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 .
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, 3-10, 12-17 and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims’ subject matter eligibility will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (January 7, 2019) (“2019 PEG”).
With respect to claim 1.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—claim 1 recites a method, which is a process.
Step 2A, prong one: Does the claim recite an abstract idea, law of nature or natural phenomenon? Yes—the limitations identified below each, under its broadest reasonable interpretation, covers mathematical concept, but for the recitation of generic computer components:
“selecting input features from the well log data based on a statistical analysis of the well log data and the core sample data; (mathematical concept of selecting variables)
forming a training dataset including the input features and corresponding core sample data as labeled output data; and training, using the training dataset, a machine learning model: (mathematical concept of optimization)
predicting permeability data for non-cored wells in the subsurface formation based on the trained machine learning model: (mathematical calculation).
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the judicial exception is not integrated into a practical application.
“obtaining well log data and core sample data from wells in the subsurface formation;” involves the mere gathering of data, which is insignificant extra-solution activity. See MPEP § 2106.05(g).
“training, using the training dataset, a machine learning model;”: Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No—there are no additional limitations beyond the mental processes identified above. The limitation treated above, are directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory. See MPEP § 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). It also includes limitations that Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). The additional element is insignificant application, which is similar to examples of activities that the courts have found to be insignificant extra-solution activity, in accordance with MPEP 2106.05(g), Insignificant Extra-Solution Activity. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 3.
Step 1: A method, as above.
Step 2A Prong 1 : The judicial exceptions of claim 1 are incorporated.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. The claim recites “wherein the machine learning model comprises a neural network.”: This limitation merely recites generic training, Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)). The additional elements as disclosed above, alone or in combination, do not integrate the judicial exception into a practical application as disclosed above. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The above limitation, mere instructions to apply as it recites only the idea of a solution or outcome, MPEP 2106.05(f). Thus, in examining the claim elements as recited by the limitations individually and as an ordered combination, as a whole the independent claims do not recite what have the courts have identified as "significantly more". The claim is not patent eligible.
Claim 4.
Step 1: A method, as above.
Step 2A Prong 1: The claim recites that “wherein the statistical analysis comprises: determining a correlation factor between the well log data and the core sample data”: This limitation merely specifies additional mathematical calculations.
Step 2A Prong 2, Step 2B: This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
Claim 5.
Step 1: A method, as above.
Step 2A Prong 1: The claim recites that “wherein the statistical analysis further comprises:
removing data points from the well log data corresponding to permeability core sample data having values less than a specified threshold.”: This limitation merely specifies additional mathematical calculations.
Step 2A Prong 2, Step 2B: This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
Claim 6.
Step 1: A method, as above.
Step 2A Prong 1: The claim recites that “wherein the specified threshold comprises a resolution of a measurement instrument.”: This limitation merely specifies additional mathematical calculations.
Step 2A Prong 2, Step 2B: This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
Claim 7.
Step 1: A method, as above.
Step 2A Prong 1: The claim recites that “wherein the input features comprise one or more of a compressional sonic log, a total porosity log, a volume of calcite log, and a volume of dolomite log”: This limitation merely specifies additional mathematical calculations.
Step 2A Prong 2, Step 2B: This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
Claim 8.
Step 1: A method, as above.
Step 2A Prong 1 : The judicial exceptions of claim 1 are incorporated.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. The claim recites “wherein training the machine learning model comprises: splitting the training dataset into a training set and a testing set based on random selection.”: This limitation merely recites generic training, Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)). The additional elements as disclosed above, alone or in combination, do not integrate the judicial exception into a practical application as disclosed above. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The above limitation, mere instructions to apply as it recites only the idea of a solution or outcome, MPEP 2106.05(f). Thus, in examining the claim elements as recited by the limitations individually and as an ordered combination, as a whole the independent claims do not recite what have the courts have identified as "significantly more". The claim is not patent eligible.
Claim 9.
Step 1: A method, as above.
Step 2A Prong 1 : The judicial exceptions of claim 1 are incorporated.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. The claim recites “wherein training the machine learning model comprises: performing cross validation by iteratively training the machine learning model; and resampling the training set and the testing set before each iteration.”: This limitation merely recites generic training, Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)). The additional elements as disclosed above, alone or in combination, do not integrate the judicial exception into a practical application as disclosed above. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The above limitation, mere instructions to apply as it recites only the idea of a solution or outcome, MPEP 2106.05(f). Thus, in examining the claim elements as recited by the limitations individually and as an ordered combination, as a whole the independent claims do not recite what have the courts have identified as "significantly more". The claim is not patent eligible.
Claims 10, and 12-16
Step 1: The claims recite a system; therefore, they fall into the statutory category of machines.
Step 2A Prong 1: The claims recite the same mental processes as claims 1, 3--5 and 7-9, respectively.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. Claims 10, and 12-16 recite generic computer components, namely “processor and a memory storing instructions”. As before, the mere recitation that the method is to be performed on a generic computer amounts to a mere instruction to apply the exception on the computer. See MPEP § 2106.05(f). With that exception, the analysis mirrors that of claims 1, 3--5 and 7-9, respectively.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The analysis, with the one exception noted above, mirrors that of claims 1, 3--5 and 7-9, respectively.
Claims 17 and 19-20
Step 1: The claims recite One or more non-transitory machine-readable storage devices; therefore, they fall into the statutory category of machines.
Step 2A Prong 1: The claims 17 and 19-20 recite the same mental processes as claims 1 and 3-5, respectively.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. Claims 17 and 19-20 recite generic computer components, namely “One or more non-transitory machine-readable storage devices storing instructions for predicting permeability in a subsurface formation, the instructions being executable by one or more processors”. As before, the mere recitation that the method is to be performed on a generic computer amounts to a mere instruction to apply the exception on the computer. See MPEP § 2106.05(f). With that exception, the analysis mirrors that of claims 1 and 3-5, respectively.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The analysis, with the one exception noted above, mirrors that of claims 1 and 3-5, respectively.
Claim Rejections - 35 USC § 102
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, 3-4, 10, 12-13, 17 and 19 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Cheddad et al. (“Enhancing Petrophysical Studies with Machine Learning: A Field Case Study on Permeability Prediction in Heterogeneous Reservoirs”, University of science and technology Houari Boumediene, 11 May 2023).
Regarding claim 1.
Cheddad discloses a method for predicting permeability in a subsurface formation (see page 1, introduction, “The study employed three machine learning algorithms, namely Artificial Neural Network (ANN), Random Forest Classifier (RFC), and Support Vector Machine (SVM), to predict permeability Log from conventional logs and match it with core data. The primary objective of this study was to compare the effectiveness of the three machine learning algorithms in predicting permeability and determine the optimal prediction method. The findings will be used to improve reservoir simulation and locate future wells more accurately.”),
the method comprising: obtaining well log data and core sample data from wells in the subsurface formation (see page 3, section 2.1.1, “The dataset's inputs for permeability prediction include depth, density, gamma ray, sonic, neutron [i.e. well log data], porosity, and permeability [i.e. core sample data], with the last two obtained from plugs.”);
selecting input features from the well log data based on a statistical analysis of the well log data and the core sample data (see page 4, section 2.1.2, “The Heat Map in figure 6 shows a relatively high correlation coefficient (inversely proportional) between sonic & density inputs. On the other hand, all the other features have lower correlation coefficients.”, also see “Table 2 shows a descriptive statistics including those that summarize the central tendency dispersion and the shape of the whole dataset”, also see page 5, section 2.1.4. Data Reduction and Principal Component Analysis (PCA) is a data reduction technique);
forming a training dataset including the input features and corresponding core sample data as labeled output data (see page 3-4, section 2.1.1, “After cleaning the datasets, the next step is to integrate all the datasets from different wells into a single dataset. This integration is necessary to prepare the dataset for the rock typing classification, which is used as the label for the supervised classification method”);
training, using the training dataset, a machine learning model; and predicting permeability data for non-cored wells in the subsurface formation based on the trained machine learning model (see page 1, “Despite advances in petroleum engineering technology, there are no logging tools available that can provide continuous permeability log measurement. Therefore, this study focused on estimating the permeability log from existing plugs in cored wells and predicting this rock property in uncored wells or intervals using a supervised classification method of rock types obtained from a statistical approach. A comparison between ANN, RFC, and SVM machine learning algorithms was made to define the most accurate model to use in this field.”, also see page 7, “The objective of this study is to predict the rock type of each example to predict the permeability log. Therefore, it involves a classic data mining task of supervised learning using classification algorithms such as Artificial Neural Networks (ANN), Random Forest, and Support Vector Machine (SVM). The aim is to determine the best algorithm for the prediction by defining the goodness of each model. The machine learning classifier consists of two stages: [Symbol font/0xB7] Training set: It takes training data, which is a set of data points with their corresponding correct labels, and tries to learn a pattern for how the points map to the label. This stage represents 80% of the total dataset. [Symbol font/0xB7] Test set: Once the classifier is trained, it acts as a function that takes in additional data points and outputs predicted classifications for them. The prediction is a specific label. This stage represents 20% of the total dataset without labels. The three classification algorithms used in this study are ANN, Random Forest, and SVM”).
Regarding claim 3.
Cheddad discloses the method of claim 1,
Cheddad further discloses wherein the machine learning model comprises a neural network (see page 7, “The objective of this study is to predict the rock type of each example to predict the permeability log. Therefore, it involves a classic data mining task of supervised learning using classification algorithms such as Artificial Neural Networks (ANN), Random Forest, and Support Vector Machine (SVM). The aim is to determine the best algorithm for the prediction by defining the goodness of each model. The machine learning classifier consists of two stages: [Symbol font/0xB7] Training set: It takes training data, which is a set of data points with their corresponding correct labels, and tries to learn a pattern for how the points map to the label. This stage represents 80% of the total dataset. [Symbol font/0xB7] Test set: Once the classifier is trained, it acts as a function that takes in additional data points and outputs predicted classifications for them. The prediction is a specific label. This stage represents 20% of the total dataset without labels. The three classification algorithms used in this study are ANN, Random Forest, and SVM”).
Regarding claim 4.
Cheddad discloses the method of claim 1,
Cheddad further discloses wherein the statistical analysis comprises: determining a correlation factor between the well log data and the core sample data (see page 4, section 2.1.2, “The Heat Map in figure 6 shows a relatively high correlation coefficient (inversely proportional) between sonic & density inputs. On the other hand, all the other features have lower correlation coefficients.”, also see “Table 2 shows a descriptive statistics including those that summarize the central tendency dispersion and the shape of the whole dataset”, also see page 5, section 2.1.4. Data Reduction and Principal Component Analysis (PCA) is a data reduction technique).
Claims 10 and 12-13 recites a system to perform the method recited in claims 1 and 3-4. Therefore the rejection of claims 1 and 3-4 above applies equally here.
Claims 17 and 19 recites one or more non-transitory machine-readable storage devices to perform the method recited in claims 1 and 3. Therefore the rejection of claims 1 and 3 above applies equally here.
Claim Rejections - 35 USC § 103
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.
Claim(s) 2, 5-6, 11, 14, 18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cheddad et al. (“Enhancing Petrophysical Studies with Machine Learning: A Field Case Study on Permeability Prediction in Heterogeneous Reservoirs”, University of science and technology Houari Boumediene, 11 May 2023) in view of Al-Malki et al. (US 20220260746 A1).
Regarding claim 2.
Cheddad discloses the method of claim 1,
Cheddad further discloses further comprising: determining hydrocarbon reserves in the subsurface formation based at least in part on the predicted permeability data (see page 1 section 1, “accurately predicting petrophysical properties when building a representative reservoir simulation model… By developing a better understanding of the relationship between porosity and permeability in the heterogeneous formations, petrophysicist can make more informed decisions and ultimately enhance reservoir performance.”); a
Cheddad do not teach in response to determining the hydrocarbon reserves, controlling equipment to produce hydrocarbons from the subsurface formation.
Al-Malki teaches in response to determining the hydrocarbon reserves, controlling equipment to produce hydrocarbons from the subsurface formation (see ¶ 46, “a control system (114) may communicate geosteering commands to the drilling system (110) based on well data updates that are further adjusted by the reservoir simulator (160) using a geological model. As such, the control system (114) may generate one or more control signals for drilling equipment (or a logging system may generate for logging equipment) based on an updated well path design and/or a geological model.”).
Both Cheddad and Al-Malki pertain to the problem of determining permeability using neural network, thus being analogous. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Cheddad and Al-Malki to teach the above limitations. The motivation for doing so would be “The method may further include determining, using a neural network and second NMR data, predicted permeability data regarding a predetermined formation within the geological region of interest. The neural network may be trained using the first NMR data and the acquired permeability data. The method may further include determining a predetermined fracture size within the predetermined formation based on the predicted permeability data. The method may further include determining a predetermined type of lost circulation material (LCM) based on the predetermined fracture size. The method may further include transmitting a command to a well system that triggers a well operation using the predetermined type of LCM.” (see Al-Malki Abstract).
Regarding claim 5.
Cheddad discloses the method of claim 4,
Cheddad do not teach limitations of claim 5,
Al-Malki teaches wherein the statistical analysis further comprises: removing data points from the well log data corresponding to permeability core sample data having values less than a specified threshold (see ¶ 68, “In Block 610…training data may be organized for a training operation automatically by a reservoir simulator”, also ¶ 73, “filtered data may be used throughout the training operation in order to tailor data from the training wells to the target well. Likewise, different training filters may be used in different iterations of a training operation to fine tune the training data for training the neural network”).
Both Cheddad and Al-Malki pertain to the problem of determining permeability using neural network, thus being analogous. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Cheddad and Al-Malki to teach the above limitations. The motivation for doing so would be “The method may further include determining, using a neural network and second NMR data, predicted permeability data regarding a predetermined formation within the geological region of interest. The neural network may be trained using the first NMR data and the acquired permeability data. The method may further include determining a predetermined fracture size within the predetermined formation based on the predicted permeability data. The method may further include determining a predetermined type of lost circulation material (LCM) based on the predetermined fracture size. The method may further include transmitting a command to a well system that triggers a well operation using the predetermined type of LCM.” (see Al-Malki Abstract).
Regarding claim 6.
Cheddad and Al-Malki teaches the method of claim 5,
Al-Malki further teaches wherein the specified threshold comprises a resolution of a measurement instrument (see ¶ 17, “acquired permeability data may be based on analyzing core samples in a laboratory, NMR data may be acquired in real-time during drilling operations… Where a single T2 cutoff value may fail to provide an accurate picture of fluid dynamics in a geological region, using NMR data based on multiple T2 cutoff values (e.g., seven different T2 cutoff values) may enable the machine-learning model to approximate the relationship between NMR data for the target well and permeability available for the training wells.”).
Both Cheddad and Al-Malki pertain to the problem of determining permeability using neural network, thus being analogous. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Cheddad and Al-Malki to teach the above limitations. The motivation for doing so would be “The method may further include determining, using a neural network and second NMR data, predicted permeability data regarding a predetermined formation within the geological region of interest. The neural network may be trained using the first NMR data and the acquired permeability data. The method may further include determining a predetermined fracture size within the predetermined formation based on the predicted permeability data. The method may further include determining a predetermined type of lost circulation material (LCM) based on the predetermined fracture size. The method may further include transmitting a command to a well system that triggers a well operation using the predetermined type of LCM.” (see Al-Malki Abstract).
Claims 11 and 14 recites a system to perform the method recited in claims 2 and 5. Therefore the rejection of claims 2 and 5 above applies equally here.
Claims 18 and 20 recites one or more non-transitory machine-readable storage devices to perform the method recited in claims 2 and 4-5. Therefore the rejection of claims 2 and 4-5 above applies equally here.
Claim(s) 7-8 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cheddad et al. (“Enhancing Petrophysical Studies with Machine Learning: A Field Case Study on Permeability Prediction in Heterogeneous Reservoirs”, University of science and technology Houari Boumediene, 11 May 2023) in view of Dargi et al. (“Optimizing acidizing design and effectiveness assessment with machine learning for predicting post-acidizing permeability”, Scientific Reports | (2023) 13:11851).
Regarding claim 7.
Cheddad discloses the method of claim 1,
Cheddad do not teach limitations of claim 7,
Dargi teaches wherein the input features comprise one or more of a compressional sonic log, a total porosity log, a volume of calcite log, and a volume of dolomite log (see page 3, table 1 inputs, “Sonic Transit Time (DT), Density Tool Reading (NPHI), Bulk Den sity (RHOB), PHIT (total porosity)… Depth, Computed gamma-ray log (CGR), Spectral gamma-ray log (SGR), Neutron porosity log (NPHI), and density log (RHOB)”, also see page 5, “The input variables used for the machine learning model included parameters such as initial permeability, porosity, skin factor, the fraction of calcite mineral, acid injection rate, and injected acid volume.”).
Both Cheddad and Dargi pertain to the problem of determining permeability using neural network, thus being analogous. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Cheddad and Dargi to teach the above limitations. The motivation for doing so would be “In this study, to predict the permeability after acidizing in oil and gas reservoirs, three machine learning models, namely artificial neural networks, random forest, and XGBoost, along with genetic programming were used to estimate permeability changes after acidizing. These models are utilized to estimate permeability changes following acidizing operations. Training of the models involved a dataset comprising 218 acidizing operations conducted in diverse reservoirs across Iran. The input parameters, namely permeability, porosity, skin factor, calcite mineral fraction, acid injection rate, and injected acid volume, were optimized through the use of a genetic algorithm. Statistical and graphical analysis of the results demonstrates that genetic programming outperformed the other machine learning techniques, yielding superior performance with R square and RMSE values of 0.82 and 17.65, respectively… The findings highlight the potential of genetic programming and machine learning algorithms in accurately predicting post acidizing permeability, thereby aiding in acidizing design, effectiveness assessment, and ultimately enhancing oil and gas production rates.” (see Dargi Abstract).
Regarding claim 8.
Cheddad discloses the method of claim 1,
Cheddad do not teach limitations of claim 8,
Dargi teaches wherein training the machine learning model comprises: splitting the training dataset into a training set and a testing set based on random selection (see page 6, “This study employed a training–testing split approach, in which 80% of the available data was randomly assigned to the training set while the remaining 20% was allocated to the testing dataset. This methodology ensures that the model is trained on a sufficient amount of data to learn patterns and trends while also being evaluated on a separate set of data to assess its generalizability and performance on new, unseen data. The split was performed randomly to ensure that the training and testing datasets are representative of the overall data distribution and to prevent any bias in the model.”).
Both Cheddad and Dargi pertain to the problem of determining permeability using neural network, thus being analogous. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Cheddad and Dargi to teach the above limitations. The motivation for doing so would be “In this study, to predict the permeability after acidizing in oil and gas reservoirs, three machine learning models, namely artificial neural networks, random forest, and XGBoost, along with genetic programming were used to estimate permeability changes after acidizing. These models are utilized to estimate permeability changes following acidizing operations. Training of the models involved a dataset comprising 218 acidizing operations conducted in diverse reservoirs across Iran. The input parameters, namely permeability, porosity, skin factor, calcite mineral fraction, acid injection rate, and injected acid volume, were optimized through the use of a genetic algorithm. Statistical and graphical analysis of the results demonstrates that genetic programming outperformed the other machine learning techniques, yielding superior performance with R square and RMSE values of 0.82 and 17.65, respectively… The findings highlight the potential of genetic programming and machine learning algorithms in accurately predicting post acidizing permeability, thereby aiding in acidizing design, effectiveness assessment, and ultimately enhancing oil and gas production rates.” (see Dargi Abstract).
Claim 15 recites a system to perform the method recited in claim 7. Therefore the rejection of claim 7 above applies equally here.
Claim(s) 9 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cheddad et al. (“Enhancing Petrophysical Studies with Machine Learning: A Field Case Study on Permeability Prediction in Heterogeneous Reservoirs”, University of science and technology Houari Boumediene, 11 May 2023) in view of Dargi et al. (“Optimizing acidizing design and effectiveness assessment with machine learning for predicting post-acidizing permeability”, Scientific Reports | (2023) 13:11851) in view of Al-Malki et al. (US 20220260746 A1).
Regarding claim 9.
Cheddad and Dargi teaches the method of claim 8,
Cheddad and Dargi do not teach the limitation of claim 9.
Al-Malki teaches wherein training the machine learning model comprises: performing cross validation by iteratively training the machine learning model; and resampling the training set and the testing set before each iteration (see ¶ 77, “Based on the previous cross-correlation values, a reservoir simulator may automatically modify one or more training parameters in order to increase the cross-correlation value. For example, new training wells may be added to a training well selection for a next iteration of a training operation, and/or previous training wells may be excluded from the next iteration. Likewise, users may change particular values (e.g., through a user interface) based on their geological knowledge, e.g., with respect to possible T2 cutoff selection or training filters. Likewise, the training well selection may be modified in several iterations in order to analyze changes in cross-correlation values.”).
Cheddad, Dargi and Al-Malki pertain to the problem of determining permeability using neural network, thus being analogous. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Cheddad, Dargi and Al-Malki to teach the above limitations. The motivation for doing so would be “The method may further include determining, using a neural network and second NMR data, predicted permeability data regarding a predetermined formation within the geological region of interest. The neural network may be trained using the first NMR data and the acquired permeability data. The method may further include determining a predetermined fracture size within the predetermined formation based on the predicted permeability data. The method may further include determining a predetermined type of lost circulation material (LCM) based on the predetermined fracture size. The method may further include transmitting a command to a well system that triggers a well operation using the predetermined type of LCM.” (see Al-Malki Abstract).
Claim 16 recites a system to perform the method recited in claims 8-9. Therefore the rejection of claims 8-9 above applies equally here.
Related prior arts:
Li et al. (US 20220351037 A1) teaches accessing a plurality of geo-exploration data from a first drilling site, wherein the plurality of geo-exploration data include spectroscopic infra-red (IR) data and well logs, wherein at least portions of the plurality of geo-exploration data are based on measurements of core samples taken from the first drilling site.
Jones et al. (US 20200378239 A1) teaches Quality factors associated with formation pressure measurements at various depths in the geologic formation are determined based on one or more well logs of formation properties in a geologic formation. A formation testing tool with two or more probes is positioned in a borehole of the geologic formation based on the quality factors. The two or more probes in the borehole perform respective formation pressure measurements, where each formation pressure measurement is performed at a different depth.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IMAD M KASSIM whose telephone number is (571)272-2958. The examiner can normally be reached 10:30AM-5:30PM, M-F (E.S.T.).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael J. Huntley can be reached at (303) 297 - 4307. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/IMAD KASSIM/Primary Examiner, Art Unit 2129
Prosecution Insights