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 .
DETAILED ACTION
Response to Amendments
Claims 1 and 3 have been amended.
Claims 2 and 7-8 have been canceled.
Claims 9-12 have been added.
Claims 1, 3-6, and 9-12 remain pending in the application.
The amendment filed 06/29/2026 is sufficient to overcome the 112(a) and 112(b) rejections. The previous rejections have been withdrawn.
The amendment filed 06/29/2026 is sufficient to overcome the 35 U.S.C. 101 rejections. The previous rejections have been withdrawn.
Response to Arguments
Argument 1, regarding the 112(a) and 112(b) rejections, applicant argues that these rejections should be withdrawn in view of claim 8 being canceled. Examiner agrees and the rejections have been withdrawn.
Argument 2, regarding the 101 rejections, applicant argues that the claims provide a technical improvement by training a model with diagenesis samples for more accurate and efficient prediction of diagenetic parameters. Examiner agrees and the 101 rejections have been withdrawn.
Argument 3, regarding the prior art rejections, applicant argues that none of the cited art teaches “obtaining, by a processor, a plurality of diagenesis samples each comprising diagenetic condition parameters and an actual diagenetic parameter evolved therefrom, wherein the diagenetic condition parameters comprise a diagenesis prediction period, and at least further comprise one or combinations of an ion concentration, a mineral content, temperature and pressure conditions, an acidity-basicity, and a porosity; the actual diagenetic parameter at least comprise one or more of the ion concentration, the mineral content, the temperature and pressure conditions, the acidity-basicity and the porosity after an evolution time elapses by the diagenesis prediction period”.
Examiner respectfully disagrees because Adelinet teaches obtaining, by a processor, a plurality of diagenesis samples each comprising diagenetic condition parameters and an actual diagenetic parameter evolved therefrom, wherein the diagenetic condition parameters comprise a diagenesis prediction period, and at least further comprise one or combinations of an ion concentration, a mineral content, temperature and pressure conditions, an acidity-basicity, and a porosity (Mechanical conditions for at least one rock sample are determined for each diagenetic stage, P0019-P0023. Mechanical conditions include porosity, permeability, and mineralogical composition, P0016, P0030-P0031); the actual diagenetic parameter at least comprise one or more of the ion concentration, the mineral content, the temperature and pressure conditions, the acidity-basicity and the porosity after an evolution time elapses by the diagenesis prediction period (Measurements of porosity permeability, and mineralogical composition are identified from geological time t to current time indicating how the parameters may have evolved over time, P0019-P0021).
Applicant also argues that none of the cited art teaches “collecting diagenetic condition parameters; inputting, by the processor, the diagenetic condition parameters into the diagenetic parameter prediction model to obtain predicted diagenetic parameters after a preset diagenesis prediction period expires based on the diagenetic condition parameters; and evaluating, by the processor, a reservoir quality according to the predicted diagenetic parameters, to perform oil and gas exploration”. Examiner notes that Adelinet teaches collecting diagenetic condition parameters (Mechanical conditions for at least one rock sample are determined, P0019-P0023. Mechanical conditions include porosity, permeability, and mineralogical composition, P0016, P0030-P0031). AlSinan teaches inputting the diagenetic condition parameters into the diagenetic parameter prediction model to obtain diagenetic parameters predicted based on the diagenetic condition parameters (predicted transmissibility data is generated using an artificial neural network with fracture image data as the input, P0083. Transmissibility predictions are interpreted as a diagenetic parameter under the broadest reasonable interpretation because diagenetic conditions include pressure conditions as described in P0011 of the specification of the instant application. Fracture image data may include diagenesis or various rock properties, P0058); and evaluating, by the processor, a reservoir quality according to the predicted diagenetic parameters, to perform oil and gas exploration (outcrop images may be used to evaluate oil and gas within a particular geologic region, P0020. “In particular, outcrop images may be obtained to understand various layers in the reservoir, e.g., in regard to diagenesis or various rock properties”, P0058).
Applicant argues that Adelinet predicts parameters of past moments by inverting from current-moment parameters, rather than training a model on historical data to predict the future and that the teachings of Adelinet are not directed towards the use of a diagenetic prediction model. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Examiner notes that AlSinan teaches the use of a predictive model in regards to diagenesis properties (see AlSinan P0058). Furthermore, the claim language does not explicitly require training a model on historical data to predict the future because the claims instead recite a “diagenesis prediction period”, which may reasonably be interpreted as the geological time t to current time reflecting the diagenetic state of the layer of carbonate sediments as taught by P0019-P0021 of Adelinet. Thus, AlSinan in view of Adelinet teaches the limitations as outlined above.
Applicant also argues that there is no motivation to combine the teachings of Adelinet and AlSinan because Adelinet relates to inversion of diagenetic mechanical parameters in carbonate rocks using stratigraphic simulation and effective medium models without any involvement in fracture network modelling or machine learning. Examiner respectfully disagrees because one would have been motivated to make such a combination of collecting measurements of porosity, permeability, and mineralogical composition of rocks over time (see Adelinet P0019-P0023) and collecting data in regard to diagenesis or various rock properties to determine fractures in rocks using an artificial neural network (see AlSinan P0058-P0059) for improved assessment of diagenetic rock properties (see Adelinet P0018).
The full prior art rejections are outlined below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 5, and 9-12 are rejected under 35 U.S.C. 103 as being unpatentable over AlSinan et al (Pub. No.: US 20230097859 A1), hereafter AlSinan in view of Adelinet (Pub. No.: US 20180163516 A1), hereafter Adelinet.
Regarding claims 1 and 9, AlSinan teaches obtaining, by a processor, a plurality of diagenesis samples each comprising diagenetic … parameters (see Abstract, discussing receiving fracture image data, which include diagenesis or various rock properties. See also P0058.)
AlSinan further teaches constructing, by the processor, an initial diagenetic parameter prediction model based on the diagenesis samples and a total dimension of the diagenetic condition parameters (a multiphase simulation model is generated using a deep artificial neural network that predicts upscaled multiphase data. The model is constructed based on fracture image data, P0017. Fracture image data may include diagenesis or various rock properties, P0058); training, by the processor, the initial diagenetic parameter prediction model with the diagenesis samples until a loss between diagenetic parameter predict values obtained by the initial diagenetic parameter prediction model and the actual diagenetic parameters is within a preset loss range or the diagenetic parameter predict values reach a preset accuracy, so as to obtain a trained diagenetic parameter prediction model (The multiphase simulation model is trained with trial-error analysis using fracture image data until the trained model satisfies a predetermined level of prediction accuracy. Based on error data from the trial-error analysis, weights and biases within an artificial neural network may be adjusted to optimize the artificial neural network architecture and the related model parameters, P0017);… inputting the diagenetic condition parameters into the diagenetic parameter prediction model to obtain diagenetic parameters predicted based on the diagenetic condition parameters (predicted transmissibility data is generated using an artificial neural network with fracture image data as the input, P0083. Transmissibility predictions are interpreted as a diagenetic parameter under the broadest reasonable interpretation because diagenetic conditions include pressure conditions as described in P0011 of the specification of the instant application. Fracture image data may include diagenesis or various rock properties, P0058); and evaluating, by the processor, a reservoir quality according to the predicted diagenetic parameters, to perform oil and gas exploration (outcrop images may be used to evaluate oil and gas within a particular geologic region, P0020. “In particular, outcrop images may be obtained to understand various layers in the reservoir, e.g., in regard to diagenesis or various rock properties”, P0058).
AlSinan does not appear to explicitly teach “obtaining, by a processor, a plurality of diagenesis samples each comprising diagenetic condition parameters and an actual diagenetic parameter evolved therefrom, wherein the diagenetic condition parameters comprise a diagenesis prediction period, and at least further comprise one or combinations of an ion concentration, a mineral content, temperature and pressure conditions, an acidity-basicity, and a porosity; the actual diagenetic parameter at least comprise one or more of the ion concentration, the mineral content, the temperature and pressure conditions, the acidity-basicity and the porosity after an evolution time elapses by the diagenesis prediction period… collecting diagenetic condition parameters”.
Adelinet teaches obtaining, by a processor, a plurality of diagenesis samples each comprising diagenetic condition parameters and an actual diagenetic parameter evolved therefrom (Measurements of porosity, permeability, and mineralogical composition are identified from geological time t to current time indicating how the parameters may have evolved over time, P0019-P0021), wherein the diagenetic condition parameters comprise a diagenesis prediction period, and at least further comprise one or combinations of an ion concentration, a mineral content, temperature and pressure conditions, an acidity-basicity, and a porosity (Mechanical conditions for at least one rock sample are determined for each diagenetic stage, P0019-P0023. Mechanical conditions include porosity, permeability, and mineralogical composition, P0016, P0030-P0031); the actual diagenetic parameter at least comprise one or more of the ion concentration, the mineral content, the temperature and pressure conditions, the acidity-basicity and the porosity after an evolution time elapses by the diagenesis prediction period (Measurements of porosity permeability, and mineralogical composition are identified from geological time t to current time indicating how the parameters may have evolved over time, P0019-P0021)… collecting diagenetic condition parameters (Mechanical conditions for at least one rock sample are determined, P0019-P0023. Mechanical conditions include porosity, permeability, and mineralogical composition, P0016, P0030-P0031).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
AlSinan and Adelinet before them, to include Adelinet’s specific teaching of collecting measurements of porosity, permeability, and mineralogical composition of rocks over time in AlSinan’s system of Determining Coarsened Grid Models Using Machine-Learning Models and Fracture Models. One would have been motivated to make such a combination of collecting measurements of porosity, permeability, and mineralogical composition of rocks over time (see Adelinet P0019-P0023) and collecting data in regard to diagenesis or various rock properties to determine fractures in rocks using an artificial neural network (see AlSinan P0058-P0059) for improved assessment of diagenetic rock properties (see Adelinet P0018).
Regarding claims 3 and 10, AlSinan in view of Adelinet teaches the limitations of claims 1 and 9 as outlined above. Adelinet further teaches wherein the ion concentration, the mineral content, the temperature and pressure conditions, the acidity-basicity, and the porosity at least comprised in the diagenetic condition parameters are measured values obtained at one or more observation moments (Measurements of porosity permeability, and mineralogical composition are identified from geological time t to current time indicating how the parameters may have evolved over time, P0019-P0021).
Regarding claim 5, AlSinan in view of Adelinet teaches the limitations of claim 3 as outlined above. AlSinan further teaches wherein the step of constructing an initial diagenetic parameter prediction model based on the diagenesis samples and a total dimension of the diagenetic condition parameters further comprises constructing a machine learning model based on the diagenesis samples when the total dimension of the diagenetic condition parameters is less than a preset dimension threshold, and taking the machine teaming model as the initial diagenetic parameter prediction model; and constructing a deep learning network model based on the diagenesis samples when the total dimension of the diagenetic condition parameters is greater than or equal to the preset dimension threshold, and taking the deep learning network model as the initial diagenetic parameter prediction model (A deep learning network model being constructed regardless of the threshold being met results in a machine learning model being constructed in the case the threshold is not met and a deep learning network model being constructed in the case the threshold is met. A deep learning CNN model is constructed to evaluate 100m x 100m dimensional porous media, P0089-P0090).
Regarding claim 11, AlSinan in view of Adelinet teaches the limitations of claim 1 as outlined above. AlSinan further teaches determining a sum of numbers of sub-dimensions of each diagenetic condition parameter except the diagenesis prediction period as a first data (grid parameters may be determined for the model based on fracture images, P0017, P0081. Fracture image data may include diagenesis or various rock properties, P0058); and determining a sum of the first data and 1 as the total dimension (Grid model includes the sum of rock properties, static reservoir properties, and/or dynamic reservoir properties plus one for the geological region of interest, P0052, P0064).
Regarding claim 12, AlSinan in view of Adelinet teaches the limitations of claim 11 as outlined above. Adelinet further teaches wherein at least one selected from the diagenetic condition parameters, except the diagenesis prediction period, has measured values obtained at a plurality of observation moments (for time t until the current time, parameters representative of the diagenetic state of the layer are measured including porosity, permeability, and mineralogical, composition, P0019-P0021).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over AlSinan in Adelinet and further in view of Li et al (Pub. No.: US 20180347354 A1), hereafter Li.
Regarding claim 4, AlSinan in view of Adelinet teaches the limitations of claim 3 as outlined above. AlSinan in view of Adelinet does not appear to explicitly teach “carrying out a feature selection on the diagenetic condition parameters, and removing a parameter with an influence coefficient less than a preset value, among the diagenetic condition parameters”.
Li teaches carrying out a feature selection on the diagenetic condition parameters, and removing a parameter with an influence coefficient less than a preset value, among the diagenetic condition parameters (Feature selection is performed on rock samples. If the theta value is too low for frequency bands associated with a feature, weights associated with the feature are reduced. If bands are above the minimal theta value, the bands associated with the feature are selected, P0096).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
AlSinan, Adelinet, and Li before them, to include Li’s specific teaching of performing feature selection on rock samples in AlSinan’s system of Determining Coarsened Grid Models Using Machine-Learning Models and Fracture Models. One would have been motivated to make such a combination of performing feature selection on rock samples (see Li P0096) and collecting data in regard to diagenesis or various rock properties to determine fractures in rocks using an artificial neural network (see AlSinan P0058-P0059).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over AlSinan in Adelinet and further in view of Wolfe et al (Pub. No.: US 20180279563 A1), hereafter Wolfe and Hellinga et al (Pub. No.: US 20200284811 A1), hereafter Hellinga.
Regarding claim 6, AlSinan in view of Adelinet does not appear to explicitly teach “classifying the diagenesis samples into a training set and a test set in a preset ratio using a random sampling method or a stratified sampling method”.
Wolfe teaches classifying the diagenesis samples into a training set and a test set in a preset ratio using a random sampling method or a stratified sampling method (Stratified random sampling may be used to divide samples into a preset ratio of a training dataset and a testing dataset, P0204. The data is regarding light and temperature data, P0185-P0186).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
AlSinan, Adelinet, and Wolfe before them, to include Wolfe’s specific teaching of using stratified random sampling in AlSinan’s system of Determining Coarsened Grid Models Using Machine-Learning Models and Fracture Models. One would have been motivated to make such a combination of using stratified random sampling to divide samples of light and temperature data into a preset ratio of a training dataset and a testing dataset (see Wolfe P0185-P0186, P0204) and collecting data in regard to diagenesis or various rock properties to determine fractures in rocks using an artificial neural network (see AlSinan P0058-P0059).
AlSinan in view of Adelinet and Wolfe does not appear to explicitly teach
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Hellinga teaches
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(Z-score normalization is applied to data regarding porosity of rock materials. The Z-scores are determined by dividing the difference between values of a dataset and the mean with the standard deviation, P0024, P0033).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
AlSinan, Adelinet, Wolfe, and Hellinga before them, to include Hellinga’s specific teaching of applying Z-score normalization to data regarding porosity of rock materials in AlSinan’s system of Determining Coarsened Grid Models Using Machine-Learning Models and Fracture Models. One would have been motivated to make such a combination of applying Z-score normalization to data regarding porosity of rock materials (see Hellinga P0024, P0033) and collecting data in regard to diagenesis or various rock properties to determine fractures in rocks using an artificial neural network (see AlSinan P0058-P0059).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/I.M./Examiner, Art Unit 2141
/ANDREW L TANK/Primary Examiner, Art Unit 2141
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