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 .
This communication is responsive to application filed on 11/18/2022.
Claims 1-3 are presented for examination.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application.
Preliminary Amendments
Applicant’s preliminary amendments relating to Specification and Drawings have been fully considered and are entered.
Claim Objections
Claim 1 is objected to because of the following informalities: claim 1 recites “Rheological model” that should be amended as “A Rheological model”. Appropriate correction is required.
Claim 2 is objected to because of the following informalities: claim 2 recites “Model” that should be amended as “The Rheological model”. Appropriate correction is required.
Claim 3 is objected to because of the following informalities: claim 3 recites “Model” that should be amended as “The Rheological model”. Appropriate correction is required.
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 1-3 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 1 recites the limitation "the database" in line 4. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the relative viscosity" in line 6. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the flow simulator" in line 8. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites “a flow simulator” in line 9. Does this limitation refer to the previous “flow simulator” recited in line 8. If so, then the limitation should be amended as “the flow simulator”.
Claim 1 recites the limitation "the emulsion viscosity model" in line 10. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the water fraction" in line 12. There is insufficient antecedent basis for this limitation in the claim.
Claim 2 recites the limitation "the rheological data" in line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim 2 recites the limitation "the dehydrated oil" in line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim 2 recites the limitation "the API" in line 3. There is insufficient antecedent basis for this limitation in the claim.
Claim 3 recites the limitation "the ensemble type" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 3, the phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
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 1-3 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (Does this claim fall within at least one statutory category?):
Claims 1-3 are directed to a method.
Therefore, claims 1-3 fall into at least one of the four statutory categories.
Step 2A, Prong 1: ((a) identify the specific limitation(s) in the claim that recites an abstract idea: and (b) determine whether the identified limitation(s) falls within at least one of the groups of abstract ideas enumerates in MPEP 2106.04(a)(2)):
Claim 1:
RHEOLOGICAL MODEL OF WATER-IN-OIL EMULSIONS OBTAINED BY ARTIFICIAL INTELLIGENCE, characterized by comprising the following steps:
(f) Accessing the database of fluids produced from Brazilian reservoirs [insignificant extra solution, e.g. mere data-gathering];
(g) Evaluating oil and emulsion rheological data based on input parameters and fluid data to build a regression model, and determining the relative viscosity of the emulsion [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion) and/or mathematical concepts] using a supervised learning framework;
(h) Coupling the flow simulator with an artificial intelligence model [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion) and/or mathematical concepts];
(i) Using a flow simulator at steady state using empirical mathematical correlations to describe the flow, as well as the emulsion viscosity model coupled to the simulator's calculation engine [“mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion) and/or mathematical concepts];
(j) Estimating the relative viscosity profile as a function of the water fraction for subsea injection of demulsifying products in producing wells [“mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion) and/or mathematical concepts].
Step 2A, Prong 2 (1. Identifying whether there are any additional elements recited in the claim beyond the judicial exception; and 2. Evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application): The claim is directed to the judicial exception.
Claim 1 recites additional elements of “database”, “an artificial intelligence model”, and “a flow simulator”. The components recited at a high level of generality (e.g. a generic computer element for performing a generic computer functions) such that it amounts to no more than mere application of the judicial exception using generic computer component(s). Accordingly, the additional element(s) of each of these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B: (Does the claim recite additional elements that amount to significantly more than the judicial exception? No): As discussed above with respect to the integration of the abstract into a practical application, the additional element of “database”. Additional element of “database” is not found to including anything more than a generic computer component. See MPEP 2106.05(a)(I)- vii. Providing historical usage information to users while they are inputting data, in order to improve the quality and organization of information added to a database, because "an improvement to the information stored by a database is not equivalent to an improvement in the database’s functionality," BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1287-88, 127 USPQ2d 1688, 1693-94 (Fed. Cir. 2018); an additional element of “artificial intelligence model” is well-known, routine or conventional (Applicant specification, [0031] The artificial intelligence model selected to predict the relative viscosity of emulsions uses an ensemble-type method known as Extra Trees);
an additional element of “flow simulator” is well-known, routine or conventional (Applicant’s specification, [0020] FERRAZ, L. A. (2015) "Comportamento reologico de emulsoes do tipo egua em 6leo de petr6leos pesados: estudo experimental e avaliagso decorrelagoes " ("Rheological behavior of water-in-oil emulsions of heavyoils: experimental study and evaluation of empirical correlations"), Dissertation (Master in Energy) - Federal University of Espirito Santo - Sao Mateus, evaluates some of the main empirical correlations aimed at predicting the viscosities of water-in-oil (W/O) emulsions present in two of the most prominent multiphase flow simulators (OLGA®, PIPESIM®)).
As per claim 2, the claim falls into “insignificant extra solution, e.g. mere data-gathering”.
As per claim 3, the claim falls into “generic computer element for performing a generic computer functions”.
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 are rejected under 35 U.S.C. 103 as being unpatentable over US Publication No. 2011/0266056 A1 issued to Pop et al in view of US Publication No. 2021/0264262 A1 issued to Colombo et al and further in view of Azodi et al, “An experimental study on factors affecting the heavy crude oil in water emulsion viscosity”, pgs. 1-8, 2013.
1. Pop et al discloses RHEOLOGICAL MODEL OF WATER-IN-OIL EMULSIONS OBTAINED BY ARTIFICIAL INTELLIGENCE, characterized by comprising the following steps:
(f) Accessing the database of fluids produced from Brazilian reservoirs (See: [0078] The example process 400 of FIG. 4 generally represents a process to dynamically plan drilling and related sampling operations to more effectively and efficiently collect and analyze formation fluid samples; par [0079] FIG. 5 is a flow diagram depicting one particular implementation of the general process 400 depicted in FIG. 4. An example process 500 depicted in FIG. 5 begins by collecting historical data relating to prior drilling and/or sampling operations (block 502). Such data may, for example, be collected from one or more databases, which may be located in or at least accessible by the logging and control computer 160 (FIG. 1). After collecting the historical or prior data at block 502, the example process 500 automatically plans drilling activities or operations and sampling activities or operations (block 504). Collectively, blocks 502 and 504 compose planning prior to drilling and, thus, correspond generally to block 402 of FIG. 4.);
(g) Evaluating oil and emulsion rheological data based on input parameters and fluid data to build a regression model (See: par [0022] The logging and control computer 160 may include a user interface that enables parameters to be input and/or outputs to be displayed that may be associated with measurements obtained by the examples described herein and/or predictions associated with sampling a formation F such as an extent of a zone invaded by the drilling fluid (e.g., drilling mud filtrate). The parameter inputs to the logging and control computer 160 may include seismic data (e.g., seismic surveys and/or seismic velocities), openhole logs including formation evaluation data, and/or rock mechanical properties (e.g., formation strength), each of which is associated with the formation F. Additionally or alternatively, the parameter inputs may include data related to drilling fluid rheology such as drilling fluid viscosity, drilling fluid density, drilling fluid yield stress, drilling fluid gel strength, drilling fluid composition and/or drilling fluid compressibility), and determining the relative viscosity of the emulsion (See: par [0025] The formation fluid 210 drawn into the LWD tool 200 via the probe 205 may be measured to determine, for example, fluid composition, viscosity, density, optical density, absorbance, fluorescence, resistivity and/or conductance, dielectric constant, temperature, etc.);
(i) Using a flow simulator at steady state using empirical mathematical correlations to describe the flow (See: [0118] Curve 1420 is a formation response curve, which depends on a mobility of the formation, a mobility ratio between the drilling fluid filtrate and the different fluids in the formation (e.g., water, oil, gas, etc.) and/or non-linear effects due, at least in part, to viscosity, fluid density and the velocity of the formation fluid being sampled. The curve 1416 may be generated by the tool response simulator 308. The mobility of the formation may be determined, for example, during a preliminary test process, during a sampling process, from openhole logs (e.g., NMR log) or from data acquired in offset wells; par [0125] The present disclosure also introduces a method of controlling a drilling operation that may involve performing a sampling process on a subterranean formation, measuring an actual response of the formation to the sampling process, calculating via a simulation engine a theoretical response of the formation to the sampling process, comparing the actual response to the theoretical response, adjusting at least one of a formation property or a drilling fluid property based on the comparison, and controlling the drilling operation based on at least one of the adjusted formation property or the adjusted drilling fluid property to improve the sampling process); as well as the emulsion viscosity model coupled to the simulator's calculation engine (See: [0125] The present disclosure also introduces a method of controlling a drilling operation that may involve performing a sampling process on a subterranean formation, measuring an actual response of the formation to the sampling process, calculating via a simulation engine a theoretical response of the formation to the sampling process, comparing the actual response to the theoretical response, adjusting at least one of a formation property or a drilling fluid property based on the comparison, and controlling the drilling operation based on at least one of the adjusted formation property or the adjusted drilling fluid property to improve the sampling process);
Pop et al does not specify but Colombo et al discloses using a supervised learning framework (See: par [0038] Turning to the reservoir simulator (160), the reservoir simulator (160) may include hardware or software with functionality for generating one or more trained models (170) regarding the formation (106). For example, the reservoir simulator (160) may store well logs (140) and data regarding core samples (150), and further analyze the well log data, the core sample data, seismic data, or other types of data to generate or update the one or more trained models (170) having a complex geological environment. For example, different types of models may be trained, such as artificial intelligence, convolutional neural networks, deep neural networks, support vector machines, decision trees, inductive learning models, deductive learning models, and supervised learning models, and are capable of approximating solutions of complex non-linear problems); (h) Coupling the flow simulator with an artificial intelligence model (See: par [0038] Turning to the reservoir simulator (160), the reservoir simulator (160) may include hardware or software with functionality for generating one or more trained models (170) regarding the formation (106). For example, the reservoir simulator (160) may store well logs (140) and data regarding core samples (150), and further analyze the well log data, the core sample data, seismic data, or other types of data to generate or update the one or more trained models (170) having a complex geological environment. For example, different types of models may be trained, such as artificial intelligence, convolutional neural networks, deep neural networks, support vector machines, decision trees, inductive learning models, deductive learning models, and supervised learning models, and are capable of approximating solutions of complex non-linear problems; par [0039] In some embodiments, the reservoir simulator (160) may include functionality for applying deep learning or artificial intelligence methodologies to precisely determine various subsurface layers).
It would have been obvious before the effective filing date to combine a deep learning framework as taught by Colombo et al to subterranean formation method of Pop et al would be to predict parameter distributions given a finite number of inputs and/or observed measurements (Colombo et al, par [0029]).
Neither the references disclose but Azodi et al disclose (j) Estimating the relative viscosity profile as a function of the water fraction for subsea injection of demulsifying products in producing wells (See: Abstract, The factors of oil concentration, emulsifier concentration and temperature have the greatest impact on the viscosity of emulsions of the two heavy oil types. With an increase in oil concentration and emulsifier concentration, the viscosity increases, while with an increase in temperature the viscosity decreases. A modified rheological equation is introduced for predicting the viscosity of oil in water emulsion based on the factors affecting viscosity; pg. 3 right side column, “3.3.1 Oil concentration” This exponential equation predicts the relative viscosity(ηr) based on the volume fraction of the dispersed phase).
It would have been obvious before the effective filing date to combine heavy crude oil in water emulsions viscosity as taught by Azodi et al to subterranean formation method of Pop et al would be to predict parameter distributions given a finite number of inputs and/or observed measurements (Colombo et al, par [0029]).
2- Pop et al discloses model, according to claim 1, characterized in that the rheological data is the API density of the oil, water content, temperature and viscosity of the dehydrated oil (See: par [0022] the parameter inputs may include data related to drilling fluid rheology such as drilling fluid viscosity, drilling fluid density, drilling fluid yield stress, drilling fluid gel strength, drilling fluid composition and/or drilling fluid compressibility; par [0118] Curve 1420 is a formation response curve, which depends on a mobility of the formation, a mobility ratio between the drilling fluid filtrate and the different fluids in the formation (e.g., water, oil, gas, etc.) and/or non-linear effects due, at least in part, to viscosity, fluid density and the velocity of the formation fluid being sampled).
3- Colombo et al discloses model, according to claim 1, characterized in that the artificial intelligence model is the ensemble type, such as Extra Tree (See: par [0038] Turning to the reservoir simulator (160), the reservoir simulator (160) may include hardware or software with functionality for generating one or more trained models (170) regarding the formation (106). For example, the reservoir simulator (160) may store well logs (140) and data regarding core samples (150), and further analyze the well log data, the core sample data, seismic data, or other types of data to generate or update the one or more trained models (170) having a complex geological environment. For example, different types of models may be trained, such as artificial intelligence, convolutional neural networks, deep neural networks, support vector machines, decision trees, inductive learning models, deductive learning models, and supervised learning models, and are capable of approximating solutions of complex non-linear problems).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KIBROM K GEBRESILASSIE whose telephone number is (571)272-8571. The examiner can normally be reached M-F 9:00 AM-5:30 PM.
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KIBROM K. GEBRESILASSIE
Primary Examiner
Art Unit 2189
/KIBROM K GEBRESILASSIE/Primary Examiner, Art Unit 2189 02/11/2026