DETAILED ACTION
Claims 1-20 are presented for examination.
This office action is in response to submission of application on 24-MAY-2023.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/24/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement (IDS) submitted on 10/16/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claims 4-6, 11-13, and 17-19 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.
Claim 4 recites the limitations “wherein the basic logs include” in line 1. There is in sufficient antecedent basis for this limitation in the claim. Claim 1, upon which claim 4 depends, disclose “basic log values”. Therefore, it is unclear which basic log is being referred to and the scope of the claim is unclear. For examination purposes examiner has interpreted “wherein the basic log” as “wherein the basic log values.”
Further, claim 4 recites the phrase “velocity radio logs”. The specification does not provide any additional definition other than the language already found in claim 4. The Examiner has been unable to find any context that a person of ordinary skill in the art would understand what the term means. The phrase could potentially be “velocity ratio logs”. If Applicant agrees the phrase should be “velocity ratio logs”, the specification should be updated accordingly. An objection has not been made to the specification because it is unclear if the Applicant intended “velocity radio logs”, requiring no change to the specification, or “velocity ratio logs”, which requires the specification to corrected. For purposes of examination, the phrase has been interpreted as “velocity ratio logs”.
Claim 5 recites the phrase “the parameters determined using the silty sand analysis”. There is insufficient antecedent basis for this limitation in the claim. Claim 1, upon which claim 5 depends, disclose basic log values and first reservoir parameters, but does not disclose parameters determined using the silty sand analysis. The “silty sand analysis” in claim 1 determines “a volume of sand, a volume of silt, a volume of clay, and a volume of shale”. For purposes of examination, “the parameters” regarding the ”silty sand analysis” be interpreted as the volumes associated with sand, silt, clay, and shale.
Claim 6 recites the limitation "wherein the trained machine learning model used to determine the sand resistivity". There is insufficient antecedent basis for this limitation in the claim. Claim 1, upon which claim 6 depends, disclose “true sand resistivity”. Therefore, it is unclear which “sand resistivity” is being referred to and the scope of the claim is unclear. For examination purpose examiner has interpreted “wherein the trained machine learning model used to determine the sand resistivity” as “wherein the trained machine learning model used to determine the true sand resistivity.”
Claims 11-13 are medium claims, containing substantially the same elements as method Claims 4-6, respectively, and are rejected on the same grounds under 35 U.S.C. 112(b) as Claims 4-6, respectively, Mutatis mutandis.
Claims 17-19 are system claims, containing substantially the same elements as method Claims 4-6, respectively, and are rejected on the same grounds under 35 U.S.C. 112(b) as Claims 4-6, respectively, Mutatis mutandis.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 (Statutory Category – Process)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claim recites a mental process, specifically:
MPEP 2106.04(a)(2)(Ill) “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.”
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
2106.04(a)(2)(I)(A) “Mathematical Relationships A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols. For example, pressure (p) can be described as the ratio between the magnitude of the normal force (F) and area of the surface on contact (A), or it can be set forth in the form of an equation such as p = F/A.”
2106.04(a)(2)(I)(B) “Mathematical Formulas or Equations A claim that recites a numerical formula or equation will be considered as falling within the "mathematical concepts" grouping. In addition, there are instances where a formula or equation is written in text format that should also be considered as falling within this grouping. For example, the phrase "determining a ratio of A to B" is merely using a textual replacement for the particular equation (ratio = A/B). Additionally, the phrase "calculating the force of the object by multiplying its mass by its acceleration" is using a textual replacement for the particular equation (F= ma).”
2106.04(a)(2)(I)(C) “Mathematical Calculations A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.”
obtaining basic log values of laminated low resistivity sands;
The “basic log values” can be a printed well log or other printed curves. The “obtaining” can be done by an observation of the well logs. The “laminated low resistivity sands” does not change how the “”values” can be observed.
determining … a volume of solids, a volume of fluids, and first reservoir parameters using a multimineral formation evaluation based on the basic log values of the laminated low resistivity sands;
The “determining” is interpreted as an evaluation. The claim recites “a multimineral formation evaluation” and is performing the evaluation on the observed “basic log values”. The “volume of solids”, “volume of fluids”, and “first reservoir parameters” are a product of an evaluation based on judgement or opinion. A person of ordinary skill in the art can observe “basic log values” and create an estimate of “volume” based on experience or judgement.
determining … a volume of sand, a volume of silt, a volume of clay, and a volume of shale using a silty sand analysis based on the determined volume of solids, the determined volume of fluids, and the first determined reservoir parameters;
The “determining” is interpreted as an evaluation. The claim recites “a silty sand analysis” and is performing the evaluation. The “volume of sand”, “volume of silt”, “volume of clay”, and “volume of shale” are determined based on the evaluation. These “volumes” can reasonably be estimated by a person of ordinary skill in the art based on judgement or opinion.
inputting … second determined parameters to a trained machine learning model to determine the true sand resistivity; and
The “machine learning model” are recited at a high level of generality and amounts to observing the outcome of the pervious evaluation. A judgment or opinion is performed based on the previous evaluations.
predicting … the true sand resistivity using the trained machine learning model based on the second determined parameters.
The “predicting” is interpreted as determining the “true sand resistivity” based on an opinion. A person of ordinary skill in the art could reasonably observe the previous evaluated volumes and form a guess or opinion to determine the “true sand resistivity”.
Therefore, the claim recites a mental process.
Step 2A – Prong 2: Integrated into a Practical Solution?
No.
MPEP 2106.05(f) Mere Instructions To Apply An Exception has found 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.
The following limitations are interpreted as a general-purpose computer:
, using a computer processor,
, using the computer processor,
, using the computer processor,
, using the computer processor,
The additional elements have been considered both individually and as an ordered combination in to determine whether they integrate the exception into a practical application.
Therefore, no meaningful limits are imposed on practicing the abstract idea.
The claim is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No, as discussed with respect to Step 2A, the additional limitation is a general-purpose computer and does not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B.
The additional elements have been considered both individually and as an ordered combination in the significantly more consideration.
The claim is ineligible.
2. “The method of claim 1, wherein a volume of hydrocarbon reserves is determined based, at least in part, on the predicted the true sand resistivity.”
The additional determined “volume of hydrocarbon” is performing an additional evaluation and judgment/opinion. (Step 2A Prong 1).
3. “The method of claim 2, wherein a wellbore is designed based on the determined volume of the hydrocarbon reserves.”
The “designed” is interpreted as an opinion or judgment. A person of ordinary skill in the art could reasonably observe the evaluated “volume of the hydrocarbon reserves” and mentally picture the wellbore should reach from the surface to the target volume. (Step 2A Prong 1).
4. “The method of claim 1, wherein the basic logs include basic gamma-ray logs, resistivity logs, density logs, neutron porosity logs, compressional sonic logs, shear sonic logs, and velocity radio logs.”
The claims recites additional logs that are observed. (Step 2A Prong 1).
5. “The method of claim 1, wherein the second determined parameters include the basic logs of the laminated low resistivity sands, the parameters determined using the multimineral formation evaluation, and the parameters determined using the silty sand analysis.”
The “basic logs” used in the evaluation are observed. The “multimineral formation evaluation” and “silty sand analysis” are both evaluations. (Step 2A Prong 1).
6. “The method of claim 1, wherein the trained machine learning model used to determine the sand resistivity is a Random Forest model based on a best root-mean-square-error.”
Performing a “Random Forest model” is an evaluation estimating the true sand resistivity. The “best root-mean-square-error” is further evaluation. The “best root-mean-square-error” is also a known mathematical concept. (Step 2A Prong 1).
7. “The method of claim 1, wherein the trained machine learning is used for vertical wells from low resistivity high anisotropy zones.”
The phrase “is used for” is interpreted as intended use. This amounts to the evaluation from the “trained machine learning” being used with “vertical wells from low resistivity high anisotropy zones”. This could amount to an opinion or judgment that may be used in interpreting a “vertical well”. (Step 2A Prong 1).
Claims 8-13 are medium claims, containing substantially the same elements as method Claims 1-6, respectively, and are rejected on the same grounds under 35 U.S.C. 101 as Claims 1-6, respectively, Mutatis mutandis. The additional components of “A non-transitory computer readable medium storing instructions executable by a computer processor” are interpreted as a general purpose computer and mere instructions to apply.
Claims 14-20 are system claims, containing substantially the same elements as method Claims 1-7, respectively, and are rejected on the same grounds under 35 U.S.C. 101 as Claims 1-7, respectively, Mutatis mutandis. The additional components of “A system comprising: a well logging system; and a true sand resistivity simulator comprising a computer processor,” are interpreted as a general purpose computer and mere instructions to apply.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over
Barry et al., “RV/RH ANISOTROPY IN UNCONVENTIONAL FORMATIONS: RESOLVING THE RIDDLE OF RESISTIVITY” [2021] (hereinafter ‘Barry’) in view of
Laalam et al., “Application of Machine Learning for Mineralogy Prediction from Well Logs in the Bakken Petroleum System” [2022] (hereinafter ‘Laalam’) further in view of
Garcia Leiceaga et al., U.S. Patent Publication 2014/0121980 A1 (hereinafter ‘Garcia Leiceaga’) further in view of
Hursan et al., “SLIMHOLE NMR T1 LOGGING WHILE DRILLING ENHANCES REALTIME PETROPHYSICS” [2018].
Regarding Claim 1: A method for predicting true sand resistivity, comprising:
Barry teaches obtaining basic log values of laminated low resistivity sands; (Pg. 10 left col 2nd paragraph “…The resulting dataset is comprised of 8 wells with triaxial induction logs, three of which have both induction and laterologs. An additional 29 wells with induction and laterologs were added to the dataset through the proximity approach. Nearest offsets were chosen within a radius of (<1000 ft) and log responses were depth shifted appropriately…” Pg. 7 right col 2nd paragraph Barry “…The three formations examined in this paper are the organic rich Upper Bakken Shale (UBS), the laminated dolomite/silty mudstone Three Forks 1 (TF1), and an isotropic dolomudstone Three Forks 4 (TF4)…”)
Barry teaches inputting, using the computer processor, second determined parameters to a trained machine learning model to determine the true sand resistivity; and (Pg. 12 right col last paragraph Barry “…Secondly, many models work better if the data is scaled prior to fitting. For example, resistivity which can have values that range from 0.1 to 10,000 may be considered by the model to be more important than permeability which may have values from 0.000001 to 1. In this study, the MinMaxScaler from SciKit-Learn library is used to scale the values from 0 to 1. (SciKit-learn, 2021) Finally, the data was split into a test and train data set with 75% of the data in the training set and 25% of the data in the test set. This was to ensure the models do not overfit the data and test model stability…”)
Barry teaches predicting, using the computer processor, the true sand resistivity using the trained machine learning model based on the second determined parameters. (Pg. 15 right col 1st paragraph Barry “…The resultant model had a very good RMSE and RSQ. The RMSE was 5.965 and the RSQ was 0.995. This RMSE is superior to the previous MLR model and the spread in the Upper Bakken Shale (UBS) especially shows this. The predicted Rh vs Actual Rh is shown in Figure 14 and the RMSE and RSQ values are shown in Table 9…” pg. 1 right col 2nd paragraph “…Wells with tri-axial resistivity modeled Rv and Rh supplemented the data set. Once the data was collected, the authors utilized simple x-y regression, multilinear regression, artificial neural net, and random forest regression to predict true Rh…”)
Barry does not appear to explicitly disclose
determining, using a computer processor, a volume of solids, a volume of fluids, and first reservoir parameters using a multimineral formation evaluation based on the basic log values of the laminated low resistivity sands;
However, Laalam teaches determining, using a computer processor, a volume of solids, a volume of fluids, and first reservoir parameters (Pg. 5 1st paragraph Laalam “…The evaluation includes the estimation of porosity, fluids saturation and minerals volume. It is done by solving simultaneous equations described by one or more interpretation models. Logs responses, and tools response parameters are used together in equations to compute volumetric formation components (minerals and fluids)…”)
Laalam teaches using a multimineral formation evaluation based on the basic log values of the laminated low resistivity sands; (Pg. 6 2nd paragraph Laalam “…A multimineral model was conducted to estimate the volume of the dominant minerals (dolomite, calcite, clay, and quartz) in the Bakken Petroleum System (BPS) using the quad combo logs…” pg. 3 last paragraph “…The Bakken shale members are black marine mudstones organic-rich (Gamma Ray 600-1000 API) and exhibit laminated to massive bedding of siltsized material with several pyrite nodules recognized in cores…”)
Barry and Laalam are analogous art because they are from the same field of endeavor, well property determination.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the obtaining basic log values of laminated low resistivity sands as disclosed by Barry by determining, using a computer processor, a volume of solids, a volume of fluids, and first reservoir parameters using a multimineral formation evaluation based on the basic log values of the laminated low resistivity sands as disclosed by Laalam.
One of ordinary skill in the art would have been motivated to make this modification in order to assess effectiveness of using machine learning in predicting mineralogy using available logs as discussed on pg. 3 2nd paragraph by Laalam “…The contest of our study in this paper is to be able to assess the effectiveness of ML algorithms as tools to predict mineralogy from conventionally available logs…”
Barry and Laalam do not appear to explicitly disclose
determining, using the computer processor, a volume of sand, a volume of silt, a volume of clay, and a volume of shale using a … analysis based on the determined volume of solids, the determined volume of fluids, and the first determined reservoir parameters;
However, Garcia Leiceaga teaches determining, using the computer processor, a volume of sand, a volume of silt, a volume of clay, and a volume of shale using … analysis based on the determined volume of solids, the determined volume of fluids, and the first determined reservoir parameters; ([0032] Garcia Leiceaga “…In one or more embodiments, the well log data (i.e., input 2) may include sonic (compressional and shear) logs, bulk density logs, and petrophysical logs ( e.g., petrophysical data logs (214)). For example, the petrophysical data logs (214) may include information regarding water saturation, porosity ( effective and/or total), TOC (total organic carbon), mineral fractions (e.g., volume of clay, volume of shale, volume of silt, volume of sand, volume of coal, volume of dolomite, volume of illite, volume of calcite, etc.)…” [0025] “…In one or more embodiments, the data received by the surface unit (200) may be sent to the E&P computer system (208) for further analysis. Generally, the E&P computer system (208) is configured to analyze, model, control, optimize, and/or perform other management tasks of the aforementioned field operations based on the data provided from the surface unit (200). In one or more embodiments, the E&P computer system (208) is provided with functionality for manipulating and analyzing the data, such as performing seismic interpretation or borehole resistivity image log interpretation to identify geological surfaces in the subterranean formation (104) or performing simulation, planning, and optimization of field operations of the wellsite-1 (102-1) through wellsite-5 (102-5), or any part of the field (100)…”)
Barry, Laalam, and Garcia Leiceaga are analogous art because they are from the same field of endeavor, well property determination.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the determining, using a computer processor, a volume of solids, a volume of fluids, and first reservoir parameters using a multimineral formation evaluation based on the basic log values of the laminated low resistivity sands as disclosed by Barry and Laalam by determining, using the computer processor, a volume of sand, a volume of silt, a volume of clay, and a volume of shale using analysis based on the determined volume of solids, the determined volume of fluids, and the first determined reservoir parameters as disclosed by Garcia Leiceaga.
One of ordinary skill in the art would have been motivated to make this modification in order to reduce risk by improving the prediction quality as discussed in paragraph [0013] of Garcia Leiceaga “…Aspects of the present disclosure include a method, system, and computer readable medium to reduce the risk of drilling low or non-producing wells by predicting reservoir quality based on correlating rock elastic properties and petrophysical properties to existing production data…”
Barry, Laalam, and Garcia Leiceaga does not appear to explicitly disclose
a silty sand analysis
However, Hursan teaches a silty sand analysis (Pg. 1 right col last paragraph Hursan “…advanced petrophysical analyses such as thin bed evaluation, silty sand analysis and advanced carbonate pore typing…”)
Barry, Laalam, Garcia Leiceaga, and Hursan are analogous art because they are from the same field of endeavor, well property determination.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the determining, using the computer processor, a volume of sand, a volume of silt, a volume of clay, and a volume of shale using analysis based on the determined volume of solids, the determined volume of fluids, and the first determined reservoir parameters as disclosed by Barry, Laalam, and Garcia Leiceaga by a silty sand analysis as disclosed by Hursan.
One of ordinary skill in the art would have been motivated to make this modification in order to provide information about well properties that are key to performing interpretation, calibration, and design as discussed on pg. 1 right col 2nd paragraph by Hursan “…In the sandstone well, the tool revealed grain size variations and provided total porosity, bound water volume, and reservoir permeability. These were key inputs for petrophysical interpretation, model calibration, and completions design…”
Regarding Claim 2: Barry, Laalam, Garcia Leiceaga, and Hursan teach The method of claim 1,
Garcia Leiceaga teaches wherein a volume of hydrocarbon reserves is determined based, at least in part, on the predicted the true sand resistivity. ([0014] Garcia Leiceaga “…In one or more embodiments, reservoir quality defines how much hydrocarbons are available in a reservoir and is referred to as reservoir production capacity. Further, reservoir quality may further account for a capacity to produce such hydrocarbons…” [0039-0042] “…where V cl is volume of clay, PHIE is effective porosity, and Sw is water saturation. In one or more embodiments, the ranges are determined empirically based on reservoir production data associated with sections of each producing well that have varying production qualities. Accordingly, these classes are referred to as petrophysical property defined classes…”)
Regarding Claim 3: Barry, Laalam, Garcia Leiceaga, and Hursan teach The method of claim 2,
Garcia Leiceaga teaches wherein a wellbore is designed based on the determined volume of the hydrocarbon reserves. ([0050] Garcia Leiceaga “…FIG. 3.3 shows a screenshot-1 (331) depicting a depth slice of a reservoir highlighted ( e.g., using cross hatch patterns) according to Poisson's ratio (PR) of the rocks and a screenshot-2 (332) depicting a cross-sectional view of the same reservoir highlighted ( e.g., using cross hatch patterns) according to the three reservoir classes predicted using the workflow described in FIG. 2 above. Using the classification naming convention described above, class 1, class 2, and class 3 correspond to high producing zone, medium producing zone, and low producing zone, respectively. The wellbore trajectory (330) shown in both screenshots represents the same well drilled on the principle that zones which exhibit low PR are easier to hydraulically fracture, and are therefore better producers…”)
Regarding Claim 4: Barry, Laalam, Garcia Leiceaga, and Hursan teach The method of claim 1,
Garcia Leiceaga teaches wherein the basic logs include basic gamma-ray logs, resistivity logs, density logs, neutron porosity logs, compressional sonic logs, shear sonic logs, and velocity radio logs. ([0030] Garcia Leiceaga “…Example elastic properties include acoustic impedance, shear impedance, fluid factor, compressional and shear velocity ratio (VpNs), density, Poisson's ratio, Young's modulus, bulk modulus, shear modulus(μ), P-wave modulus, Ap and μp where p represents density, Alμ, Lame's coefficient (A)…” [0032] Garcia Leiceaga “…In one or more embodiments, the well log data (i.e., input 2) may include sonic (compressional and shear) logs, bulk density logs, and petrophysical logs ( e.g., petrophysical data logs (214)). For example, the petrophysical data logs (214) may include information regarding water saturation, porosity ( effective and/or total), TOC (total organic carbon), mineral fractions (e.g., volume of clay, volume of shale, volume of silt, volume of sand, volume of coal, volume of dolomite, volume of illite, volume of calcite, etc.)…”)
Regarding Claim 5: Barry, Laalam, Garcia Leiceaga, and Hursan teach The method of claim 1,
Barry teaches wherein the second determined parameters include the basic logs of the laminated low resistivity sands, (Pg. 11 right col 5th paragraph Barry “…In order to resolve the discrepancy between laterolog and induction measurements, which has been thoroughly documented in previous sections, an empirical methodology was thought best. The aim of this model was to provide a pragmatic solution that could be used on traditional triple combo well logs and be validated through more robust scientific data…” Pg. 7 right col 3rd paragraph Barry “…The Upper Bakken Shale is known for have a high GR signature due to an abundance of organics and moderate amounts of clay. Acoustic evidence suggests this interval maintains a high level of acoustic anisotropy. (Firdaus, et. al, 2020) Observably high Rh is a result of organic rich content and hydrocarbon saturation. Layering within the rock is at a microscopic level. (Liu, 2018) Figure 8 depicts the abundance of organics and clay and details small laminations…”)
Laalam the parameters determined using the multimineral formation evaluation, and (Pg. 4 2nd paragraph Laalam “…Several statistical models are used to estimate the volume of minerals using well logs. These models are available in different commercial software such as Quanti Elan in Techlog and Multimin in Geolog. Before conducting any Multimineral Analysis (MMA), the analyst should know the main existing minerals in each reservoir in order to supply the appropriate analysis parameters to the mineralogy and fluid properties in addition to assigning the appropriate uncertainties of the used logging tools…”)
Hursan teaches the parameters determined using the silty sand analysis. (Pg. 1 right col last paragraph Hursan “…advanced petrophysical analyses such as thin bed evaluation, silty sand analysis and advanced carbonate pore typing…”)
Regarding Claim 6: Barry, Laalam, Garcia Leiceaga, and Hursan teach The method of claim 1,
Barry teaches wherein the trained machine learning model used to determine the sand resistivity is a Random Forest model based on a best root-mean-square-error. (Pg. 14 right col 2nd paragraph Barry “…Seeking improvement over the MLR model, we next tried a random forest regression (RF). Random Forest regression is an ensemble technique that uses a multitude of decision trees to output the mean prediction of all the trees. There are several benefits of this technique. First, the RF model does not suffer from the same co-variant issues as MLR does. Second, the RF feature importance is easier to determine as one does not have to read through values of T and P and try to make inferences about variable importance. (Ho, 1995)…” Pg. 15 left col 2nd paragraph Barry “…Many of the variables in the RF model can likely be removed without degradation of the model. An attempted to run the RF with only the 4 variables, representing more than 1% of the variance, achieved similar RMSE results…”)
Regarding Claim 7: Barry, Laalam, Garcia Leiceaga, and Hursan teach The method of claim 1,
Barry teaches wherein the trained machine learning is used for vertical wells from low resistivity high anisotropy zones. (Examiner notes the phrase “is used” is interpreted as intended use. The remaining limitation following the phrase “is used” is not given patentable weight. However, in the interest of compact prosecution, prior art is provided if the phrase were to be amended to positively recite the elements. Pg. 5 Figure 4 and description Barry “…Figure 4: Demonstrates change in the ratio of apparent resistivity Rac, referred to as Ra in the rest of this paper, and the horizontal resistivity (Rh) vs the resistivity anisotropy λ. (Chemali et al., 1987)…”)
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Claims 8-13 are medium claims, containing substantially the same elements as method Claims 1-6, respectively, and are rejected on the same grounds under 35 U.S.C. 103 as Claims 1-6, respectively, Mutatis mutandis.
Claims 14-20 are system claims, containing substantially the same elements as method Claims 1-7, respectively, and are rejected on the same grounds under 35 U.S.C. 103 as Claims 1-7, respectively, Mutatis mutandis.
Conclusion
Claims 1-20 are rejected.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN E JOHANSEN whose telephone number is (571)272-8062. The examiner can normally be reached M-F 9AM-3PM.
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/JOHN E JOHANSEN/Examiner, Art Unit 2187
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