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 .
This Office Action is in response to the application filed on 01/26/2022. Claims 1-20 are pending in this application. Claims 1, 10, and 19 are independent claims.
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 .
SC 112 Rejection
4. The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 10, and 19 as amended discloses “determine stimulated reservoir volumes are determined without simulation of fractures for the target well and the other potential wells”. Theses feature is not descried in specification thus interpret as new matter.
Specification para [0053]: FIG. 6 illustrates examples of stimulation of wells in a subsurface volume of interest, in accordance with one or more implementations. 610 represents a subsurface volume of interest with three stimulated portions of wells represented by the regions 612, 613, 614, 615, and 616. A first well may be represented by regions 612 and 613, a second well may be represented by regions 613, 614, and 615, and a third well may be represented by regions 615 and 616. Region 613 may represent well overlap between region 612 and region 614, and region 615 may represent well overlap …”.
[0054] discloses “In contrast, the presently disclosed technology may rely on an image-based machine learning model (e.g., convolutional neural network) to estimate the well interference. This may include generating a well overlap and/or extraction interference probabilities.” Specification [0071] discloses Operation 204 may include obtaining estimated reservoir volumes. The estimated reservoir volumes may represent a total volume of reservoir that can be hydraulically fractured. In some implementations, the estimated reservoir volumes may be generated based on at least productivity cut off values and permeability values for the target well and the other potential wells in the subsurface volume of interest. The productivity cut off values may be based on a minimum permeability value corresponding to a productivity threshold. The permeability values specify a capacity of a subsurface region to transmit a fluid. Operation 204 may be performed by a physical computer processor configured by machine-readable instructions including a component that is the same as or similar to estimated reservoir volume component 110 in accordance with one or more implementations.”
USC 102/103 Rejection
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 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by (US PGPub 2021018655) (hereinafter “Sun et al”) or, in the alternative, under 35 U.S.C. 103 as obvious over [Sun et al in view of 20190361146 (hereinafter "Roth") ].
Regarding Claim 1, Sun et al anticipates a computer-implemented method for estimating well interference on a target well from other potential wells in a subsurface volume of interest, the method being implemented in a computer system that includes a physical computer processor, a graphical user interface, and non-transient electronic storage, the method comprising ([0011] and Figure 1, The system of Figure 1 can be a computational device comprising a processor, a user interface has a graphical user interface element, and a memory, where [0127] “The memory 128 may correspond to any type of non-transitory computer-readable medium”; ([0013]-[0014]) A depletion estimate attribute can be determined for a planned well in a geographical area; [0255] The AVAS technique includes modeling well interference):
obtaining well implementation data for the target well and the other potential wells in the subsurface volume of interest from the non-transient electronic storage ([0015] " A well interference system may obtain input data associated with one or more wells such as one or more fully-bounded or half- bounded child wells and associated parent wells."; paragraph [0037]);
determine stimulated reservoir volumes for the target well and the other potential wells in the subsurface volume of interest based on productivity cut off values and permeability values for the target well and the other potential wells, wherein the stimulated reservoir volumes are determined without simulation of fractures for the target well and the other potential wells (paragraph [0038]: 'The raw data may include a table or database that includes child well production information for one or more child wells, a true vertical depth of the child wells, a latitude of the child wells, a longitude of the child wells, a lateral length of the child wells, a fluid volume associated with the child wells, and a proppant mass associated with the child wells." paragraph [0039]: “the input data is collected from the public databases and data sources, machine learning model development is performed.”) generating, with the physical computer processor, well overlap between the target well and the other potential wells based on at least shared regions between the stimulated reservoir volume for the target well and the other potential wells” paragraph [0020]: "With decreased cluster spacing and increased treatment size, well production forecasting of infill or child wells can become uncertain as a result of possible interrelated effects of well interference and near-wellbore fracture competition.");
and generating, with the physical computer processor, extraction interference probabilities based differences between stimulated reservoir volumes and the well overlap, wherein the extraction interference probabilities each specify an effect one or more of the other potential wells have on productivity of the target well (paragraph [0021] describes the effects of wells on productivity ; paragraph [0066] describes: table 504 incudes data describing the parent wells and their environment carried forward into the machine learning modeling process, such as additional wellbore physical characteristics, reservoir parameters, stimulation treatment design parameters relative to the child well.” );
generating, with the physical computer processor, a representation of a well layout as a function of position in the subsurface volume of interest using visual effects to depict at least a portion of one of the well implementation data, the stimulated reservoir volumes, and the well overlap, wherein the well layout corresponds to one of the extraction interference probabilities that is below an interference threshold (paragraph [0036]: "As a result, the system can be used to enhance pre-job or real-time completion design and optimization for infill child wells. By using sensitivity analysis of input parameters, an optimized completion design can be determined to maximize well production of the infill child wells”; paragraph [0067] describes: fig 6 and diagram 600. Fig 65 shows a five-fold cross validation RMSE score for each of the machine learning models. In an example, three of the machine learning models are chosen because they have lowest RMSE.” );
and displaying the representation in the graphical user interface, wherein the visual effects of the representation that are displayed in the graphical user interface include a visual transformation in how the representation is displayed in the graphical user interface. paragraph [0046]: "FIG. 2 illustrates a well interference system 201 according to an example. The well interference system 201 can be implemented for predicting infill or child well production efficiently as described herein. In this example, the well interference system 201 can include compute components 202, a data input engine 204, a model development engine 206, and a storage 208. In some implementations, the well interference system 201 can also include a display device 210 for displaying data and graphical elements such as images, videos, text, simulations, and any other media or data content." Also, See fig 7, step 810.
In summary, Sun et al discloses machine learning model for predicting well production for at least one parent and the child well and generate learning output using the at least one machine learning model and hyper parameters for each of the at least one machine learning model, the learning output indicating a test root-mean-square error (RMSE) and a training RMSE. Sun does not explicitly discloses the stimulated reservoir volume are determined without simulation of fractures for the target well and the other potential wells.
However, specification does not support how stimulated reservoir volume can be determined without simulation of fracture. In fact, In Para [0054] of the instant application as filed discloses: “In contrast to the presently disclosed technology, existing technology requires two different simulation to be run, a fracturing simulation along with a reservoir simulation. Some existing technology may need to run calculations on a cell by cell basis. In some implementations, existing technology may need to run calculations for each well and various combinations of the wells. For a two well scenario, existing technology may first calculate hydrocarbon production for the individual wells. Then, the hydrocarbon production may be calculated for simultaneously draining two adjacent wells. Then, the difference between the simultaneous hydrocarbon production and a sum of the two individual wells is used to determine the interference. For a three well scenario, the difference between the simultaneous hydrocarbon production for three adjacent wells and a sum of the three adjacent wells are used to determine the interference. It should be appreciated that as the number of wells in a scenario increases, the number of calculations increases. In contrast, the presently disclosed technology may rely on an image-based machine learning model (e.g., convolutional neural network) to estimate the well interference. This may include generating a well overlap and/or extraction interference probabilities.
Therefore it would have obvious before the filling date of the invention to utilize teaching of Stimulated volume without simulation of fractures of well into Sun et al because the teaching of machine learning model for well interference system as per existing technology would predict similar result as the invention as claimed.
Regarding Claim 2, Sun in view of Roth discloses: the method as recited in claim 1, wherein the visual transformation in how the representation is displayed in the graphical user interface includes a visual zoom, a visual filter, a visual rotation, or a visual overlay. (Sun et a, discloses: Fig 3., overhead view (i.e., plan view or map view) of example wells that can be analyzed by the well interface system 201; [0075] At step 810, well interfaces system 210 can generate a learning output using the at least one machine learning model and the hyper parameters for each of the at least one machine learning model. …RMSE be displayed on the display 210 as part of a graphical user interface. Roth: [0240] and Figure 41, "To account for higher permeability nearer to the wellbore, the lateral distance between wells is typically weighted using a Gaussian, Logistic, or other mathematical distribution function that weights more heavily at closer distances and less heavily at further distances. This weighting function, which is used to model the decrease in the producibility of a well as a function of distance from the wellbore, is an approximation of the fracture permeability created by the hydraulic fracturing for a given well"; ([0293]-[0295] and Figure 71) Further the penalty for the predicted resource volume is “determined using the well spacing and depletion relationships determined through multi-variate modeling as discussed above with respect to FIGS. 41-47. Moreover, the penalty increases as the distance from each existing well 6908 decreases, consistent with the modeling described with respect to FIGS. 41-47”).
Regarding Claim 3, Sun in view of Roth discloses the method as recited in claim 1, wherein the productivity cut off values are based on a minimum permeability value corresponding to a productivity threshold ([0240] "This weighting function, which is used to model the decrease in the producibility of a well as a function of distance from the wellbore, is an approximation of the fracture permeability created by the hydraulic fracturing for a given well. Nearest to the well is the greatest amount of induced fracturing and the highest weighting factor. Moving away from the well, the amount of induced fracturing decreases, ultimately reaching zero where no frac fluid has reached from the given well. This is emulated by the fall-off of the weighting function, ultimately to zero/negligible values". [0241] "Determining the depletion metric or attribute involves modeling the decrease in producibility (e.g., fracture permeability) laterally away from the wellbore of a given well, using a function (e.g., Gaussian, Logistic, Linear) with half width X. FIG. 41 provides an overview of the process of making this determination"; The point where zero frac fluid is induced can be considered a minimum permeability value, which corresponds to a productivity threshold, such that any distance further from the wellbore will also result in zero/negligible producibility and distances closer to the wellbore will result in producibility values).
Regarding Claim 4, Sun in view of Roth discloses the method as recited in claim 1, wherein the permeability values specify a capacity of a subsurface region to transmit a fluid ([0244] “The permeability of the well is a measure of the permeability of the ground surrounding the well due to hydraulic fracturing. The process of hydraulic fracturing generates cracks in the rock, props them open, and thus artificially creates permeability in the rock to a degree that oil can actually flow to the well”).
Regarding Claim 5, Sun in view of Roth discloses the method as recited in claim 1, wherein the target well and the other potential wells form a pre-configured layout in the subsurface volume of interest ([0293]-[0295] and Figure 70, Figure 70 shows a target well 6916 and existing wells forming a pre-configured layout in a subsurface volume of interest).
Regarding Claim 6, Sun in view of Roth discloses the method as recited in claim 1, wherein productivity values derived from the stimulated reservoir volumes are used to generate the extraction interference probability ([0245] “For each neighbor well having a producibility curve that overlaps the producibility curve of the well in question, the area of overlap is calculated, then multiplied by the cumulative oil production of the well prior to the date that the neighboring well reaches 180 days of production (or any other length of time of production determined by a user or an operator of the production prediction system). This creates the “depletion estimate” of the first well on the second well, and this process is repeated for all neighbor wells within 10,000 feet laterally of the well in question”).
Regarding Claim 7, Sun in view of Roth discloses the method as recited in claim 1, wherein the well implementation data comprises one of a well location, a well spacing, and a well geometry ([0010] "an analysis of the structural model that includes a prediction of performance for the at least one well, wherein the prediction of performance is based, at least in part, on a location of the at least one well within the structural model, a length of the at least one well, an average distance from the at least one well to a bottom of a formation in the structural model, a distance between wells in the geographical area").
Regarding Claim 8, Sun in view of Roth discloses the method as recited in claim 1, wherein the extraction interference probability comprises one of a P10 value, a P50 value, and a P90 value ([0239] and Figure 15, The depletion metric allows the production prediction of the well of interest to account for neighboring wells, ([0269] and Figure 49) where the productivity prediction of a well that hasn’t been drilled can include probability curves for the total production over time and the curves can include P10, P50, and P90 values as shown in Figure 49 ).
Regarding Claim 9, Sun in view of Roth discloses the method as recited in claim 1, wherein the well overlap between the target well and the other potential wells comprises a given shared region between the stimulated reservoir volume for two adjacent wells ([0074] “FIG. 43 is a series of charts showing the overlap between production zones of adjacent wells for use in calculating a depletion estimate”; (Figure 71) Further, Figure 71 shows well overlap between a target well and adjacent existing wells sharing a region based on their production volumes).
Regarding claim 10, it is the system claim, having similar limitations of claim 1. The additional limitations of claim 10, with respect to claim 1, is that the system has a physical computer processor configured by machine-readable instructions. Sun in view of Roth discloses processor configured by machine-readable instructions ( [0014] “The computer-readable memory may store additional instructions for execution by the processor that, when executed by the processor, further cause the processor to generate a production prediction for each of a plurality of planned wells based on the structural model, wherein the production prediction accounts for depletion effects of the plurality of planned wells”). The remaining limitations of claim 10 are also rejected under the similar rationale as cited in the rejection of claim 1.
Regarding claim 11, it is the system claim, having similar limitations of claim 2. Thus, claim 11 is also rejected under the similar rationale as cited in the rejection of claim 2.
Regarding claim 12, it is the system claim, having similar limitations of claim 3. Thus, claim 12 is also rejected under the similar rationale as cited in the rejection of claim 3.
Regarding claim 13, it is the system claim, having similar limitations of claim 4. Thus, claim 13 is also rejected under the similar rationale as cited in the rejection of claim 4.
Regarding claim 14, it is the system claim, having similar limitations of claim 5. Thus, claim 14 is also rejected under the similar rationale as cited in the rejection of claim 5.
Regarding claim 15, it is the system claim, having similar limitations of claim 6. Thus, claim 15 is also rejected under the similar rationale as cited in the rejection of claim 6.
Regarding claim 16, it is the system claim, having similar limitations of claim 7. Thus, claim 16 is also rejected under the similar rationale as cited in the rejection of claim 7.
Regarding claim 17, it is the system claim, having similar limitations of claim 8. Thus, claim 17 is also rejected under the similar rationale as cited in the rejection of claim 8.
Regarding claim 18, it is the system claim, having similar limitations of claim 9. Thus, claim 18 is also rejected under the similar rationale as cited in the rejection of claim 9.
Regarding claim 19, it is the article claim, having similar limitations of claim 1. The additional limitations of claim 19, with respect to claim 1, is that it contains a non-transitory computer-readable medium storing instructions. Sun in view of Roth discloses a non-transitory computer-readable medium storing instructions ([0014] “The computer-readable memory may store additional instructions for execution by the processor that, when executed by the processor, further cause the processor to generate a production prediction for each of a plurality of planned wells based on the structural model, wherein the production prediction accounts for depletion effects of the plurality of planned wells”; [0127] “The memory 128 may correspond to any type of non-transitory computer-readable medium”). The remaining limitations of claim 19 are also rejected under the similar rationale as cited in the rejection of claim 1.
Regarding claim 20, it is the article claim, having similar limitations of claim 2. Thus, claim 20 is also rejected under the similar rationale as cited in the rejection of claim 2.
Response to Arguments
Applicant’s arguments and claim amendment as filed on 09/19/2025 over comes 35 USC 101 rejection directed to an abstract idea.
Applicant’s arguments with respect to claim(s) 1-20 over prior art rejection over Roth have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s argument presented on page 5 of remarks, such as “Then, well overlap is generated based on shared regions between the simulated reservoir volume of the wells. For example, FIG. 5 of the Application shows example well overlap using shared regions between simulated reservoir volumes. In FIG. 5, simulated reservoir volumes of three wells are determined. The well overlap between the stimulated reservoir volumes are shown in lighter shading. Extraction interference probabilities for wells are determined based on the differences between the stimulated reservoir volumes and the well overlap.
Antherword, in view of argument presented above it is simulated reservoir volume of wells are determined based on well interface estimation tool that are conventionally determined using machine learning model as disclosed above under 35 USC 103 rejection.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAMINI S SHAH whose telephone number is (571)272-2279. The examiner can normally be reached 8PM-5PM EST M-F.
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KAMINI S. SHAH
Supervisory Patent Examiner
Art Unit 2116
/KAMINI S SHAH/Supervisory Patent Examiner, Art Unit 2115
Prosecution Insights