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 .
Priority
Application 18/041,093 filed on 02/09/2023 is a 371 of PCT/IB2021/057475 filed on 08/13/2021 and claims foreign priority to EUROPEAN PATENT OFFICE (EPO) 20191806.7 08/19/2020.
Current Status
This office action is a first office action, non-final rejection based on the merits. Claims 1-5, 8, and 11-12 are pending and have been considered below. Claims 6-7, 9-10, and 13 have been cancelled.
Election/Restrictions
Applicant’s election of 1-5, 8, and 11-12 in the reply filed on 12/19/2025 is acknowledged. Because applicant did not distinctly and specifically point out the supposed errors in the restriction requirement, the election has been treated as an election without traverse (MPEP § 818.01(a)).
Claim Objections
Claim 8 is objected to because of the following informalities:
Claim 8 claims “pseudo-components” (line 10). Examiner believe this is a typographical error and should read “pseudo-components.”
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “reader module,” “correlating module,” “clustering module” in claims 1 and 12 and “machine learning module” in claims 1, 3, and 12.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-5, 8, and 11-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite an abstract idea as discussed below. This abstract idea is not integrated into a practical application for the reasons discussed below. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons discussed below.
Step 1 of the 2019 Guidance requires the examiner to determine if the claims are to one of the statutory categories of invention. Applied to the present application, the claims belong to one of the statutory classes of a process or product as a computer implemented method or a computer system/product.
Step 2A of the 2019 Guidance is divided into two Prongs. Prong 1 requires the examiner to determine if the claims recite an abstract idea, and further requires that the abstract idea belong to one of three enumerated groupings: mathematical concepts, mental processes, and certain methods of organizing human activity.
Claim 1 is copied below, with the limitations belonging to an abstract idea being underlined.
A computer-implemented method for predicting hydrocarbon fluid properties using machine-learning-based models, the method comprising:
receiving an incomplete set of pressure-volume-temperature (PVT) data for hydrocarbon fluid samples from a PVT data base, wherein the incomplete set of PVT data comprises ones of black oil properties and compositional properties;
reading the incomplete set of PVT data by a reader module;
transforming the incomplete set of PVT data into a unified data structure by the reader module, wherein the unified data structure is used for storing items of data input from different sources in a unified way;
selecting items of the PVT data from the transformed incomplete set of PVT data by the reader module using exploratory data analysis (EDA);
processing the selected items of the transformed incomplete set of PVT data by a correlating module to identify a plurality of correlations in the selected items of the transformed incomplete set of PVT data based on one or more of the fluid properties of the hydrocarbon fluid samples;
clustering, using of at least one of a plurality of clustering schemes, the selected items of the transformed incomplete set of PVT data into a plurality of clusters by a clustering module; and
performing machine learning by a machine learning module on ones of the plurality of clusters to predict missing fluid properties in the incomplete set of PVT data and thus to obtain a complete set of PVT data, wherein the predicted complete set of PVT data comprises the black oil properties and compositional properties of the incomplete set of PVT data and further comprises the predicted items of data for the ones of the black oil properties and predicted compositional properties.
Claim 12 is copied below, with the limitations belonging to an abstract idea being underlined.
A system for predicting hydrocarbon fluid properties using machine-learning-based models, the system comprising:
a pressure-volume-temperature (PVT) data base providing an incomplete set of pressure-volume-temperature (PVT) data for hydrocarbon fluid samples, wherein the incomplete set of PVT data comprises ones of black oil properties and compositional properties;
a reader module for reading in the incomplete set of PVT data, wherein the reader module is configured to transform the incomplete set of PVT data into a unified data structure, and wherein the unified data structure is used for storing items of data input from different sources in a unified way, and wherein the reader module is configured to select items of the PVT data from the incomplete set of PVT data using exploratory data analysis (EDA);
a correlating module for processing the selected items of the transformed incomplete set of PVT data to identify a plurality of correlations in the selected items of the PVT data based on one or more of the fluid properties of the hydrocarbon fluid samples;
a clustering module for clustering, using of at least one of a plurality of clustering schemes, the selected items of the transformed incomplete set of PVT data into a plurality of clusters; and
a machine learning module performing machine learning on ones of the plurality of clusters to predict missing fluid properties in the incomplete set of PVT data and thus to obtain a complete set of PVT data, wherein the predicted complete set of PVT data comprises the black oil properties and compositional properties of the incomplete set of PVT data and further comprises the predicted items of data for the ones of the black oil properties and predicted compositional properties.
Regarding the underlined limitations “transforming the incomplete set of PVT data into a unified data structure by the reader module” and “the reader module is configured to transform the incomplete set of PVT data into a unified data structure,” they are each an abstract idea as each is a set of programming routines and patterns for transforming data into a unified data structure therefore it is an algorithm or program which is a mathematical routine or set of mental steps.
Regarding the underlined limitations “selecting items of the PVT data from the transformed incomplete set of PVT data by the reader module using exploratory data analysis (EDA)” and “the reader module is configured to select items of the PVT data from the incomplete set of PVT data using exploratory data analysis (EDA),” they are each an abstract idea as each is a set of programming routines and patterns for selecting data and analyzing data using exploratory data analysis (EDA) therefore it is an algorithm or program which is a mathematical routine or a set of mental steps.
Regarding the underlined limitation “processing the selected items of the transformed incomplete set of PVT data by a correlating module to identify a plurality of correlations in the selected items of the transformed incomplete set of PVT data based on one or more of the fluid properties of the hydrocarbon fluid samples” and “a correlating module for processing the selected items of the transformed incomplete set of PVT data to identify a plurality of correlations in the selected items of the PVT data based on one or more of the fluid properties of the hydrocarbon fluid samples” they are each an abstract idea as each is a set of programming routines and patterns for processing data in order to correlate data based on fluid properties therefore it is an algorithm or program which is a mathematical routine or a set of mental steps.
Regarding the underlined limitations with respect to “clustering,” they are each an abstract idea as “clustering” is a set of programming routines and patterns for clustering data into a plurality of clusters and therefore it is an algorithm or program which is a mathematical routine or a set of mental steps.
Regarding the underlined limitations with respect to “machine learning,” they are each an abstract idea as “machine learning” is a set of programming routines and patterns for predicting, in these claims, missing properties and therefore it is an algorithm or program which is a mathematical routine or a set of mental steps.
The lack of a specific equation in the claim merely points out that the claim would monopolize all possible appropriate equations/two-group significance tests for accomplishing this purpose in all possible systems. These steps recited by the claim therefore amount to a series of mental and/or mathematical steps, making these limitations amount to an abstract idea.
In summary, the highlighted steps in the claim above therefore recite an abstract idea at Prong 1 of the 101 analysis.
The additional elements in the claim have been left in normal font. The additional concepts of “receiving,” “reading,” “providing,” and “storing,” equates to routine data gathering and extra solution data activity (See MPEP 2106.05(g)).
The claims do not integrate the abstract idea into a practical application. Various considerations are used to determine whether the additional elements are sufficient to integrate the abstract idea into a practical application. The claim does not recite a particular machine applying or being used by the abstract idea. The claim does not effect a real-world transformation or reduction of any particular article to a different state or thing. (Manipulating data from one form to another or obtaining a mathematical answer using input data does not qualify as a transformation in the sense of Prong 2.)
The claim does not contain additional elements which describe the functioning of a computer, or which describe a particular technology or technical field, being improved by the use of the abstract idea. (This is understood in the sense of the claimed invention from Diamond v Diehr, in which the claim as a whole recited a complete rubber-curing process including a rubber-molding press, a timer, a temperature sensor adjacent the mold cavity, and the steps of closing and opening the press, in which the recited use of a mathematical calculation served to improve that particular technology by providing a better estimate of the time when curing was complete. Here, the claim does not recite carrying out any comparable particular technological process.) In all of these respects, the claim fails to recite additional elements which might possibly integrate the claim into a particular practical application. Instead, based on the above considerations, the claim would tend to monopolize the abstract idea itself, rather than integrate the abstract idea into a practical application.
Step 2b of the 2019 Guidance requires the examiner to determine whether the additional elements cause the claim to amount to significantly more than the abstract idea itself. The considerations for this particular claim are essentially the same as the considerations for Prong 2 of Step 2a, and the same analysis leads to the conclusion that the claim does not amount to significantly more than the abstract idea.
Therefore, claims 1 and 12 are rejected under 35 U.S.C. 101 as directed to an abstract idea without significantly more.
Dependent claims 2-5, 8, and 11 are similarly ineligible. The dependent claims merely add limitations which further detail the abstract idea, namely further mathematical steps detailing how the data processing algorithm is implemented, i.e. additional software limitations. These do not help to integrate the claim into a practical application or make it significant more than the abstract idea (which is recited in slightly more detail, but not in enough detail to be considered to narrow the claim to a particular practical application itself). Considering all the limitations individually and in combination, the claimed additional elements do not show any inventive concept to applying algorithms such as improving the performance of a computer or any technology, and do not meaningfully limit the performance of the application.
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 1-5 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Alamashan et al., “Estimating PVT properties of Crude Oil Systems Based on a Boosted Decision Tree Regression Modelling Scheme with K-Means Clustering” downloaded from http://onepetro.org/SPEAPOG/proceedings-pdf/19APOG/19APOG/D022S006R003/3618401/spe-196453-ms.pdf/1 in view of Sun et al., U.S. Pub. No. 2021/0010351 A1, in view of Zuo et al., U.S. Pub. No. 2022/0155275 A1.
Regarding independent claim 1 Alamashan teaches:
“A computer-implemented method for predicting hydrocarbon fluid properties using machine-learning-based models” (Alamashan, Abstract.)
“the method comprising: receiving an incomplete set of pressure-volume-temperature (PVT) data for hydrocarbon fluid samples from a PVT data base, wherein the incomplete set of PVT data comprises ones of black oil properties and compositional properties” (Alamashan, § Data Acquisition page 6: Alamashan teaches “a large dataset consisting of diverse data points generated from oil samples collected from different geographical locations” (§ Data Acquisition) where the data set the includes a “solution-gas ratio” disclosing “black oil properties” and “gas specific gravity” disclosing “compositional properties.”)
“selecting items of the PVT data from the transformed incomplete set of PVT data by the reader module using exploratory data analysis (EDA)” (Alamashan teaches selecting items for processing by clustering where “the clustering in our model is done by the algorithm, based on the four input features and the predefined number of clusters, and it’s not hardcoded” (§ K-means clustering page 8) disclosing the selecting is done by using “exploratory data analysis” as a person of ordinary skill in the art would understand EDA while using algorithms is not a “hardcoded” method of selection. Additionally, a person of ordinary skill in the art would understand the clustering (selecting) would be done by a processor which acts as the “reader module” (see below).)
If the above explanation with regards to exploratory data analysis were to be challenged than the following rejection should be applied in order to ensure compact persecution.
Sun teaches: “performing exploratory data analysis and extracting preliminary insights from the input data” (¶ 0036) where the “input data” includes hydrocarbon fluid properties such as the “gas-oil ratio” (¶ 0037). Therefore the combination of Alamashan and Sun disclose the limitation “selecting items of the PVT data from the transformed incomplete set of PVT data by the reader module using exploratory data analysis (EDA).”
It would have been obvious to one of ordinary skill in the art to apply the exploratory data analysis (EDA) as disclosed by Sun to Alamashan to select data for processing as finding key variables and identifying correlations using the well know method EDA provides for the improvement of the efficiency of machine learning models in order to “improve and hasten . . . model prediction” (Sun, ¶ 0032).
Alamashan teaches:
“processing the selected items of the transformed incomplete set of PVT data by a correlating module to identify a plurality of correlations in the selected items of the transformed incomplete set of PVT data based on one or more of the fluid properties of the hydrocarbon fluid samples; clustering, using of at least one of a plurality of clustering schemes, the selected items of the transformed incomplete set of PVT data into a plurality of clusters by a clustering module” (Alamashan, § K-means clustering, page 8-9: Alamashan teaches pattern recognition for grouping data by clustering where the clustering is done based on solution gas-oil ratio (Rs), gas specific gravity (γg), oil API gravity (γAPI), and reservoir temperature (T) disclosing identifying correlations, selecting and clustering items “based on one or more of the fluid properties of the hydrocarbon fluid samples.”)
“performing machine learning by a machine learning module on ones of the plurality of clusters to predict missing fluid properties in the incomplete set of PVT data and thus to obtain a complete set of PVT data, wherein the predicted complete set of PVT data comprises the black oil properties and compositional properties of the incomplete set of PVT data and further comprises the predicted items of data for the ones of the black oil properties and predicted compositional properties” (Alamashan, § Data acquisition page 6-7, § Results, page 9-11: Alamashan teaches “the BDTR (boosted decision tree regression) predictive model with K-means clustering produced the highest accuracy in predicting Pb (bubble point pressure) and Bob (oil formation volume factor at bubble point pressure)” (§ Results, page 10) where “BDTR” (a machine learning algorithm) with “K-means clustering” is used to predict “Pb and Bob” (compositional properties). Table 2 lists the input parameters of the BDTR and the output parameters disclosing “the predicted complete set of PVT data comprises the black oil properties and compositional properties of the incomplete set of PVT data and further comprises the predicted items of data for the ones of the black oil properties and predicted compositional properties.”)
Alamashan does not teach:
“reading the incomplete set of PVT data by a reader module; transforming the incomplete set of PVT data into a unified data structure by the reader module, wherein the unified data structure is used for storing items of data input from different sources in a unified way”
Zuo teaches:
“reading the incomplete set of PVT data by a reader module; transforming the incomplete set of PVT data into a unified data structure by the reader module, wherein the unified data structure is used for storing items of data input from different sources in a unified way” (Zuo, ¶ 0042, ¶ 0115: Zuo teaches a system which includes “the processor is configured to execute the instructions to perform the method for measuring composition and property of formation fluid” (¶ 0042) where the processor discloses a “reader module” and where the sample data set may be preprocessed where “original feature vectors/matrices are changed into more suitable expression for downstream computation” and then “standardized” (¶ 0115) disclosing “transforming the incomplete set of PVT data into a unified data structure by the reader module” Moreover, after preprocessing and standardization the data (unified data structure) is stored in a database (see fig 14).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for predicting PVT properties of crude oil systems as taught by Alamashan by including preprocessing and standardizing data by a unified data structure as taught by Zuo in order to provide a system with improved efficiency and accuracy of downhole fluids (Zuo, ¶ 0002).
Regarding claim 2 Alamashan as modified teaches:
“predicting of fluid properties for the incomplete sets of PVT data for the hydrocarbon fluid samples” (Alamashan, Table 2, § Data acquisition page 6-7: Alamashan teaches “the statistical measures of the PVT datasets are shown in TABLE 2” and “the bubble point pressure (Pb) and the oil formation volume factor at bubble point pressure (Bob) are outputs” (§ Data acquisition page 6) where “bubble point pressure (Pb) and the oil formation volume factor at bubble point pressure (Bob)” are “fluid properties.”)
Regarding claim 3 Alamashan as modified teaches:
“plotting the machine learning predictions by the machine learning module” (Alamashan, fig. 1, fig. 2, § Results page 9-11: Alamashan, teaches fig. 1 and fig. 2 showing “the estimated values by the build model, BDTR with K-means clustering, are in good agreement with the experimental ones” (§ Results, page 10) disclosing “plotting the machine learning predictions by the machine learning module.”)
Regarding claim 4 Alamashan as modified teaches:
“comparing the identified plurality of correlation results with the machine learning predictions” (Alamashan, Table 5, § Results, page 9-11: Alamashan teaches “as can be seen in Table 5, solution gas-oil ratio (Rs) is determined by the predictive model as the most important input feature in estimating Pb and Bob” (§ Results page 11) where table 5 lists the importance of the different input features when estimating Pb and Bob thereby disclosing comparing the input features (identified plurality of correlation results) with Pb and Bob (machine learning predictions) (see claim 1 above)).
Regarding claim 5 Alamashan as modified teaches:
“completing fluid composition including C12+, C20+ and C36+ mole fraction and molecular weight and/or completing black oil properties, including, in the following order: solution gas oil ratio (GOR), BO@Psat, and saturation pressure (Psat)” (Alamashan, Table 2, § Data acquisition page 6-7: Alamashan teaches “the statistical measures of the PVT datasets are shown in TABLE 2” where the input parameters include “solution gas-oil ratio” and “the bubble point pressure (Pb) and the oil formation volume factor at bubble point pressure (Bob) are outputs” (§ Data acquisition page 6) where “bubble point pressure (Pb) discloses “saturation pressure (Psat)” and “oil formation volume factor at bubble point pressure (Bob)” discloses “BO@Psat” therefore Alamashan discloses “completing black oil properties, including, in the following order: solution gas oil ratio (GOR), BO@Psat, and saturation pressure (Psat).”)
Regarding claim 11 Alamashan as modified teaches:
“identifying of clusters to which PVT data belong” (Alamashan, Table 2, § Data acquisition page 6-7: Alamashan teaches the “input parameters of the BDTR predictive model” include “the cluster assignment number generated by the K-means clustering algorithm” (§ Data acquisition page 6) thereby disclosing “identifying of clusters to which PVT data belong.”)
Regarding independent claim 12 Alamashan teaches:
“A system for predicting hydrocarbon fluid properties using machine-learning-based models” (Alamashan, Abstract.)
“the system comprising: a pressure-volume-temperature (PVT) data base providing an incomplete set of pressure-volume-temperature (PVT) data for hydrocarbon fluid samples, wherein the incomplete set of PVT data comprises ones of black oil properties and compositional properties” (Alamashan, § Data Acquisition page 6: Alamashan teaches “a large dataset consisting of diverse data points generated from oil samples collected from different geographical locations” (§ Data Acquisition) where the data set the includes a “solution-gas ratio” disclosing “black oil properties” and “gas specific gravity” disclosing “compositional properties.”)
wherein the reader module is configured to select items of the PVT data from the incomplete set of PVT data using exploratory data analysis (EDA)” (Alamashan teaches selecting items for processing by clustering where “the clustering in our model is done by the algorithm, based on the four input features and the predefined number of clusters, and it’s not hardcoded” (§ K-means clustering page 8) disclosing the selecting is done by using “exploratory data analysis” as a person of ordinary skill in the art would understand EDA while using algorithms is not a “hardcoded” method of selection. Additionally, while Alamashan does not explicitly teach a “reader module” a person of ordinary skill in the art would understand the clustering (selecting) would be done by a processor which acts as the “reader module” (see below).)
If the above explanation with regards to exploratory data analysis were to be challenged than the following rejection should be applied in order to ensure compact persecution.
Sun teaches: “performing exploratory data analysis and extracting preliminary insights from the input data” (¶ 0036) where the “input data” includes hydrocarbon fluid properties such as the “gas-oil ratio” (¶ 0037). Therefore the combination of Alamashan and Sun disclose the limitation “the reader module is configured to select items of the PVT data from the incomplete set of PVT data using exploratory data analysis (EDA)”
It would have been obvious to one of ordinary skill in the art to apply the exploratory data analysis (EDA) as disclosed by Sun to Alamashan to select data for processing as finding key variables and identifying correlations using the well know method EDA provides for the improvement of the efficiency of machine learning models in order to “improve and hasten . . . model prediction” (Sun, ¶ 0032).
Alamashan teaches:
“a correlating module for processing the selected items of the transformed incomplete set of PVT data to identify a plurality of correlations in the selected items of the PVT data based on one or more of the fluid properties of the hydrocarbon fluid samples; a clustering module for clustering, using of at least one of a plurality of clustering schemes, the selected items of the transformed incomplete set of PVT data into a plurality of clusters” (Alamashan, § K-means clustering, page 8-9: Alamashan teaches pattern recognition for grouping data by clustering where the clustering is done based on solution gas-oil ratio (Rs), gas specific gravity (γg), oil API gravity (γAPI), and reservoir temperature (T) disclosing identifying correlations, selecting and clustering items “based on one or more of the fluid properties of the hydrocarbon fluid samples.”)
“a machine learning module performing machine learning on ones of the plurality of clusters to predict missing fluid properties in the incomplete set of PVT data and thus to obtain a complete set of PVT data, wherein the predicted complete set of PVT data comprises the black oil properties and compositional properties of the incomplete set of PVT data and further comprises the predicted items of data for the ones of the black oil properties and predicted compositional properties” (Alamashan, § Data acquisition page 6-7, § Results, page 9-11: Alamashan teaches “the BDTR (boosted decision tree regression) predictive model with K-means clustering produced the highest accuracy in predicting Pb (bubble point pressure) and Bob (oil formation volume factor at bubble point pressure)” (§ Results, page 10) where “BDTR” (a machine learning algorithm) with “K-means clustering” is used to predict “Pb and Bob” (compositional properties). Table 2 lists the input parameters of the BDTR and the output parameters disclosing “the predicted complete set of PVT data comprises the black oil properties and compositional properties of the incomplete set of PVT data and further comprises the predicted items of data for the ones of the black oil properties and predicted compositional properties.”)
Alamashan does not teach:
“a reader module for reading in the incomplete set of PVT data, wherein the reader module is configured to transform the incomplete set of PVT data into a unified data structure, and wherein the unified data structure is used for storing items of data input from different sources in a unified way”
Zuo teaches:
“a reader module for reading in the incomplete set of PVT data, wherein the reader module is configured to transform the incomplete set of PVT data into a unified data structure, and wherein the unified data structure is used for storing items of data input from different sources in a unified way” (Zuo, ¶ 0042, ¶ 0115: Zuo teaches a system which includes a processor where “the processor is configured to execute the instructions to perform the method for measuring composition and property of formation fluid” (¶ 0042) where the processor discloses a “reader module” and where the sample data set may be preprocessed where “original feature vectors/matrices are changed into more suitable expression for downstream computation” and then “standardized” (¶ 0115) disclosing “the reader module is configured to transform the incomplete set of PVT data into a unified data structure” Moreover, after preprocessing and standardization the data (unified data structure) is stored in a database (see fig 14).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for predicting PVT properties of crude oil systems as taught by Alamashan by including preprocessing and standardizing data by a unified data structure as taught by Zuo in order to provide a system with improved efficiency and accuracy of downhole fluids (Zuo, ¶ 0002).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Alamashan as modified by Sun and Zuo as applied to claim 1 above, and further in view of Wong et al., U.S. Pub. No. 2016/0369600 A1.
Regarding claim 8 Alamashan as modified teaches:
“the incomplete set of PVT data for hydrocarbon fluid samples comprises black oil properties and compositional properties, wherein the black oil properties comprise at least one of reservoir temperature, solution gas-oil ratio, oil API gravity, gas gravity, dead oil viscosity, saturation pressure, saturated bubble point oil formation factor at saturation pressure, fluid density at reservoir conditions, fluid compressibility at reservoir conditions, viscosity at reservoir conditions, fluid density at reservoir conditions or any other black oil property, and wherein the compositional properties comprise at least one of mole fractions of the components, in particular N2, H2S, CO2, C1,C2, C3, C4, C5, C7, C8, and pseud-components, in particular C7+,C12+, C20+ and C36+ or any other pseudo-component as well as the molecular weight of the pseudo-components” (Alamashan, Table 2, § Data acquisition page 6-7: Alamashan teaches “The statistical measures of the PVT datasets are shown in TABLE 2. The input parameters of the BDTR predictive model are the reservoir temperature, solution gas-oil ratio, gas specific gravity, oil API gravity (γAPI) and the cluster assignment number generated by the K-means clustering algorithm” (§ Data acquisition page 6) disclosing “the black oil properties comprise at least one of reservoir temperature, solution gas-oil ratio, oil API gravity.”
While Alamashan teaches the classification of crude oils based on oil API gravity (γAPI), which would include pseudo-components (See table 1), Alamashan does not teach the mole fraction and molecular weight of pseudo-components.
Wong teaches “the C7+ heavy components are defined using a probability distribution function that provides the molecular weight and mole fraction for each carbon number from C7 to C45” (¶ 0069). Therefore the combination of Alamashan and Wong disclose the limitations “the incomplete set of PVT data for hydrocarbon fluid samples comprises black oil properties and compositional properties, wherein the black oil properties comprise at least one of reservoir temperature, solution gas-oil ratio, oil API gravity, gas gravity, dead oil viscosity, saturation pressure, saturated bubble point oil formation factor at saturation pressure, fluid density at reservoir conditions, fluid compressibility at reservoir conditions, viscosity at reservoir conditions, fluid density at reservoir conditions or any other black oil property, and wherein the compositional properties comprise at least one of mole fractions of the components, in particular N2, H2S, CO2, C1,C2, C3, C4, C5, C7, C8, and pseud-components, in particular C7+,C12+, C20+ and C36+ or any other pseudo-component as well as the molecular weight of the pseudo-components.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for predicting PVT properties of crude oil systems as taught by Alamashan by including the molecular weight and mole fraction of pseudo-components as taught by Wong because this allows for an accurate simulation of the reservoir fluid behavior allowing for an accurate description of their influence on phase behavior in order to provide a system that will “determine optimal well operating points and maximize fluid production” (Wong, ¶ 0052).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Yang et al., U.S. Pub. No. 2022/0163503 A1, teaches techniques for prediction of reservoir fluid properties of a hydrocarbon reservoir fluid.
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/DENISE R KARAVIAS/Examiner, Art Unit 2857
/ARLEEN M VAZQUEZ/Supervisory Patent Examiner, Art Unit 2857
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