according to well placement constraints.”) and a polygon grid (para. [0116], “[s]till further, analyzing isolated and connected intervals of the hydrocarbon wells to identify drainage may include creating a drainage grid. Within this drainage grid, each formation uses the production volume or estimated ultimate recovery allocated to the formation in each hydrocarbon well.” As depicted in FIG. 1, all of the data modeled in computer system 101 may be stored in Data Store 140 and all of the data stored in Data Store 140 may be included in the model), wherein the simulation grid is associated with the static and dynamic data (as identified in claim 11, the static and dynamic data includes the geological, petrophysical, and engineering data. As discussed in para. [0035], this data is modeled in a grid in order to generate a geo-reservoir model which is used to identify new potential drilling locations. See also method 1900 which uses a 3-dimentional ), and wherein the polygon grid is associated with drainage areas of the reservoir (see citations to para. [0010], [0033], [0088], and [0116]).
Regarding claim 17, Benhallam discloses wherein the static data and dynamic data include both field data (para. [0058], “well surveys, well logs, well formation tops, completion and perforation data, production data, fluid contacts data, static geologic models”) and data obtained in a simulation of the reservoir (para. [0057], “[t]he platform implements inter-disciplinary field data including well surveys, well logs, well formation tops, completion and perforation data, production data, fluid contacts data, static geologic models, simulation models, and other types of data.”).
Regarding claim 18, Benhallam discloses [a] system comprising: a storage configured to store instructions (system memory 103 of computer system 101); a processor (processor 102 of computer system 101) configured to execute the instructions and cause the processor to: receive static data of a reservoir (para. [0051], “[t]he communications module 104 may receive data from a hydrocarbon extraction site 130 (e.g. from sensors running within or around the wells 131). The data may include petrophysical data 135, geological data 136, engineering data 137 or other data related to the production of hydrocarbons at the site 130.”; para. [0118], “the data accessing module 107 may access petrophysical log data 135, geological log data 136, and/or engineering data 137 related to the hydrocarbon extraction site 130. This data may be received directly from the site, or may be retrieved from a data store such as data store 140.”) from a spatio-temporal database (data store 140 includes petrophysical log data 135, geological log data 136, engineering data 137, historical completion data 141, and/or production data 142);
receive dynamic data of the reservoir (para. [0051], “[t]he communications module 104 may receive data from a hydrocarbon extraction site 130 (e.g. from sensors running within or around the wells 131). The data may include petrophysical data 135, geological data 136, engineering data 137 or other data related to the production of hydrocarbons at the site 130.”; para. [0118], “the data accessing module 107 may access petrophysical log data 135, geological log data 136, and/or engineering data 137 related to the hydrocarbon extraction site 130. This data may be received directly from the site, or may be retrieved from a data store such as data store 140.”) from a spatio-temporal database (data store 140 includes petrophysical log data 135, geological log data 136, engineering data 137, historical completion data 141, and/or production data 142);
process the static data and the dynamic data to identify centroids of wells in the reservoir (para. [0010], “[t]he computer system also includes a drainage analyzer configured to identify zones of the hydrocarbon extraction site that include drained portions that have been drained by existing wells on the site, along with a 3D map generator configured to generate a relative probability of success (RPOS) 3D map that ranks the identified zones of the site according to zone-mappable attributes associated with productive hydrocarbon wells.”; para. [0033], “the computer system analyzes isolated and connected intervals of the hydrocarbon wells to determine, according to the geological map, which portions of the hydrocarbon wells have been drained by existing completions.”; para. [0088], “[a] 3D spacing analysis module 1201 flags no-go cells based on spacing to avoid collisions with existing wells. 3. A volumetric production analysis module 1202 identifies cells drained by existing wells.” The existing well locations are required input order to determine the drainage shapes around the wells.);
generate polygons (drainage grid) based on the centroids of the wells in the reservoir (para. [0116], “[s]till further, analyzing isolated and connected intervals of the hydrocarbon wells to identify drainage may include creating a drainage grid. Within this drainage grid, each formation uses the production volume or estimated ultimate recovery allocated to the formation in each hydrocarbon well.” See also the citations to para. [0010], [0033], and [0088] as provided above. The system of Benhallam includes a drainage analyzer 109 which identifies the spatial areas and/or volumes around the wells which have been drained/depleted. Determining a drainage area on a cell-by-cell basis (e.g., see para. [0088]) for a gridded reservoir/geospatial model is equivalent to generating a polygon); and
generate a reservoir model based on at least one of the polygons, the static data, or the dynamic data, wherein the reservoir model (see methods 1700 and 1900 of FIGs. 17 and 19. As depicted in the workflow, the static and dynamic data as identified above is taken as an input to identify the drainage information where the static data, dynamic data, and drainage information is used to forecast well production in order to identify recompletion opportunities or underdeveloped areas where new drilling can occur) includes reservoir performance (e.g., interference analysis at step 1970 of FIG. 19), field development (e.g., selecting and analyzing candidate wells described throughout method 1900 including steps 1950 and 1960 of FIG. 19), production metrics (e.g., production forecasts), and operation metrics (calculating geologic uncertainty 118 at step 1750 of method 1700; para. [0013], “[t]he uncertainty calculation module 117 may then calculate a level of geologic uncertainty 118 relative to the forecasted production results 114 and determined drainage 110 (1750). The geologic uncertainty levels 118 indicate levels of risk 113. The risk level accounts for various unknowns in the drilling process including how much material is actually remaining, how many of the wells are interconnected, how many different rock formations exist, different pressures, the presence of other materials, etc. Many factors may make placing a well in a given zone 134 more or less risky in terms of ultimate production from the well. In some cases, calculating the level of geologic uncertainty 118… may include identifying structural risks, mapping risks, petrophysical risks, saturation risks or other types of risk.”).
Regarding claim 19, Benhallam discloses wherein processing the static data and the dynamic data includes inputting the static data (geological data listed in log-based attributes and geo-engineering attributes as described in Table 3 of para. [0098]) and the dynamic data (production-related attributes listed in geo-engineering attributes and the target attributes) into a machine learning model (para. [0098], “Artificial Neural Networks (ANN's) are used to develop a model that relates the production performance to a series of data attributes including petrophysical interpretations, geological properties, and various engineering attributes. The training set used to train the model include past completion intervals, and the training attributes are derived or calculated specifically over these intervals.”).
Regarding claim 20, Benhallam discloses train a machine learning model (para. [0098], “Artificial Neural Networks (ANN's) are used to develop a model that relates the production performance to a series of data attributes including petrophysical interpretations, geological properties, and various engineering attributes. The training set used to train the model include past completion intervals, and the training attributes are derived or calculated specifically over these intervals.”) based on the static data (geological data listed in log-based attributes and geo-engineering attributes as described in Table 3 of para. [0098]) and the dynamic data (production-related attributes listed in geo-engineering attributes and the target attributes).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Published US Patent Application to Katterbauer et al. (US 20170067323 A1) which is directed to a recursively updated reservoir simulation where the reservoir simulation is constructed using multiple data sets such as production, seismic, electromagnetic, gravimetric and surface deformation data (e.g., static and dynamic reservoir data). Katterbauer et al. is further directed to a history matching process which improves model accuracy;
Issued US Patent Application to Middya et al. (US 8589135 B2) which is directed to a reservoir simulator further comprising static and dynamic features populated within a reservoir grid (see FIGs. 2—4) where the reservoir simulator is used to calculate drainage area and associated production depletion features;
Issued US Patent Application to Cheng et al., (US 10584570 B2) which is directed to a reservoir simulator which is used to identify target drilling locations;
Published US Patent Application to Tilke et al. (US 20080300793 A1) which is directed to a hybrid evolutionary algorithm technique for automatically calculating well and drainage locations in a hydrocarbon field where the hybrid evolutionary algorithm technique includes planning a set of wells for a static reservoir model using an automated well planner tool, and then selecting a subset of the wells based on dynamic flow simulation using a cost function that maximizes recovery or economic benefit;
Published US Patent Application to Abasov et al., (US 20100125349 A1) which is directed to a grid-based reservoir simulator based on actual and potential reservoir performance which is capable of determining drainage features associated with production/depletion operations;
Issued US Patent Application to Sarduy et al., (US 10260319 B2) which is directed to a reservoir model generated from statistical learning methods (e.g., machine learning) trained on historical data which takes drainage area into consideration and provides an estimated production forecast; and
Published US Patent Application to Zanon et al., (US 20190120022 A1) which is directed to a reservoir simulation used to determine drainage of a hydraulically fractured reservoir.
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/U.L.N./Examiner, Art Unit 3676
/TARA SCHIMPF/Supervisory Patent Examiner, Art Unit 3676
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