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 the Application
This action is a first action on the merits in response to the application filed on 11/14/2024.
Status of Claims
Claims 1-25 filed on 11/14/2024 are currently pending and have been examined in this application.
Examiner’s search notes
With respect to the prior arts search, none of the prior arts of record, taken individually or in any combination, teach, inter alia,
A method for classifying a root cause of a sub-optimal production performance for hydrocarbon wells associated with at least one unconventional reservoir, wherein at least a portion of the method is implemented via a computing system comprising a processor, and wherein the method comprises: for each of a plurality of hydrocarbon wells: determining an expected production performance of the hydrocarbon well during each of multiple units of time via performance forecasting; determining an actual production performance of the hydrocarbon well during each of the multiple units of time based on production data corresponding to hydrocarbon production via the hydrocarbon well; determining a performance delta value for each of the multiple units of time by subtracting the sum of the actual production performance for each unit of time from the sum of the expected production performance for the same unit of time; and determining a volatility in the performance delta values for the hydrocarbon well using a statistical metric; generating a scatter plot representing production performances of the plurality of hydrocarbon wells, wherein the scatter plot comprises the volatility in the performance delta values for each hydrocarbon well versus a most recent performance delta value for the corresponding hydrocarbon well, and wherein the most recent performance delta value comprises the performance delta value for the unit of time corresponding to a most recently-occurring time period; and classifying a root cause of a sub-optimal production performance of at least a portion of the hydrocarbon wells based on quadrants of the scatter plot.
The prior art references most closely resembling the Applicant’s claimed invention are Zhang et al. (US 20240403775 A1), Bansal et al. (US 20230205948 A1), Burch et al. (US 9946974 B2), Motteram et al. (US 20070222595 A1), and Xu Wenyue (WO 2013016734 A1).
Zhang teaches a method for predicting well production of a reservoir, comprising: obtaining a training data set for training a machine learning (ML) model, wherein the ML model generates predicted well production data based on geological, completion, and petrophysical data of interest, wherein the training data set comprises historical well production data and corresponding geological, completion, and petrophysical data; selecting an artificial neural network (ANN) model structure, the model structure including a number of layers and a number of nodes of each layer; generating, using an ML algorithm applied to the training data set, a plurality of individually trained ML models, wherein each individually trained ML model is generated based on one of a plurality sets of initial model parameters and selecting the plurality of individually trained ML models based on loss values of the training data set; calculating a model performance of each trained model by evaluating a difference between a model prediction and a well performance data (see claim 1)
Bansal teaches acquiring, by a computing device from a data store, wellsite data for one or more existing production wells in a hydrocarbon producing field; transforming, by the computing device, the wellsite data into one or more model data sets for predictive modeling; training a first machine learning (ML) model to predict well logs for the one or more existing production wells, based on the one or more model data sets; generating a first well model to estimate production for the one or more existing production wells, based on the well logs predicted using the trained first ML model; tuning parameters of the first well model, based on a comparison between the estimated production and an actual production of the one or more existing production wells; training a second ML model to predict parameters of a second well model for a new production well in the hydrocarbon producing field, based on the tuned parameters of the first well model, the parameters of the second well model including completion design parameters for the new production well; forecasting production of the new production well over a period of time, based on the predicted parameters of using the trained second ML well model, the predicted parameters of the second well model including completion design parameters for the new production well; estimating completion costs for the new production well, based on the completion design parameters and the forecasted production over the period of time [Bansal, para. 0096]
Burch teaches a method for determining well parameters for optimization of well performance, comprising: training, via a computing system, a well performance predictor based on field data corresponding to a hydrocarbon field, generating, via the computing system, a plurality of candidate well parameter combinations for the well; predicting, via the computing system, a performance of the well for each of the plurality of candidate well parameter combinations using the trained well performance predictor; determining, via the computing system, an optimized well parameter combination for the well such that the predicted performance of the well is maximized, wherein the predicted performance comprises a hydrocarbon production and a corresponding uncertainty of the prediction; and causing a well to be drilled and completion to be installed in the well based on the optimized well parameter combination [claim 1]
Motteram teaches performance by determining a difference between actual performance and programmed or forecast performance of the effective utilization ratio in a warehouse (see para. 0017)
Xu teaches Examples of empirical forecasts are provided in US Patent Nos. 7788074,
6101447 and 6101447, and disclosed in Arps, "Analysis of Decline Curves", SPE Journal Paper, Chapt. 2, pp. 128-247 (1944). Empirical forecasts may involve an estimate of well production using various types of curves with adjustable parameters for different flow regimes separately during a reservoir's lifespan.
None of the cited documents by the Examiner disclose “determining an expected production performance of the hydrocarbon well during each of multiple units of time via performance forecasting; determining an actual production performance of the hydrocarbon well during each of the multiple units of time based on production data corresponding to hydrocarbon production via the hydrocarbon well; determining a performance delta value for each of the multiple units of time by subtracting the sum of the actual production performance for each unit of time from the sum of the expected production performance for the same unit of time; and determining a volatility in the performance delta values for the hydrocarbon well using a statistical metric; generating a scatter plot representing production performances of the plurality of hydrocarbon wells, wherein the scatter plot comprises the volatility in the performance delta values for each hydrocarbon well versus a most recent performance delta value for the corresponding hydrocarbon well, and wherein the most recent performance delta value comprises the performance delta value for the unit of time corresponding to a most recently-occurring time period; and classifying a root cause of a sub-optimal production performance of at least a portion of the hydrocarbon wells based on quadrants of the scatter plot”
As a result, none of the cited documents by the Examiner, taken individually or in combination, discloses or suggests the features in the independent claims, nor could a person skilled in the art easily conceive of such features even in the light of common technical knowledge at the time of filing. The pending claims 1-25 are therefore distinguished from the prior arts.
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 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1-25 are directed to an abstract idea without additional elements to integrate the claims into a practical application or to amount to significantly more than the abstract idea.
Claims 1-25 are directed to a process, machine, or manufacture (Step 1), however the claims are directed to the abstract idea of determining root cause of a sub-optimal production performance of a hydrocarbon wells by comparing forecasted and actual well performance and plotting a scattered plot representing the well production performances.
With respect to Step 2A Prong One of the frameworks, claim 1 recites an abstract idea. Claim 1 includes limitations for “a method for classifying a root cause of a sub-optimal production performance for hydrocarbon wells associated with at least one unconventional reservoir, wherein the method comprises: for each of a plurality of hydrocarbon wells: determining an expected production performance of the hydrocarbon well during each of multiple units of time via performance forecasting; determining an actual production performance of the hydrocarbon well during each of the multiple units of time based on production data corresponding to hydrocarbon production via the hydrocarbon well; determining a performance delta value for each of the multiple units of time by subtracting the sum of the actual production performance for each unit of time from the sum of the expected production performance for the same unit of time; and determining a volatility in the performance delta values for the hydrocarbon well using a statistical metric; generating a scatter plot representing production performances of the plurality of hydrocarbon wells, wherein the scatter plot comprises the volatility in the performance delta values for each hydrocarbon well versus a most recent performance delta value for the corresponding hydrocarbon well, and wherein the most recent performance delta value comprises the performance delta value for the unit of time corresponding to a most recently-occurring time period; and classifying a root cause of a sub-optimal production performance of at least a portion of the hydrocarbon wells based on quadrants of the scatter plot”
The limitations above recite an abstract idea under Step 2A Prong One. More particularly, the limitations above recite Mental Process because an ordinary skilled in the art can reasonably analyze actual and forecasted well performance data to plot volatility performance and determine root cause for underperformed wells. As a result, claim 1 recites an abstract idea under Step 2A Prong One.
Claims 17 and 25 recite substantially similar limitations to those presented with respect to claim 1. As a result, claims 17 and 25 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claim 1. Similarly, claims 2-16 and 18-24 recite Mental Process because an ordinary skilled in the art can reasonably analyze actual and forecasted well performance data to plot volatility performance and determine root cause for under performance. As a result, claims 2-16 and 18-24 recite an abstract idea under Step 2A Prong One.
With respect to Step 2A Prong Two of the framework, claim 1 does not include additional elements that integrate the abstract idea into a practical application. Claim 1 includes additional elements that do not recite an abstract idea. The additional elements of claim 1 include “wherein at least a portion of the method is implemented via a computing system comprising a processor, and”. When considered in view of the claim as a whole, the recited computer elements do not integrate the abstract idea into a practical application because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely include[ing] instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application. Therefore, the claim is directed to an abstract idea.
As a result, claim 1 does not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
As noted above, claims 17 and 25 recite substantially similar limitations to those recited with respect to claim 1. Although claim 17 further recites “A hydrocarbon well system, comprising: multiple hydrocarbon wells, wherein hydrocarbon fluids are produced from each hydrocarbon well concurrently with a measurement of corresponding production data; and a computing system that is communicably coupled to the multiple hydrocarbon wells, wherein the computing system comprises: a processor; and a non-transitory, computer-readable storage medium comprising program instructions that are executable by the processor to cause the processor to” and claim 25 further recites “A non-transitory, computer-readable storage medium”, when considered in view of the claim as a whole, the recited computer elements do not integrate the abstract idea into a practical application because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 17 and 25 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2-16 and 18-24 do not include any additional elements beyond those recited by independent claims 1, 17, and 25. As a result, claims 2-16 and 18-24 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, claim 1 does not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 1 includes additional elements that do not recite an abstract idea. The additional elements of claim 1 include “wherein at least a portion of the method is implemented via a computing system comprising a processor, and”. The recited computer elements do not amount to significantly more than the abstract idea because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claim 1 does not include additional elements that amount to significantly more than the abstract idea under Step 2B.
As noted above, claims 17 and 25 recite substantially similar limitations to those recited with respect to claim 1. Although claim 17 further recites “A hydrocarbon well system, comprising: multiple hydrocarbon wells, wherein hydrocarbon fluids are produced from each hydrocarbon well concurrently with a measurement of corresponding production data; and a computing system that is communicably coupled to the multiple hydrocarbon wells, wherein the computing system comprises: a processor; and a non-transitory, computer-readable storage medium comprising program instructions that are executable by the processor to cause the processor to” and claim 25 further recites “A non-transitory, computer-readable storage medium”, the recited computer elements do not amount to significantly more than the abstract idea because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 17 and 25 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 2-16 and 18-24 do not include any additional elements beyond those recited by independent claims 1, 17, and 25. As a result, claims 2-16 and 18-24 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1-25 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
Any inquiry concerning this communication from the examiner should be directed to Abdallah El-Hagehassan whose contact information is (571) 272-0819 and Abdallah.el-hagehassan@uspto.gov The examiner can normally be reached on Monday- Friday 8 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached on (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-3734.
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/ABDALLAH A EL-HAGE HASSAN/
Primary Examiner, Art Unit 3623