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 . Claims 1-20 are pending.
Note to Applicant
Examiner contacted Attorney Peacock regarding a proposed Examiner’s Amendment. Examiner did not receive any follow-up after the initial conversation, however. See attached interview summary for additional details.
Drawings
The drawings are objected to because FIGS. 1-4 appear to be informal screenshots, as opposed to high-contrast/black-and-white figures that are reproducible.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112(b)
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.
Claims 8-14 are 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In claim 8, applicant recites “A holdup prediction system,” then proceeds to claim program code instructions in the body of the claim. It’s unclear how the claim is to be interpreted, i.e., as a system or as a computer program product. Given this ambiguity, the metes and bounds are indeterminate, and the claim is rejected for indefiniteness.
The dependent claims are rejected by virtue of their dependency. Appropriate correction is required.
Claim Rejections - 35 USC § 101 (Software Per Se)
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 8-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because claims 8 and 15 only recite the software instructions pertaining to program code, which is non-statutory (i.e., Software Per Se). That a processor is recited doesn’t alleviate these concerns, given its functional recitation.
The dependent claims are rejected by virtue of their dependency. Appropriate correction is required.
Claim Rejections - 35 USC § 101 (Abstract Idea)
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture, or composition of matter? MPEP 2106.03.
Per Step 1, claims 1-7 are to a method (i.e., a process) and therefore directed to a statutory category of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
(Claims 8, 15, and dependents, while not being directed to statutory subject matter, are included in the analysis below, for the purposes of compact prosecution.)
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claims 1, 8, and 15 is (claim 1 being representative):
generating an expanded dataset of simulated pulsed neutron logging (PNL) data based, at least in part, on an original dataset of empirical PNL data;
converting, using one or more calibration coefficients, the simulated PNL data into lab-equivalent synthetic data.
The abstract idea steps italicized above are those which could be performed mentally, including with pen and paper. The steps describe, at a high level, 1) generating an expanded data set of simulated data, based on an original empirical data; 2) converting the simulated data using one or more calibration coefficients. These are steps an administrator could accomplish these tasks with pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps italicized above describe converting simulated data using one or more calibration coefficients, which constitutes a process that, under its broadest reasonable interpretation, covers mathematical concepts. If a claim limitation, under its broadest reasonable interpretation, covers mathematical concepts, including mathematical relationships, mathematical formulas or equations, mathematical calculations, then it falls within the Mathematical Concepts grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f).
Claim 1 recites the following additional elements: controlling a learning machine; training an ensemble of machine learning models based on the lab-equivalent synthetic data.
Claim 8 recites the following additional elements: a learning machine; program code; one or more processors; instructions to train an ensemble of machine learning models based on the lab-equivalent synthetic data.
Claim 15: one or more non-transitory machine-readable media; a learning machine; program code; one or more processors; instructions to train an ensemble of machine learning models based on the lab-equivalent synthetic data.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen in [0012] of applicant’s specification as filed, for example.
Examiner interprets the machine learning ensemble training features as additional elements. MPEP 2106.05(f) is explicit that simply using other machinery as a tool also amounts to no more than merely applying the abstract idea to a computer, especially when claimed in a solution-oriented manner:
(1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
[…]
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
In this case, the machine learning ensemble training features are merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. Because the additional elements are merely instructions to apply the abstract idea to a generic computing system, they do not integrate the abstract idea into a practical application, when viewed in combination. See MPEP 2106.05(f).
Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitate the tasks of the abstract idea, as described in MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
The analysis takes into consideration all dependent claims as well:
Claims 3-7, 10-14, and 17-20 further narrow the abstract idea by adding additional mental and/or mathematical steps. This narrowing of the abstract idea does not result in integration into practical application and/or being significantly more.
Claims 2, 9, and 16, in addition to narrowing the abstract idea with mental and/or mathematical steps, also recite further additional elements: using a selected machine learning model. Similar to above, these additional elements are recited at a high level of generality and in a results-oriented manner and do no more than facilitate the tasks of the abstract idea. Whether viewed alone or in combination, this does not integrate the abstract idea into practical application and/or add significantly more. See MPEP 2106.05(f).
Accordingly, claims 1-20 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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, 8, 10, 15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Al Madani (US 20210304060) in view of Chen (US 20050246297).
Claims 1, 8, and 15
Al Madani discloses:
[A method for controlling a learning machine to predict fluid holdup in a borehole {method described in [0002]; controlling a neural network or learning machine to predict fluid holdup in a borehole described in [0015], [0022]}, the method comprising:]
[A holdup prediction system including a learning machine and comprising program code configured to predict fluid holdup in a borehole drilled into a subsurface formation {See previous citations to [0002], [0015], and [0022]. program code described in [0077]}, the program code executable on one or more processors {one or more processors described in [0004]}, the program code comprising:]
[One or more non-transitory machine-readable media including a learning machine and comprising program code configured to predict fluid holdup in a borehole drilled into a subsurface formation, the program code executable on one or more processors {See previous citations to [0002], [0004], [0015], [0022], and [0077]}, the program code comprising:]
generating an expanded dataset of simulated data based, at least in part, on an original dataset of empirical data {generating an expanded dataset of simulated data based, at least in part, on an original dataset of empirical data described in [0036]: In Block 300, acquired well data are obtained in accordance with one or more embodiments. For example, the acquired well data may correspond to well logs obtained for an interval of interest using a logging system (112) and/or logging tools (113) described above in FIG. 1 and the accompanying description. The interval of interest may be a particular depth interval within a formation, for example. generating an expanded dataset of simulated data based on an original dataset of empirical data described in [0037]: In Block 310, augmented well data is generated using one or more geological factors and acquired well data in accordance with one or more embodiments. For example, data augmentation may include performing various processes on acquired data, such as log cropping or adding noise, in order to generate augmented data. In particular, data augmentation for well data may introduce various machine-learning algorithms to uncommon problems, such as problems specific to random geological and mechanical processes. For example, a data augmentation process may alter a normal well log to produce an extremely complex log that mimics circumstances faced by real-time drilling operations. Through such augmented well data, an artificial intelligence model may be made immune to various abnormalities that might occur while drilling through an unknown formation.};
converting, using one or more calibration coefficients, the simulated data into lab-equivalent synthetic data {converting, using one or more calibration coefficients, the simulated data into lab-equivalent synthetic data described in [0042]: In some embodiments, well data is augmented to account for the lifetime and calibration of a logging tool being used to acquire well data measurements. For example, depending on the length of time and/or physical conditions of a logging tool in a well, the logging tool may need to be recalibrated in order to provide accurate sensor measurements. Without calibration, the well data may be offset from the actual well properties. Thus, data augmentation may generate augmented well data similar to well data produced by a logging tool in need of calibration.}.
Al Madani, while disclosing lab-equivalent synthetic data, doesn’t explicitly disclose, however, Chen, in a similar field of endeavor directed to processing well logging data, teaches:
pulsed neutron logging (PNL); PNL {pulsed neutron logging (PNL) described in [0011]: Systems using a single neural network trained in this way are capable of providing good synthetic or artificial triple combo open hole logs from real data taken by pulsed neutron logging tools, at least for wells near, or in the same geological area as, the well or wells used for training.}
training an ensemble of machine learning models based on the data {training an ensemble of machine learning models based on the data described in [0027]: At block 16, the data sets 12 and 14 are used to train a set of neural networks. That is, a plurality of neural networks are generated by inputting the data 14 and adjusting network coefficients until the network outputs are close approximations to the actual open hole data 12 from the same well. At 18, a genetic algorithm is used to select a subset of neural networks to form a neural network ensemble 20.}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Al Madani to include the features of Chen. Given that Al Madani is directed to determining subsurface formations based on collected and generated data, one of ordinary skill in the art would have been motivated to look to Chen, in order to reduce superfluous data collection, thereby saving time and money {[0006] of Chen}.
Claims 3, 10, and 17
Chen further teaches: selecting a machine learning model of least error from the ensemble {selecting a machine learning model from the ensemble described in [0027]: At 18, a genetic algorithm is used to select a subset of neural networks to form a neural network ensemble 20. of least error described in [0069]: n the above described methods, the weighting functions, k.sub.1, k.sub.2, and k.sub.3 may be selected based on various factors. FIGS. 9 and 10 illustrate a process by which the weighting functions may be estimated using a separate genetic algorithm driven inverse process if additional data from a test well is available. This process may be used to determine what weighting factors applied to the primary validation data from the training well or wells would lead to the finding of a set of ensembles that could minimize the prediction error on other application wells similar to the test well.}; and validating the selected machine learning model against the original dataset of empirical PNL data {error minimization and validating described in [0069]: In the above described methods, the weighting functions, k.sub.1, k.sub.2, and k.sub.3 may be selected based on various factors. FIGS. 9 and 10 illustrate a process by which the weighting functions may be estimated using a separate genetic algorithm driven inverse process if additional data from a test well is available. This process may be used to determine what weighting factors applied to the primary validation data from the training well or wells would lead to the finding of a set of ensembles that could minimize the prediction error on other application wells similar to the test well.}.
The motivation and rationale to include the additional features of Chen is the same as above.
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Al Madani and Chen, further in view of Inanc (US 20160320523).
Claims 2, 9, and 16
Chen further teaches: using a selected machine learning model {using a selected machine learning model described in [0027]: At 18, a genetic algorithm is used to select a subset of neural networks to form a neural network ensemble 20.}.
The motivation and rationale to include the additional features of Chen is the same as above.
The combination of Al Madani and Chen, while teaching the features above, doesn’t explicitly teach, however, Inanc, in a similar field of endeavor directed to density measurements using detectors on a pulsed neutron measurement platform, teaches: predicting, based on data collected from the borehole, a value of fluid holdup in the borehole {predicting, based on data collected from the borehole, a value of fluid holdup described in [0011]: In one embodiment, the fluid density measurements are used to estimate the holdup of one or more phases of the fluid based on the density measurements (i.e., the holdup density). The combination tool is configured to be disposed in a downhole environment, for example, in a wireline or logging-while-drilling (LWD) well logging application.}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of Al Madani and Chen to include the features of Inanc. Given that Al Madani is directed to a determining subsurface formations based on collected and generated data, one of ordinary skill in the art would have been motivated to look to Inanc, in order to facilitate estimating borehole fluid data including the holdup of phases of the fluid {[0012] of Inanc}.
Claims 4-5, 11-12, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Al Madani and Chen, further in view of Jacobson (US 20050067160).
Claims 4, 11, and 18
The combination of Al Madani and Chen, while teaching the features above, doesn’t explicitly teach, however, Jacobson, in a similar field of endeavor directed to pulsed-neutron formation density, teaches: calibrating, based on the empirical PNL data, one or more ratios and channels within the simulated PNL data, wherein the one or more channels comprise portions of a PNL spectrum {calibrating, based on the empirical PNL data, one or more ratios and channels, wherein the one or more channels comprise portions of a PNL spectrum described in [0052]: Thus, a Monte Carlo, or similar analysis, may be run to determine the tool response for various assumed porosities. Once the ratio of the near and far inelastic gamma ray count rates, and the ratio of the near and far thermal capture gamma ray count rates are determined for the modeled parameters, the coefficient needed to create the compensated ratio of inelastic count rate may be determined (along with the coefficients of the characteristic equation). The logging tool may then be used in an actual formation to obtain a ratio of actual near and far inelastic count rates, and also to determine a ratio of actual near and far thermal capture gamma rays. Using the actual ratios and the coefficient Z determined in the modeling process, a compensated ratio may be created, which may be applied to equation 6 (along with the coefficients of equation 6 determined from the model) to calculate a density. Determining density in this manner, the neutron transport effects may be substantially reduced, thus increasing the accuracy of the density determination using a pulse-neutron logging tool.}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of Al Madani and Chen to include the features of Jacobson. Given that Al Madani is directed to determining subsurface formations based on collected and generated data, one of ordinary skill in the art would have been motivated to look to Jacobson, in order to compensate for the effects on data from pulsed-neutron density logging tools {[0004] of Jacobson}.
Claims 5 and 12
Chen further teaches: wherein converting, using the one or more calibration coefficients, the simulated PNL data into the lab-equivalent synthetic data comprises: plotting, for each channel of the one or more channels, the simulated PNL data against the empirical PNL data {plotting the simulated data against empirical described in [0057]: FIG. 8 provides a comparison of actual triple combo logs of formation density 64, neutron porosity 65 and deep resistivity 66 to synthetic predictions 68, 69, 70 of the same log parameters. The synthetic versions were generated by inputting seven parameters from a cased hole pulsed neutron logging tool into a neural network ensemble created by the methods described above. The close correlation of the synthetic open hole logs to actual open hole logs indicates that ensembles produced by use of the present invention can accurately produce predictions of the open hole logging parameters.}; generating a calibration curve to fit the simulated PNL data and the empirical PNL data {As seen in Fig. 8 and [0057].}; selecting a set of calibration coefficients based on a function of the calibration curve {As seen in Fig. 8 and [0057].}; and converting the simulated PNL data into the lab-equivalent synthetic data based on the set of calibration coefficients {As seen in Fig. 8 and [0057].}.
The motivation and rationale to include the additional features of Chen is the same as above.
Claims 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Al Madani and Chen, further in view of Ma (US 20180058188).
Claims 6 and 13
The combination of Al Madani and Chen, while teaching the features above, doesn’t explicitly teach, however, Ma, in a similar field of endeavor directed to determining salinity of water in a borehole of a formation, teaches: wherein converting, using the one or more calibration coefficients, the simulated PNL data into the lab-equivalent synthetic data further comprises: interpolating and extrapolating unknown data values to fill a variable space of the lab-equivalent synthetic data based, at least in part, on the one or more calibration coefficients {interpolating and extrapolating unknown data values to fill a variable space described in [0025]: The various formation and borehole conditions used in a laboratory or in a computer simulation may be selected to bracket the conditions that may be found in various field situations of formation and borehole conditions. By bracketing expected field conditions, the correlations may be used to calculate, by interpolation or extrapolation for example, results of field responses of far and near detectors. If a field situation of formation and borehole conditions lies outside a bracket, an extrapolation may be performed to determine field responses of far and near detectors.}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of Al Madani and Chen to include the features of Ma. Given that Al Madani is directed to determining subsurface formations based on collected and generated data, one of ordinary skill in the art would have been motivated to look to Ma, in order to facilitate determining water salinity, which is necessary to compute an accurate estimate of a water fraction in formations {[0003] of Ma}.
Claims 7, 14, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Al Madani, Chen, and Jacobson, further in view of Ma.
Claims 7, 14, and 20
The combination of Al Madani, Chen, and Jacobson, while teaching the features above, doesn’t explicitly teach, however, Ma, in a similar field of endeavor directed to determining salinity of water in a borehole of a formation, teaches: generating a set of features using a physics-based selection process, wherein the set of features maximizes a correlation between carbon, oxygen, and density measurements to a value of fluid holdup, wherein calibrating the one or more ratios and channels comprises mapping the set of features to the empirical PNL data {generating a set of features using a physics-based selection process, wherein the set of features maximizes a correlation between carbon, oxygen, and density measurements to a value of fluid holdup described in [0048]: From the inelastic spectra 204, a carbon to oxygen ratio 212 may be obtained by using a standards database of inelastic spectra for various elements, as illustrated in FIG. 3. The standards database may be used to determine coefficients for carbon and oxygen where the coefficients represent relative amounts of carbon and oxygen. The relative amounts may be determined at the near and far detectors. Carbon may be representative of the amount of oil, and oxygen may be representative of the amount of water. From the carbon to oxygen ratio 212, an oil saturation value and a water saturation value 218 may be obtained for the formation. From the capture spectra 206, a chlorine to hydrogen ratio 214 may be obtained by using a standards database of capture spectra for various elements, as illustrated in FIG. 4. The capture spectra standards database may be used to determine coefficients for chlorine and hydrogen where the coefficients represent relative amounts of the elements. The relative amounts may be determined at the near and far detectors. Chlorine may be representative of the amount of salt, and hydrogen may be representative of the amount of water and oil. Using the chlorine to hydrogen ratio 214 and a water saturation value 218, a formation water salinity 222 may be calculated. The water saturation value 218 is necessary to provide a correction to the hydrogen value for the calculation. The various data processing 220 methods may use a database for borehole and formation conditions for the PNL tool, where the PNL tool was previously characterized under various borehole and formation conditions.}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of Al Madani, Chen, and Jacobson to include the features of Ma. Given that Al Madani is directed to determining subsurface formations based on collected and generated data, one of ordinary skill in the art would have been motivated to look to Ma, in order to facilitate determining water salinity, which is necessary to compute an accurate estimate of a water fraction in formations {[0003] of Ma}.
Claim 19
Chen further teaches: wherein the instructions to convert, using the one or more calibration coefficients, the simulated PNL data into the lab-equivalent synthetic data comprise: instructions to plot, for each channel of the one or more channels, the simulated PNL data against the empirical PNL data {plotting the simulated data against empirical described in [0057]: FIG. 8 provides a comparison of actual triple combo logs of formation density 64, neutron porosity 65 and deep resistivity 66 to synthetic predictions 68, 69, 70 of the same log parameters. The synthetic versions were generated by inputting seven parameters from a cased hole pulsed neutron logging tool into a neural network ensemble created by the methods described above. The close correlation of the synthetic open hole logs to actual open hole logs indicates that ensembles produced by use of the present invention can accurately produce predictions of the open hole logging parameters.}; instructions to generate a calibration curve to fit the simulated PNL data and the empirical PNL data {As seen in Fig. 8 and [0057].}; instructions to select a set of calibration coefficients based on a function of the calibration curve {As seen in Fig. 8 and [0057].}; instructions to convert the simulated PNL data into the lab-equivalent synthetic data based on the set of calibration coefficients {As seen in Fig. 8 and [0057].}.
The motivation and rationale to include the additional features of Chen is the same as above.
The combination of Al Madani, Chen, and Jacobson, while teaching the features above, doesn’t explicitly teach, however, Ma, in a similar field of endeavor directed to determining salinity of water in a borehole of a formation, teaches: instructions to interpolate and extrapolate unknown data values to fill a variable space of the lab-equivalent synthetic data based, at least in part, on the set of calibration coefficients {interpolating and extrapolating unknown data values to fill a variable space described in [0025]: The various formation and borehole conditions used in a laboratory or in a computer simulation may be selected to bracket the conditions that may be found in various field situations of formation and borehole conditions. By bracketing expected field conditions, the correlations may be used to calculate, by interpolation or extrapolation for example, results of field responses of far and near detectors. If a field situation of formation and borehole conditions lies outside a bracket, an extrapolation may be performed to determine field responses of far and near detectors.}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of Al Madani, Chen, and Jacobson to include the features of Ma. Given that Al Madani is directed to determining subsurface formations based on collected and generated data, one of ordinary skill in the art would have been motivated to look to Ma, in order to facilitate determining water salinity, which is necessary to compute an accurate estimate of a water fraction in formations {[0003] of Ma}.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
“Improving the pulsed neutron-gamma density method with machine learning regression algorithms” (NPL attached), which teaches: To improve density accuracy: first, we forgo exploring an explicit theoretical formula for density calculation and instead treat the NGD mathematically as a regression problem and introduce the machine learning regressor, a powerful and popular tool for solving regression problems, into the NGD for the first time; second, we select features less affected by changes in formation chemical composition as input features.
US 20100327154, which teaches: A method for logging a subsurface formation includes acquiring neutron capture data using a pulsed neutron tool at a plurality of locations along a borehole penetrating the subsurface formation, wherein the plurality of locations include a formation zone that contains water; comparing an apparent water salinity or an apparent water sigma value estimated from the neutron capture data acquired in the formation zone that contains water with a water salinity or water sigma value of a water sample from the subsurface formation to produce a calibration parameter for the neutron capture data; and correcting the neutron capture data, based on the calibration parameter, to produce corrected neutron capture data. The method may further include determining a water saturation from the corrected neutron capture data.
US 20200401951, which teaches: Permeability values are estimated based on well logs using regression algorithms, such as gradient boosting and random forest. The training data is selected from well logs for which core-analysis-based permeability values are available. The estimated permeability values are used to plan hydrocarbon production. The well logs used to build the depth blended model may include total porosity, gamma ray, volume of calcite, density, resistivity, and neutron logs.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN SAMUEL WASAFF whose telephone number is (571)270-5091. The examiner can normally be reached Monday through Friday 8:00 am to 6:00 pm.
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, SARAH MONFELDT can be reached at (571) 270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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JOHN SAMUEL WASAFF
Primary Examiner
Art Unit 3629
/JOHN S. WASAFF/Primary Examiner, Art Unit 3629