DETAILED ACTION
Claims 1-10 are presented for examination.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
The drawings received on 17 November 2022 are accepted.
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because:
The abstract includes phrases which can be implied. Examiner suggests amending the abstract as follows:
Predicting a relative permeability curve based on machine learning. Inputting log curve dataoutputting water saturation endpoint values
A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
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.
Claim 4 is 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 pre-AIA the applicant regards as the invention.
Claim 4 recites “screening an optimal logging curve….” It is unclear what “screening” means in this context. Screening is sometimes a synonym for a filtering but if that were the intended meaning then it is unclear how filtering is performed “with an empirical equation, a rock physical model and a deep learning method.” For purposes of compact prosecution, the Examiner is interpreting the “screening” as a form of constraining or being based on.
Claim 4 recites “complementing other second logging curve data according to the optimal logging curve.” It is unclear what it means to complement this logging curve data “according to” the optimal logging curve. In particular, the “optimal logging curve” is “from the second logging curve data.” Thus, this last clause now appears to be circular. The logging curve is based on the data; yet now the data is somehow complemented based on the curve. The amount of speculation required to determine the possible intended meaning of this limitation exceeds what is feasible even for purposes of compact prosecution. Whatever the meaning and scope of this limitation, Examiner believes that either pairing the data or displaying the data is a “complementing” of respective data and curves.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3 and 5-10
Claims 1-3 and 5-10 are rejected under 35 U.S.C. 103 as being unpatentable over Mathew, E.S., et al. “Artificial Intelligence Coreflooding Simulator for Special Core data Analysis” Society of Petroleum Engineers Reservoir Evaluation & Engineering, pp. 780-808 (May 2021) [herein “Alsumaiti”] in view of US patent 11,828,168 B2 AlTammar, et al. [herein “AlTammar”].
Claim 1 recites “1. A method for predicting a relative permeability curve based on machine learning.” Mathew page 800 Conclusion discloses “Fast and accurate estimation of relative permeability (Kr) and capillary pressure (Pc) curves is critical in multiphase flow in porous media. This paper proposed an AI workflow that was developed to predict Kr and Pc curves.” Mathew page 783 Methodology section discloses “The prediction of relative permeability and capillary pressure curves.”
Claim 1 further recites “comprising: acquiring relative permeability curve data of a rock sample and logging curve data of a well where the rock sample is located, the relative permeability curve data comprising water saturations and relative permeabilities corresponding to different water saturations.” Mathew page 780 fifth paragraph discloses “Relative permeability can be calculated from the pressure drop and the injected water/oil ratio using Darcy’s law assuming no change in differential pressure and saturation profiles along the core.” The calculated relative permeability in the measurement section is an acquired relative permeability curve data of a rock sample. The core is a rock sample. The saturation profiles along the core are a water/oil saturations.
Mathew does not explicitly disclose logging data; however, in analogous art of reservoir data analysis, AlTammar column 2 lines 5-6 teaches “The instructions further include training a PCML model using the obtained well logs data as inputs.”
AlTammar column 1 lines 10-14 teach:
A well log is a detailed and sequential collection of one category of data (e.g., gamma ray, sonic, porosity, resistivity, density, etc.) for a geological formation by using a logging tool along the path of a well borehole in the ground.
Well log data of gamma ray, sonic, resistivity, and density are all well log data.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Mathew and AlTammar. One having ordinary skill in the art would have found motivation to use well log data into the system of artificial intelligence core data analysis for the advantageous purpose of “determining one or more mechanical properties for well planning.” See AlTammar column 2 lines 15-16.
Claim 1 further recites “selecting a part of the relative permeability curve data and a part of the logging curve data as sample relative permeability curve data and sample logging curve data.” Mathew page 785 sixth paragraph discloses:
the final list of parameters that were fed into the ML algorithm as features are core dimensions, porosity (
ϕ
), permeability (K), pressure drop (ΔP), average water saturation of the core, change in water saturation (Sw) along the core in selected gridblocks, and injection rates of both oil and water. The pressure drop and the average water saturation points included in the features were equal in number and correspond to the specific timesteps mentioned earlier.
The parameters selected to input into the ML algorithm are selected data.
Mathew does not explicitly disclose logging data; however, in analogous art of reservoir data analysis, AlTammar column 2 lines 5-6 teaches “The instructions further include training a PCML model using the obtained well logs data as inputs.”
AlTammar column 1 lines 10-14 teach:
A well log is a detailed and sequential collection of one category of data (e.g., gamma ray, sonic, porosity, resistivity, density, etc.) for a geological formation by using a logging tool along the path of a well borehole in the ground.
Well log data of gamma ray, sonic, resistivity, and density are all well log data.
Claim 1 further recites “taking the sample logging curve data as an input and a water saturation starting value in the sample relative permeability curve data as a marker, and training a relative permeability curve starting point model with a machine learning algorithm to obtain a first relative permeability curve starting point model.” Mathew page 785 sixth paragraph discloses:
the final list of parameters that were fed into the ML algorithm as features are core dimensions, porosity (
ϕ
), permeability (K), pressure drop (ΔP), average water saturation of the core, change in water saturation (Sw) along the core in selected gridblocks, and injection rates of both oil and water. The pressure drop and the average water saturation points included in the features were equal in number and correspond to the specific timesteps mentioned earlier.
The parameters selected to input into the ML algorithm are selected data. Training the model is training with the machine learning algorithm. See further Mathew page 784 figure 1. Without loss of generality some part of the permeability is a starting point.
Claim 1 further recites “obtaining a predicted water saturation starting value according to the first relative permeability curve starting point model.” Mathew page 786 first paragraph discloses:
the target was set as the entirety of Kr and Pc curves for two samples, whereas for another sample, the target was the Corey exponents nwd and nod, saturation endpoints Swc and
K
r
o
*
, and the fitting parameters for
P
c
-
c
w
d
, cod, awd, aod, and bd. Therefore, the input data set for an ML model comprises the discussed features and target strung together as a single file.
The permeability curve is an obtained first relative permeability. The saturation corresponds with obtained water saturation values. Without loss of generality some part of the permeability is a starting point.
Claim 1 further recites “taking the sample logging curve data and the predicted water saturation starting value as an input, and a relative permeability in the sample relative permeability curve data as a marker, and training a relative permeability model with the machine learning algorithm to obtain a first relative permeability model.” Mathew page 785 sixth paragraph discloses:
the final list of parameters that were fed into the ML algorithm as features are core dimensions, porosity (
ϕ
), permeability (K), pressure drop (ΔP), average water saturation of the core, change in water saturation (Sw) along the core in selected gridblocks, and injection rates of both oil and water. The pressure drop and the average water saturation points included in the features were equal in number and correspond to the specific timesteps mentioned earlier.
The parameters selected to input into the ML algorithm are selected data. Training the model is training with the machine learning algorithm. See further Mathew page 784 figure 1.
Claim 1 further recites “obtaining a predicted relative permeability according to the first relative permeability model.” Mathew page 786 first paragraph discloses:
the target was set as the entirety of Kr and Pc curves for two samples, whereas for another sample, the target was the Corey exponents nwd and nod, saturation endpoints Swc and
K
r
o
*
, and the fitting parameters for
P
c
-
c
w
d
, cod, awd, aod, and bd. Therefore, the input data set for an ML model comprises the discussed features and target strung together as a single file.
The permeability curve is an obtained first relative permeability.
Claim 1 further recites “and plotting a relative permeability curve according to the predicted water saturation starting value and the predicted relative permeability corresponding to the predicted water saturation starting value.” Mathew page 794 figure 15 shows “Comparison between ML-predicted Kr curves and analytically estimated Kr curves for Reservoir Sample 1.” The figure is a plot of the relative permeability curve as predicted by the machine learning value. Mathew page 794 figure 15 shows “water saturation (fraction)” as the x-axis.
Claim 2 further recites “2. The method for predicting a relative permeability curve based on machine learning according to claim 1, wherein the logging curve data comprise one or more of a gamma-ray (GR), a depth, a diameter, a spontaneous potential (SP), a time difference, a neutron, an acoustic (AC), a shallow resistivity, a gradient resistivity, an induction conductivity (COND) and a density (DEN).” From the above list of alternatives the Examiner is selecting “a gamma-ray (GR).”
Mathew does not explicitly disclose logging data; however, in analogous art of reservoir data analysis, AlTammar column 2 lines 5-6 teaches “The instructions further include training a PCML model using the obtained well logs data as inputs.”
AlTammar column 1 lines 10-14 teach:
A well log is a detailed and sequential collection of one category of data (e.g., gamma ray, sonic, porosity, resistivity, density, etc.) for a geological formation by using a logging tool along the path of a well borehole in the ground.
Well log data of gamma ray, sonic, resistivity, and density are all well log data. See further AlTammar column 2 line 66.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Mathew and AlTammar. One having ordinary skill in the art would have found motivation to use well log data into the system of artificial intelligence core data analysis for the advantageous purpose of “determining one or more mechanical properties for well planning.” See AlTammar column 2 lines 15-16.
Claim 3 further recites “3. The method for predicting a relative permeability curve based on machine learning according to claim 1, after the acquiring relative permeability curve data of a rock sample and logging curve data of a well where the rock sample is located, further comprising: performing preprocessing on the logging curve data; and taking processed logging curve data as new logging curve data.” Mathew page 783 third paragraph discloses “once the data are secured, they have to be preprocessed depending on the requirements of the chosen algorithm.”
Claim 5 further recites “5. The method for predicting a relative permeability curve based on machine learning according to claim 1, after the obtaining a first relative permeability curve starting point model, further comprising: testing the first relative permeability curve starting point model, specifically comprising: respectively selecting remaining relative permeability curve data and remaining logging curve data except the sample relative permeability curve data and the sample logging curve data as test relative permeability curve data and test logging curve data.” Mathew page 786 second paragraph discloses “the remaining 15% was used for blind testing.” See also Mathew page 786 figure 2 showing “testing data.”
Claim 5 further recites “inputting the test logging curve data to the first relative permeability curve starting point model to obtain a predicted water saturation starting value.” Mathew page 786 figure 2 shows inputting the test data into the “test model.”
Claim 5 further recites “establishing a loss function with a mean square error (MSE) according to the predicted water saturation starting value and a water saturation starting value in the test relative permeability curve data.” Mathew page 804 Appendix A discloses “This loss function is quantified by the mean squared error.” Mathew page 786 figure 2 shows RMSE output from the test model which received the test data. Mathew figure 2 caption defines “RMSE=root mean squared error.”
Claim 5 further recites “training the first relative permeability curve starting point model completely in case of a minimum of the loss function to obtain a well-trained relative permeability curve starting point model.” Mathew page 786 third paragraph discloses “tries to minimize the loss function, thereby establishing a better relationship between the features and the target as the training process continues.” Minimizing the loss function corresponds with completing the training at a minimum of the loss function to obtain the trained model. See further Mathew page 786 figure 2.
Claim 5 further recites “and taking the well-trained relative permeability curve starting point model as a new first relative permeability curve starting point model, and returning to the step of "obtaining a predicted water saturation starting value according to the first relative permeability curve starting point model".” Mathew page 786 third paragraph discloses “The algorithm initializes the model with a first guess and in subsequent steps tries to minimize the loss function, thereby establishing a better relationship between the features and the target as the training process continues.” The teaching of subsequent steps corresponds to the iterative training of the machine learning algorithm with the training data. A subsequent step of minimizing the loss function is taking the new starting point model as a next starting point and returning to the start of the training iterations.
Claim 6 further recites “6. The method for predicting a relative permeability curve based on machine learning according to claim 1, after the obtaining a first relative permeability model, further comprising: testing the first relative permeability model, specifically comprising: respectively selecting remaining relative permeability curve data and remaining logging curve data except the sample relative permeability curve data and the sample logging curve data as test relative permeability curve data and test logging curve data.” Mathew page 786 second paragraph discloses “the remaining 15% was used for blind testing.” See also Mathew page 786 figure 2 showing “testing data.”
Claim 6 further recites “inputting the test logging curve data and the predicted water saturation starting value to the first relative permeability model to obtain a predicted relative permeability.” Mathew page 786 figure 2 shows inputting the test data into the “test model.”
Claim 6 further recites “establishing a loss function with an MSE according to the predicted relative permeability and a relative permeability in the test relative permeability curve data.” Mathew page 804 Appendix A discloses “This loss function is quantified by the mean squared error.” Mathew page 786 figure 2 shows RMSE output from the test model which received the test data. Mathew figure 2 caption defines “RMSE=root mean squared error.”
Claim 6 further recites “training the first relative permeability model completely in case of a minimum of the loss function to obtain a well-trained relative permeability model.” Mathew page 786 third paragraph discloses “tries to minimize the loss function, thereby establishing a better relationship between the features and the target as the training process continues.” Minimizing the loss function corresponds with completing the training at a minimum of the loss function to obtain the trained model. See further Mathew page 786 figure 2.
Claim 6 further recites “and taking the well-trained relative permeability model as a new first relative permeability model, and returning to the step of "obtaining a predicted relative permeability according to the first relative permeability model".” Mathew page 786 third paragraph discloses “The algorithm initializes the model with a first guess and in subsequent steps tries to minimize the loss function, thereby establishing a better relationship between the features and the target as the training process continues.” The teaching of subsequent steps corresponds to the iterative training of the machine learning algorithm with the training data. A subsequent step of minimizing the loss function is taking the new starting point model as a next starting point and returning to the start of the training iterations.
Claim 7 further recites “7. The method for predicting a relative permeability curve based on machine learning according to claim 1, wherein the machine learning algorithm comprises a random forest (RF), an adaptive boosting (AdaBoost), a gradient boosted decision tree (GBDT) and an extreme gradient boosting (XGBoost).” From the above list of alternatives the Examiner is selecting “a random forest (RF).”
Mathew page 782 sixth paragraph discloses “the main algorithms analyzed in this study are gradient boosting (GB), random forest (RF), extreme GB (XGBoost), and SVM.”
Claim 8 further recites “8. A system for predicting a relative permeability curve based on machine learning.” Mathew page 800 Conclusion discloses “Fast and accurate estimation of relative permeability (Kr) and capillary pressure (Pc) curves is critical in multiphase flow in porous media. This paper proposed an AI workflow that was developed to predict Kr and Pc curves.” Mathew page 783 Methodology section discloses “The prediction of relative permeability and capillary pressure curves.”
Claim 8 further recites “comprising: a sample acquisition module configured to acquire relative permeability curve data of a rock sample and logging curve data of a well where the rock sample is located, the relative permeability curve data comprising water saturations and relative permeabilities corresponding to different water saturations.” Mathew page 780 fifth paragraph discloses “Relative permeability can be calculated from the pressure drop and the injected water/oil ratio using Darcy’s law assuming no change in differential pressure and saturation profiles along the core.” The calculated relative permeability in the measurement section is an acquired relative permeability curve data of a rock sample. The core is a rock sample. The saturation profiles along the core are a water/oil saturations.
Mathew does not explicitly disclose logging data; however, in analogous art of reservoir data analysis, AlTammar column 2 lines 5-6 teaches “The instructions further include training a PCML model using the obtained well logs data as inputs.”
AlTammar column 1 lines 10-14 teach:
A well log is a detailed and sequential collection of one category of data (e.g., gamma ray, sonic, porosity, resistivity, density, etc.) for a geological formation by using a logging tool along the path of a well borehole in the ground.
Well log data of gamma ray, sonic, resistivity, and density are all well log data.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Mathew and AlTammar. One having ordinary skill in the art would have found motivation to use well log data into the system of artificial intelligence core data analysis for the advantageous purpose of “determining one or more mechanical properties for well planning.” See AlTammar column 2 lines 15-16.
Claim 8 further recites “a sample data selection module configured to select a part of the relative permeability curve data and a part of the logging curve data as sample relative permeability curve data and sample logging curve data.” Mathew page 785 sixth paragraph discloses:
the final list of parameters that were fed into the ML algorithm as features are core dimensions, porosity (
ϕ
), permeability (K), pressure drop (ΔP), average water saturation of the core, change in water saturation (Sw) along the core in selected gridblocks, and injection rates of both oil and water. The pressure drop and the average water saturation points included in the features were equal in number and correspond to the specific timesteps mentioned earlier.
The parameters selected to input into the ML algorithm are selected data.
Mathew does not explicitly disclose logging data; however, in analogous art of reservoir data analysis, AlTammar column 2 lines 5-6 teaches “The instructions further include training a PCML model using the obtained well logs data as inputs.”
AlTammar column 1 lines 10-14 teach:
A well log is a detailed and sequential collection of one category of data (e.g., gamma ray, sonic, porosity, resistivity, density, etc.) for a geological formation by using a logging tool along the path of a well borehole in the ground.
Well log data of gamma ray, sonic, resistivity, and density are all well log data.
Claim 8 further recites “a first relative permeability curve starting point model training module configured to take the sample logging curve data as an input and a water saturation starting value in the sample relative permeability curve data as a marker, and train a relative permeability curve starting point model with a machine learning algorithm to obtain a first relative permeability curve starting point model.” Mathew page 785 sixth paragraph discloses:
the final list of parameters that were fed into the ML algorithm as features are core dimensions, porosity (
ϕ
), permeability (K), pressure drop (ΔP), average water saturation of the core, change in water saturation (Sw) along the core in selected gridblocks, and injection rates of both oil and water. The pressure drop and the average water saturation points included in the features were equal in number and correspond to the specific timesteps mentioned earlier.
The parameters selected to input into the ML algorithm are selected data. Training the model is training with the machine learning algorithm. See further Mathew page 784 figure 1. Without loss of generality some part of the permeability is a starting point.
Claim 8 further recites “a water saturation starting value prediction module configured to obtain a predicted water saturation starting value according to the first relative permeability curve starting point model.” Mathew page 786 first paragraph discloses:
the target was set as the entirety of Kr and Pc curves for two samples, whereas for another sample, the target was the Corey exponents nwd and nod, saturation endpoints Swc and
K
r
o
*
, and the fitting parameters for
P
c
-
c
w
d
, cod, awd, aod, and bd. Therefore, the input data set for an ML model comprises the discussed features and target strung together as a single file.
The permeability curve is an obtained first relative permeability. The saturation corresponds with obtained water saturation values. Without loss of generality some part of the permeability is a starting point.
Claim 8 further recites “a first relative permeability model training module configured to take the sample logging curve data and the predicted water saturation starting value as an input, and a relative permeability in the sample relative permeability curve data as a marker, and train a relative permeability model with the machine learning algorithm to obtain a first relative permeability model.” Mathew page 785 sixth paragraph discloses:
the final list of parameters that were fed into the ML algorithm as features are core dimensions, porosity (
ϕ
), permeability (K), pressure drop (ΔP), average water saturation of the core, change in water saturation (Sw) along the core in selected gridblocks, and injection rates of both oil and water. The pressure drop and the average water saturation points included in the features were equal in number and correspond to the specific timesteps mentioned earlier.
The parameters selected to input into the ML algorithm are selected data. Training the model is training with the machine learning algorithm. See further Mathew page 784 figure 1.
Claim 8 further recites “a relative permeability prediction module configured to obtain a predicted relative permeability according to the first relative permeability model.” Mathew page 786 first paragraph discloses:
the target was set as the entirety of Kr and Pc curves for two samples, whereas for another sample, the target was the Corey exponents nwd and nod, saturation endpoints Swc and
K
r
o
*
, and the fitting parameters for
P
c
-
c
w
d
, cod, awd, aod, and bd. Therefore, the input data set for an ML model comprises the discussed features and target strung together as a single file.
The permeability curve is an obtained first relative permeability.
Claim 8 further recites “and a relative permeability curve plotting module configured to plot a relative permeability curve according to the predicted water saturation starting value and the predicted relative permeability corresponding to the predicted water saturation starting value.” Mathew page 794 figure 15 shows “Comparison between ML-predicted Kr curves and analytically estimated Kr curves for Reservoir Sample 1.” The figure is a plot of the relative permeability curve as predicted by the machine learning value. Mathew page 794 figure 15 shows “water saturation (fraction)” as the x-axis.
Dependent claim 9 is substantially similar to claim 3 above and is rejected for the same reasons.
Dependent claim 10 is substantially similar to claim 5 above and is rejected for the same reasons.
Dependent Claim 4
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Mathew and AlTammar as applied to claim 3 above, and further in view of US patent 9,229,127 B2 Leseur [herein “Leseur”].
Claim 4 further recites “4. The method for predicting a relative permeability curve based on machine learning according to claim 3, wherein the performing preprocessing on the logging curve data specifically comprises: selecting a marker layer for the logging curve data to obtain first logging curve data.” Mathew page 785 sixth paragraph discloses:
the final list of parameters that were fed into the ML algorithm as features are core dimensions, porosity (
ϕ
), permeability (K), pressure drop (ΔP), average water saturation of the core, change in water saturation (Sw) along the core in selected gridblocks, and injection rates of both oil and water. The pressure drop and the average water saturation points included in the features were equal in number and correspond to the specific timesteps mentioned earlier.
The parameters selected to input into the ML algorithm are selected data. Without loss of generality, any of the selected data is a marker layer within the data.
Mathew does not explicitly disclose logging data; however, in analogous art of reservoir data analysis, AlTammar column 2 lines 5-6 teaches “The instructions further include training a PCML model using the obtained well logs data as inputs.”
AlTammar column 1 lines 10-14 teach:
A well log is a detailed and sequential collection of one category of data (e.g., gamma ray, sonic, porosity, resistivity, density, etc.) for a geological formation by using a logging tool along the path of a well borehole in the ground.
Well log data of gamma ray, sonic, resistivity, and density are all well log data.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Mathew and AlTammar. One having ordinary skill in the art would have found motivation to use well log data into the system of artificial intelligence core data analysis for the advantageous purpose of “determining one or more mechanical properties for well planning.” See AlTammar column 2 lines 15-16.
Claim 4 further recites “performing consistency correction on the first logging curve data with a plotting tool to obtain second logging curve data.” Mathew does not explicitly disclose a consistency correction; however, in analogous art of reservoir analysis, Leseur column 16 lines 37-51 teaches:
For example, one or more embodiments provide a filtering preprocessing based on the discrepancy between log and core porosity that leads to a better prediction (Eq. 1-3). According to one or more embodiments, the input parameters traditionally arbitrarily fixed by the user are optimized through a constrained nonlinear optimization algorithm, and the individual weights of every input property influencing the output KNN-based permeability prediction type can be optimized (Eq. 5). According to one or more embodiments, residuals from a blind test on a one-by-one sample basis, as described in the BTP, are defined as the most effective and least biased objective function to minimize (Eq. 7); and/or the latter objective function sets the basis for measurable and easy-to-interpret quality criteria and QC of the permeability prediction.
The discrepancy is a measure of consistency. Accordingly, filtering preprocessing based on the discrepancy is a consistency correction.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Mathew, AlTammar, and Leseur. One having ordinary skill in the art would have found motivation to use preprocessing into the system of artificial intelligence core data analysis for the advantageous purpose “for measurable and easy-to-interpret quality criteria and QC of the permeability prediction.” See Leseur column 16 lines 37-51.
Claim 4 further recites “screening an optimal logging curve from the second logging curve data with an empirical equation, a rock physical model and a deep learning method.” Mathew page 780 fifth paragraph discloses “Relative permeability can be calculated from the pressure drop and the injected water/oil ratio using Darcy’s law assuming no change in differential pressure and saturation profiles along the core.” Darcy’s law is an empirical equation and rock physics modeling. Mathew page 784 first paragraph disclose “to ensure that the curves are in line with the physics.” Ensuring the curves are in-line with physics is ensuring the curves are according to a rock physical model.
Mathew page 782 sixth paragraph discloses “the main algorithms analyzed in this study are gradient boosting (GB), random forest (RF), extreme GB (XGBoost), and SVM.” The machine learning algorithms are at least one deep learning method.
Claim 4 further recites “and complementing other second logging curve data according to the optimal logging curve.” See §112 section above regarding claim interpretation of this limitation.
Mathew page 785 sixth paragraph discloses “the two sets of output data sets generated from the coreflooding simulator mentioned in the previous section were combined to form a single database.” This combination of data to form a single data set is one interpretation of complementing respective data.
Mathew page 797 figures 21-23 show respective history matched data which displays both “simulation” and “experimental” data. The display of both types of data is one interpretation of complementing one type of data with the other type of data being displayed.
Conclusion
Prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 11,913,865 B2 Alsumaiti, et al.
teaches
“In-Situ Prediction and Dynamic Visualization of Relative Permeability.” Alsumaiti column 4 lines 26-29 disclose “predicting the in-situ relative permeability (Kr) and capillary pressure (Pc) characteristics of a rock sample using machine learning inference.”
US 11,402,315 B2 Ramsay; Travis St. George et al.
Estimating relative permeability and capillary pressures of a geological formation based on multiphase upscaling
US 8,510,242 B2 Al-Fattah; Saud Mohammad A.
Artificial neural network models for determining relative permeability of hydrocarbon reservoirs
US 11,599,790 B2 Pandey; Yogendra Narayan et al.
Deep learning based reservoir modeling
Sudakov, O., et al. "Driving digital rock towards machine learning: Predicting permeability with gradient boosting and deep neural networks" Computers & Geosciences, vol. 127, pp. 91-98 (2019)
Gradient boosting prediction of permeability.
Image-based permeability prediction.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jay B Hann whose telephone number is (571)272-3330. The examiner can normally be reached M-F 10am-7pm EDT.
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, Renee Chavez can be reached at (571) 270-1104. 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.
/Jay Hann/Primary Examiner, Art Unit 2186 9 February 2026