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 .
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.
Regarding Claim 1
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 1 is a method claim thus it falls into one of the four categories of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding independent claim 1, following limitations recite a judicial exception:
“obtaining field data from an oil and gas field comprising an oil and gas well and a reservoir”
[Mental Process] – obtaining data can be simply done with human hearings and visions which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“obtaining a set of operation parameters related to the oil and gas field”
[Mental Process] – obtaining data can be simply done with human hearings and visions which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“determining, with a hybrid machine learning (ML) model comprising at least one ML model, a predicted inflow performance relationship (IPR) based on the field data and in view of the set of operation parameters
[Mathematical Calculations] – determining the IPR using the dataset requires to go through multiple steps of mathematical computations which recites to an abstract idea
“adjusting, with a well control, the set of operation parameters based on, at least, the predicted IPR
[Mental Process] – adjusting the parameters based on the IPR or a number is an action of changing the numbers or status which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 1, the claim recites additional elements of
“with a hybrid machine learning (ML) model comprising at least one ML model”
The hybrid machine learning model is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
“a well controller”
The well controller is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
[Even when viewed in combination, the additional elements do no more than automate the mental processes that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional elements [1, 2] are considered a mere instruction to apply an exception to the generic computer components and machine-learning components that simply runs mathematical calculations. (see MPEP 2106.05(f)) These limitations remain a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional elements represent a mere instruction to apply an exception, which cannot provide an inventive concept.
Regarding Claim 2
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 2 is a dependent claim of 1, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
As Claim 2 does not have any abstract idea by itself, thus uses all the limitations of Claim 1.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 2 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 3
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 3 is a dependent claim of 1, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
As Claim 3 does not have any abstract idea by itself, thus uses all the limitations of Claim 1.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 3 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 4
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 4 is a dependent claim of 1, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 4, following limitations recite a judicial exception:
“the hybrid ML model comprises a first ML model that determines at least one mathematical function describing a predicted flow rate, based on at least, the field data”
[Mathematical Calculations] – determining a mathematical function that describes the flow rate based on the data requires multiple steps of mathematical computations which recites to an abstract
“the hybrid ML model further comprises a second ML model that determines the predicted IPR based on, at least, the at least one mathematical function, the field data, and the set of operation parameters
[Mathematical Calculations] – determining the IPR based on the mathematical function and the data requires mathematical computations which recites to an abstract idea.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 4, the claim recites additional elements of
“the hybrid ML model comprises a first ML model”
The hybrid machine learning model comprising the first ML model is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
the hybrid ML model further comprises a second ML model”
The hybrid machine learning model comprising the second ML model is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
[Even when viewed in combination, the additional elements do no more than automate the mental processes that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional elements [1, 2] are considered a mere instruction to apply an exception to the generic computer components and machine-learning components that simply runs mathematical calculations. (see MPEP 2106.05(f)) These limitations remain a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional elements represent a mere instruction to apply an exception, which cannot provide an inventive concept.
Regarding Claim 5
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 5 is a dependent claim of 4, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 5, following limitations recite a judicial exception:
“the first ML model uses genetic programming techniques to evolve a plurality of mathematical functions describing the predicted flow rate from the reservoir and well, based on, at least, the field data”
[Mathematical Calculations] – using the genetic programming techniques can be considered as applying mathematical computations to the data to construct a mathematical function requires multiple steps of mathematical computations which recites to an abstract
“the hybrid ML model further comprises a second ML model that determines the predicted IPR based on, at least, the at least one mathematical function, the field data, and the set of operation parameters
[Mathematical Calculations] – determining the IPR based on the mathematical function and the data requires mathematical computations which recites to an abstract idea.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 5, the claim recites additional elements of
“the first ML model”
The first ML model is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
[Even when viewed in combination, the additional elements do no more than automate the mental processes that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional element [1] is considered a mere instruction to apply an exception to the generic computer components and machine-learning components that simply runs mathematical calculations. (see MPEP 2106.05(f)) This limitation remains a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional element represents a mere instruction to apply an exception, which cannot provide an inventive concept.
Regarding Claim 6
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 6 is a dependent claim of 4, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
As Claim 6 does not have any abstract idea by itself, thus uses all the limitations of Claim 4.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 6, the claim recites additional elements of
“the second ML model is an artificial neural network”
The second ML model and it being the ANN is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
[Even when viewed in combination, the additional elements do no more than automate the mental processes that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional element [1] is considered a mere instruction to apply an exception to the generic computer components and machine-learning components that simply runs mathematical calculations. (see MPEP 2106.05(f)) This limitation remains a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional element represents a mere instruction to apply an exception, which cannot provide an inventive concept.
Regarding Claim 7
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 7 is a dependent claim of 3, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
As Claim 7 does not have any abstract idea by itself, thus uses all the limitations of Claim 3.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 7 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 8
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 8 is a system claim thus it falls into one of the four categories of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding independent claim 8, following limitations recite a judicial exception:
“obtain field data from an oil and gas field”
[Mental Process] – obtaining data can be simply done with human hearings and visions which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“obtain a set of operation parameters related to the oil and gas field”
[Mental Process] – obtaining data can be simply done with human hearings and visions which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“determine, with a hybrid machine learning (ML) model comprising at least one ML model, a predicted inflow performance relationship (IPR) based on the field data and in view of the set of operation parameters
[Mathematical Calculations] – determining the IPR using the dataset requires to go through multiple steps of mathematical computations which recites to an abstract idea
“adjust, automatically, the set of operation parameters based on, at least, the predicted IPR
[Mental Process] – adjusting the parameters based on the IPR or a number is an action of changing the numbers or status regardless of being done automatically or not as it is a matter of human actions being taken whenever there is the predicted IPR is given and thus it involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 8, the claim recites additional elements of
“with a hybrid machine learning (ML) model comprising at least one ML model”
The hybrid machine learning model is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
“a plurality of field devices disposed throughout the oil and gas field”
The plurality of eild devices is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
“the plurality of field devices gather field data”
Gathering the field data is merely data gathering recited at a high level of generality, thus is insignificant extra-solution activity (See MPEP 2106.05(g)).
“a control system configured to adjust one or more field devices in the plurality of field devices”
The control system is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
“a computer configured to”
The computer is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
[Even when viewed in combination, the additional elements do no more than automate the mental processes that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional elements [1, 2, 4, 5] are considered a mere instruction to apply an exception to the generic computer components and machine-learning components that simply runs mathematical calculations. (see MPEP 2106.05(f))
The additional element [3] is considered an insignificant extra solution activity and at best the equivalent of a mere data gathering recited at a high level of generality and amount to receiving or transmitting data over a network, which is well-understood, routine, conventional activity (See MPEP 2106.05(d), subsection II). Look at the card above}
These limitations remain a mere instruction to apply an exception and an insignificant extra solution activity even upon reconsideration. Even when considered in combination, the additional elements represent a mere instruction to apply an exception and an insignificant extra solution activity, which cannot provide an inventive concept.
Regarding Claim 9
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 9 is a dependent claim of 8, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
As Claim 9 does not have any abstract idea by itself, thus uses all the limitations of Claim 8.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 9 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 10
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 10 is a dependent claim of 8, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
As Claim 10 does not have any abstract idea by itself, thus uses all the limitations of Claim 8.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 10 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 11
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 11 is a dependent claim of 8, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 11, following limitations recite a judicial exception:
“the hybrid ML model comprises a first ML model that determines at least one mathematical function describing a predicted flow rate, based on at least, the field data”
[Mathematical Calculations] – determining a mathematical function that describes the flow rate based on the data requires multiple steps of mathematical computations which recites to an abstract
“the hybrid ML model further comprises a second ML model that determines the predicted IPR based on, at least, the at least one mathematical function, the field data, and the set of operation parameters
[Mathematical Calculations] – determining the IPR based on the mathematical function and the data requires mathematical computations which recites to an abstract idea.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 11, the claim recites additional elements of
“the hybrid ML model comprises a first ML model”
The hybrid machine learning model comprising the first ML model is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
the hybrid ML model further comprises a second ML model”
The hybrid machine learning model comprising the second ML model is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
[Even when viewed in combination, the additional elements do no more than automate the mental processes that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional elements [1, 2] are considered a mere instruction to apply an exception to the generic computer components and machine-learning components that simply runs mathematical calculations. (see MPEP 2106.05(f)) These limitations remain a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional elements represent a mere instruction to apply an exception, which cannot provide an inventive concept.
Regarding Claim 12
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 12 is a dependent claim of 11, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 12, following limitations recite a judicial exception:
“the first ML model uses genetic programming techniques to evolve a plurality of mathematical functions describing the predicted flow rate from the reservoir and well, based on, at least, the field data”
[Mathematical Calculations] – using the genetic programming techniques can be considered as applying mathematical computations to the data to construct a mathematical function requires multiple steps of mathematical computations which recites to an abstract
“the hybrid ML model further comprises a second ML model that determines the predicted IPR based on, at least, the at least one mathematical function, the field data, and the set of operation parameters
[Mathematical Calculations] – determining the IPR based on the mathematical function and the data requires mathematical computations which recites to an abstract idea.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 12, the claim recites additional elements of
“the first ML model”
The first ML model is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
[Even when viewed in combination, the additional elements do no more than automate the mental processes that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional element [1] is considered a mere instruction to apply an exception to the generic computer components and machine-learning components that simply runs mathematical calculations. (see MPEP 2106.05(f)) This limitation remains a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional element represents a mere instruction to apply an exception, which cannot provide an inventive concept.
Regarding Claim 13
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 13 is a dependent claim of 11, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
As Claim 13 does not have any abstract idea by itself, thus uses all the limitations of Claim 11.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 13, the claim recites additional elements of
“the second ML model is an artificial neural network”
The second ML model and it being the ANN is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
[Even when viewed in combination, the additional elements do no more than automate the mental processes that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional element [1] is considered a mere instruction to apply an exception to the generic computer components and machine-learning components that simply runs mathematical calculations. (see MPEP 2106.05(f)) This limitation remains a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional element represents a mere instruction to apply an exception, which cannot provide an inventive concept.
Regarding Claim 14
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 14 is a non-transitory computer-readable medium claim thus it falls into one of the four categories of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding independent claim 14, following limitations recite a judicial exception:
“obtaining field data from an oil and gas field comprising an oil and gas well and a reservoir”
[Mental Process] – obtaining data can be simply done with human hearings and visions which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“obtaining a set of operation parameters related to the oil and gas field”
[Mental Process] – obtaining data can be simply done with human hearings and visions which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“determining, with a hybrid machine learning (ML) model comprising at least one ML model, a predicted inflow performance relationship (IPR) based on the field data and in view of the set of operation parameters
[Mathematical Calculations] – determining the IPR using the dataset requires to go through multiple steps of mathematical computations which recites to an abstract idea
“adjusting, automatically, the set of operation parameters based on, at least, the predicted IPR
[Mental Process] – adjusting the parameters based on the IPR or a number is an action of changing the numbers or status regardless of being done automatically or not as it is a matter of human actions being taken whenever there is the predicted IPR is given and thus it involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 14, the claim recites additional elements of
“with a hybrid machine learning (ML) model comprising at least one ML model”
The hybrid machine learning model is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
“the processor”
The processor is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
[Even when viewed in combination, the additional elements do no more than automate the mental processes that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional elements [1, 2] are considered a mere instruction to apply an exception to the generic computer components and machine-learning components that simply runs mathematical calculations. (see MPEP 2106.05(f)) These limitations remain a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional elements represent a mere instruction to apply an exception, which cannot provide an inventive concept.
Regarding Claim 15
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 15 is a dependent claim of 14, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
As Claim 15 does not have any abstract idea by itself, thus uses all the limitations of Claim 14.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 15 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 16
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 16 is a dependent claim of 14, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
As Claim 16 does not have any abstract idea by itself, thus uses all the limitations of Claim 14.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 16 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 17
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 17 is a dependent claim of 14, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 17, following limitations recite a judicial exception:
“the hybrid ML model comprises a first ML model that determines at least one mathematical function describing a predicted flow rate, based on at least, the field data”
[Mathematical Calculations] – determining a mathematical function that describes the flow rate based on the data requires multiple steps of mathematical computations which recites to an abstract
“the hybrid ML model further comprises a second ML model that determines the predicted IPR based on, at least, the at least one mathematical function, the field data, and the set of operation parameters
[Mathematical Calculations] – determining the IPR based on the mathematical function and the data requires mathematical computations which recites to an abstract idea.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 17, the claim recites additional elements of
“the hybrid ML model comprises a first ML model”
The hybrid machine learning model comprising the first ML model is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
the hybrid ML model further comprises a second ML model”
The hybrid machine learning model comprising the second ML model is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
[Even when viewed in combination, the additional elements do no more than automate the mental processes that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional elements [1, 2] are considered a mere instruction to apply an exception to the generic computer components and machine-learning components that simply runs mathematical calculations. (see MPEP 2106.05(f)) These limitations remain a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional elements represent a mere instruction to apply an exception, which cannot provide an inventive concept.
Regarding Claim 18
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 18 is a dependent claim of 17, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 18, following limitations recite a judicial exception:
“the first ML model uses genetic programming techniques to evolve a plurality of mathematical functions describing the predicted flow rate from the reservoir and well, based on, at least, the field data”
[Mathematical Calculations] – using the genetic programming techniques can be considered as applying mathematical computations to the data to construct a mathematical function requires multiple steps of mathematical computations which recites to an abstract
“the hybrid ML model further comprises a second ML model that determines the predicted IPR based on, at least, the at least one mathematical function, the field data, and the set of operation parameters
[Mathematical Calculations] – determining the IPR based on the mathematical function and the data requires mathematical computations which recites to an abstract idea.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 18, the claim recites additional elements of
“the first ML model”
The first ML model is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
[Even when viewed in combination, the additional elements do no more than automate the mental processes that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional element [1] is considered a mere instruction to apply an exception to the generic computer components and machine-learning components that simply runs mathematical calculations. (see MPEP 2106.05(f)) This limitation remains a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional element represents a mere instruction to apply an exception, which cannot provide an inventive concept.
Regarding Claim 19
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 19 is a dependent claim of 17, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
As Claim 19 does not have any abstract idea by itself, thus uses all the limitations of Claim 17.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 19, the claim recites additional elements of
“the second ML model is an artificial neural network”
The second ML model and it being the ANN is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
[Even when viewed in combination, the additional elements do no more than automate the mental processes that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional element [1] is considered a mere instruction to apply an exception to the generic computer components and machine-learning components that simply runs mathematical calculations. (see MPEP 2106.05(f)) This limitation remains a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional element represents a mere instruction to apply an exception, which cannot provide an inventive concept.
Regarding Claim 20
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 20 is a dependent claim of 16, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
As Claim 20 does not have any abstract idea by itself, thus uses all the limitations of Claim 16.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 20 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 2, 3, 7, 14, 15, 16, 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Adesanwo et al. (Adesanwo), Non-Patent Literature, “Interpreting Downhole Pressure and Temperature Data from ESP Wells byUse of Inversion-Based Methods in Samabri Biseni Field”, published on August 2019, 11 Pages.
As to independent Claim 1,
Adesanwo teaches a method, comprising:
obtaining field data from an oil and gas field comprising an oil and gas well and a reservoir (Adesanwo, Pg1, Abstract, Lines22-24, "The model and estimation techniques are evaluated with field data obtained from multiple wells located in a producing field" and Pg2, Paragraph3, Lines5-7, "The paper also challenges the ability to generalize the surrogate model, by exploring the impact of reservoir, completion, design and operating inputs on model accuracy when training the surrogate model", wherein the model uses the data obtained from the multiple wells while it also explores the impact of reservoir such that the data from it also can be utilized as inputs, which is equivalent to the claimed invention);
obtaining a set of operation parameters related to the oil and gas field (Adesanwo, Pg3, Paragraph4, Lines1-3, "ESP(electrical submersible pump) operational data such as pump intake pressure, pump discharge pressure, motor amperage, well head tubing pressure and ESP frequency are used to infer variables such as water cut (WC), GOR, Oil API, Water Specific Gravity (WaterSG), Gas Density and Productivity Index (PI)" and Pg2, Introduction, Paragraph2, Lines8-9, "using physics and/or data-driven models along with low cost real-time process parameters measurements");
determining, with a hybrid machine learning (ML) model comprising at least one ML model, a predicted inflow performance relationship (IPR) based on the field data and in view of the set of operation parameters (Adesanwo, Pg2, Introduction, Paragraph3, Lines1-2, "In this paper, hybrid surrogate and computational intelligence models are developed for estimating multiphase flow rates in production wells.", Adesanwo, Pg1, Abstract, Lines14-16, "The model is used as a forward engine and an inversion procedure is then added to interpret the measured data to estimate reservoir pressure, productivity index, downhole multiphase flow rates,...", Pg8, Results and Discussion, Lines4-7, "With this approach we can calculate not only ESP pump related parameters such as pump flow but also reservoir properties such as static pressure and productivity index. A good understanding of inflow performance will help ESP operator to define most efficient and optimal ESP operating envelop", Pg6, Paragrapg1, Lines1-2, "Based on well inflow performance IPR, we can correlate flow rate with a flowing perforation pressure. Figure 4 is a typical IPR curve for ESP well", wherein using the hybrid models to compute the flow rates including the static reservoir pressure, and the productivity index, which the two features are the primary components in computing the corresponding IPR using the data obtained above as mentioned to bring the efficient ESP operation, thus it is functionally equivalent to the claimed invention of computing IPR using the obtained data above); and
adjusting, with a well controller, the set of operation parameters based on, at least, the predicted IPR (Adesanwo, Pg10, Conclusion, Paragraph2, Lines2-6, "Interpretation of these data stream can be used to determine well flow rates and provide critical information on well performance such as water breakthrough, gas coning and production characteristic. Such information on well flow rates in real time will allow well control decisions to be implemented that are capable of optimizing current production and long-term recovery.", Pg1, Abstract, Lines25-27, "The satisfactory predictive accuracy of the physics-based data driven model makes the determination of multiphase flow and reservoir parameters computationally inexpensive, adaptive to operational changes, and suitable for online real-time system implementation.", Pg1, Abstract, Lines9-12, "Correct interpretation of temperature and pressure data can lead to improved accuracy of continuous downhole flow performance characteristics and reservoir properties such as static reservoir pressure and productivity index, which are key information to control and optimize ESP-based well production.", wherein predicting and coming up with all these flow data, which includes the IPR information as mentioned above, was purely to control and optimize ESP-based well production, which is functionally equivalent to the claimed invention.)
As to dependent Claim 2,
Adesanwo teaches, as mentioned above, all the limitations of Claim 1. Adesanwo teaches about obtaining data from the field which the data is delivered to the hybrid model to compute IPR for the better performance of the well system.
Adesanwo further teaches the method of claim 1:
wherein the set of operation parameters comprises well control parameters defining the operation of the well (Adesanwo, Pg3, Paragraph4, Lines1-3, "ESP operational data such as pump intake pressure, pump discharge pressure, motor amperage, well head tubing pressure and ESP frequency are used to infer variables such as water cut (WC), GOR, Oil API, Water Specific Gravity (WaterSG), Gas Density and Productivity Index (PI).")
As to dependent Claim 3,
Adesanwo teaches, as mentioned above, all the limitations of Claim 1. Adesanwo teaches about obtaining data from the field which the data is delivered to the hybrid model to compute IPR for the better performance of the well system.
Adesanwo further teaches the method of claim 1:
wherein the field data comprises well data, reservoir data, and fluid data describing fluid in the well and the reservoir (Adesanwo, Pg2, Introduction, Paragraph1, Lines8-11, "The ESP-based well system can be modeled based on well fluid properties, well temperature profile, well survey, ESP equipment performance, reservoir inflow performance (IPR), pump setting depth, tubing and casing size, tubing pressure, casing pressure and desired flow rate.", Adesanwo, Pg3, Paragraph4, Lines1-3, "ESP operational data such as pump intake pressure, pump discharge pressure, motor amperage, well head tubing pressure and ESP frequency are used to infer variables such as water cut (WC), GOR, Oil API, Water Specific Gravity (WaterSG), Gas Density and Productivity Index (PI).", Pg2, Paragraph3, Lines5-7, "The paper also challenges the ability to generalize the surrogate model, by exploring the impact of reservoir, completion, design and operating inputs on model accuracy when training the surrogate model.", wherein the well system uses all this incoming data including well fluid properties as well as the reservoir data as it is exploring the impact of the reservoir, which is equivalent to the claimed invention.)
As to dependent Claim 7,
Adesanwo teaches, as mentioned above, all the limitations of Claim 3. Adesanwo teaches about that obtained field data comprises well data, reservoir data and fluid data in the well and the reservoir.
Adesanwo further teaches the method of claim 3, wherein
the reservoir data comprises reservoir pressure and reservoir temperature (Pg1, Abstract, Lines9-12, "Correct interpretation of temperature and pressure data can lead to improved accuracy of continuous downhole flow performance characteristics and reservoir properties such as static reservoir pressure and productivity index, which are key information to control and optimize ESP-based well production")
As to independent Claim 14,
it is a non-transitory computer-readable medium claim that contains similar limitations of Claim 1 and thus rejected under the same rationale.
As to dependent Claim 15,
it is a non-transitory computer-readable medium claim that contains similar limitations of Claim 2 and thus rejected under the same rationale.
As to dependent Claim 16,
it is a non-transitory computer-readable medium claim that contains similar limitations of Claim 3 and thus rejected under the same rationale.
As to dependent Claim 20,
it is a non-transitory computer-readable medium claim that contains similar limitations of Claim 7 and thus rejected under the same rationale.
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.
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 4, 6, 17, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Adesanwo as mentioned in Claim 1 in view of Kumari et al. (Kumari), Non-Patent Literature, “Remaining useful life prediction using hybrid neural network and genetic algorithm approaches”, published on 2021, Pages: 6.
As to dependent Claim 4,
Adesanwo teaches, as mentioned above, all the limitations of Claim 1. Adesanwo teaches about obtaining data from the field which the data is delivered to the hybrid model to compute IPR for the better performance of the well system.
Adesanwo further teaches the method of claim 1:
wherein the hybrid ML model comprises a first ML model that determines at least one mathematical function describing a predicted flow rate, based on, at least, the field data (Adesanwo, Pg2, Introduction, Paragraph3, Lines1-2, "In this paper, hybrid surrogate and computational intelligence models are developed for estimating multiphase flow rates in production wells", Adesanwo, Pg3, Development of Flow estimation models, Paragraph1, Lines1-3, "The steps to build a surrogate model are: ... (3) fit the results to an approximate function" and Pg7, Equation1, "Flow rate of phase I at time k = F(static variables, dynamic variables)", wherein the hybrid model mentioned in Claim1 constructs the approximate function or the corresponding mathematical function which describes the flow rate or the corresponding predicted flow rate using the obtained data mentioned above, which is functionally equivalent to the claimed invention.)
While Adesanwo teaches about computing the IPR using the functions above and the data mentioned in Claim 1. However, Adesanwo does not teach about that the function constructed is delivered to the second ML model for further computations. In the same field of endeavor, Kumari teaches this limitation (Kumari, Pg2, Left Column, Paragraph2, Lines1-2, "The recent research indicates the increasing trend of hybridizing GA(genetic algorithm) and ANN", Kumari, Pg3, Left Column, Equation1, "As explained above, the inputs with their allotted weights, multiply each input in with the corresponding weight in eq1. So that we get
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. Where Y, is the resulting desired output. Eq.1 is taken as the fitness function for the GA after finding the optimal solution; it will import as an input in ANN", wherein the architecture of importing the function constructed by GA or the genetic algorithm to the second model ANN. Thus, once the IPR is computed using the functions and data from the first model within this second model, it is functionally equivalent to the claimed invention.)
Adesanwo and Kumari are analogous to the claimed invention as they are from the same field of endeavor of hybrid artificial intelligence modeling and data-driven computational techniques that utilize operational sensor measurements to monitor, diagnose, and predict the performance and condition of complex engineering systems and machinery. Therefore, it would have been obvious, before the effective filing date, to combine the physics-based data-driven surrogate modeling framework of Adesanwo which uses multi-point operational data to predict downhole multiphase flow rates and determine the IPR with the cascaded hybrid network architecture of Kumari, wherein an optimizing Genetic Algorithm model explicitly selects the best subset of input variables, formulates a mathematical function, and imports it a direct input into an ANN to automate structure optimization. The motivation is as recited by Kumari (Kumari, Pg1, Abstract, Lines8-15, “But these methods involve uncertainties in RUL(remaining useful life) prediction due to the inability to select the best input and suboptimal Artificial Neural Network (ANN) structures. The manual method of optimizing the ANN structure is time taking preprocessing to formulate the prediction model. To sort out these issues, this paper proposes a hybrid ANN and Genetic Algorithm approach to select the best input and optimize the ANN structure for higher accuracy”) such that in implementing Adesanwo’s data-driven monitoring model, managing massive surveillance data fusion and manually tweaking the neural network parameters creates a highly complex, slow, and non-automated trial-and-error process for engineering experts, which risks introducing suboptimal structures and prediction uncertainties. Thus, the combination of the two can evolves and imports the most optimal mathematical functions and feature subsets directly into the neural network.
As to dependent Claim 6,
The combination of Adesanwo and Kumari teaches, as mentioned above, all the limitations of Claim 4. The combination teaches about the hybrid model comprising two ML models such that the first model constructs or determines the mathematical functions describing the flow rate while the second model utilizes those functions to compute the predicted IPR.
Adesanwo, however, does not teach about the second model and it being an artificial neural network. In the same field of endeavor, Kumari teaches this limitation (Kumari, Pg3, Left Column, Equation1, "As explained above, the inputs with their allotted weights, multiply each input in with the corresponding weight in eq1. So that we get
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. Where Y, is the resulting desired output. Eq.1 is taken as the fitness function for the GA after finding the optimal solution; it will import as an input in ANN", wherein the input, the functions, is imported to the second model, the ANN or the artificial neural network.)
Adesanwo and Kumari are analogous to the claimed invention as they are from the same field of endeavor of hybrid artificial intelligence modeling and data-driven computational techniques that utilize operational sensor measurements to monitor, diagnose, and predict the performance and condition of complex engineering systems and machinery. Therefore, it would have been obvious, before the effective filing date, to combine the physics-based data-driven surrogate modeling framework of Adesanwo which uses multi-point operational data to predict downhole multiphase flow rates and determine the IPR with the cascaded hybrid network architecture of Kumari, wherein an optimizing Genetic Algorithm model explicitly selects the best subset of input variables, formulates a mathematical function, and imports it a direct input into an ANN to automate structure optimization. The motivation is as recited by Kumari (Kumari, Pg1, Abstract, Lines8-15, “But these methods involve uncertainties in RUL(remaining useful life) prediction due to the inability to select the best input and suboptimal Artificial Neural Network (ANN) structures. The manual method of optimizing the ANN structure is time taking preprocessing to formulate the prediction model. To sort out these issues, this paper proposes a hybrid ANN and Genetic Algorithm approach to select the best input and optimize the ANN structure for higher accuracy”) such that in implementing Adesanwo’s data-driven monitoring model, managing massive surveillance data fusion and manually tweaking the neural network parameters creates a highly complex, slow, and non-automated trial-and-error process for engineering experts, which risks introducing suboptimal structures and prediction uncertainties. Thus, the combination of the two can evolves and imports the most optimal mathematical functions and feature subsets directly into the neural network.
As to dependent Claim 17,
it is a non-transitory computer-readable medium claim that contains similar limitations of Claim 4 and thus rejected under the same rationale.
As to dependent Claim 19,
it is a non-transitory computer-readable medium claim that contains similar limitations of Claim 6 and thus rejected under the same rationale.
Claims 5, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Adesanwo and Kumari as mentioned in Claim 4 in further view of Kamal et al. (Kamal), Non-Patent Literature, “Solving Curve Fitting problems using Genetic Programming”, published on 2002, Pages: 6.
As to dependent Claim 5,
The combination of Adesanwo and Kumari teaches, as mentioned above, all the limitations of Claim 4. The combination teaches about the hybrid model comprising two ML models such that the first model constructs or determines the mathematical functions describing the flow rate while the second model utilizes those functions to compute the predicted IPR.
Adesanwo teaches about constructing or determining the mathematical functions using the field data as mentioned in Claim4, but does not teach that this construction being done using the genetic programming techniques. Although Kumari teaches about creating the functions using the genetic algorithms, it is not identical to the claimed genetic programming. In the same field of endeavor, Kamal teaches this limitation (Kamal, Pg316, Abstract, Lines11-16, "Curve Fitting problems used to be solved by assuming the equation shape or degree then searching for the parameter values as done in regression techniques. This paper demonstrates the Curve fitting problems can be solved using GP(genetic programming) without need to assume the equation shape" and Pg316, Abstract, Lines2-7, "The main difference between genetic programming and genetic algorithms is the representation of the solution. Genetic programming creates computer programs in LISP computer language as the solution where genetic algorithms create a string of numbers that represent the solution", wherein the claim 4 above already discloses about finding the functions and the associated flow rate. Also, the architecture of the hybrid model which the first model uses genetic algorithm to be fed into the second model, thus using the genetic programming described here into the genetic algorithm, will be functionally equivalent to the claimed invention.)
Adesanwo, Kumari and Kamal are analogous to the claimed invention as they are from the same field of endeavor of data-driven computational modeling, machine learning, and evolutionary optimization techniques used to perform curve fitting, formulate mathematical relationships, and predict operational profiles or performance characteristics of complex engineering machinery from measured observational datasets. Therefore, it would have been obvious, before the effective filing date, to combine the data-driven surrogate modeling framework of Adesanwo, the cascaded hybrid network architecture of Kumari with the genetic programming curve-fitting technique of Kamal. The motivation is as recited by Kamal (Kamal, Pg316, Abstract, Lines11-16, "Curve Fitting problems used to be solved by assuming the equation shape or degree then searching for the parameter values as done in regression techniques. This paper demonstrates the Curve fitting problems can be solved using GP(genetic programming) without need to assume the equation shape” and Pg319, Left Column, Lines2-8, “Genetic programming technique needs no assumption for an equation before starting solve the problem. GP is used to find both the equation and its parameters. We just give the program a set of mathematical functions “+, -, Sin, Exp, …” and a terminal value “X” then let GP evolves the equations and compute the fitness of each individual”) such that when implementing Adesanwo’s machine learning workflow to monitor dynamic fluid systems and map downhole flow characteristics, engineers traditionally face the limitation of having to predefine the empirical mathematical model or pre-assume a specific equation shape before running parameter optimization or training algorithms. While Kumari’s cascaded hybrid framework provides a distinct structural solution by automatically importing an optimized function from a first evolutionary model into a secondary neural network to minimize the human configuration errors, its use of standard optimization constrains still confines the initial search to rigid parameter subsets. By incorporating Kamal’s genetic programming paradigm into the first machine learning model stage of the pipeline, it is possible to break free from these structural constraints. This integration enables the unified framework to automatically discover, generate and evolve a highly flexible and diverse plurality of valid tree-structured flow-rate equations directly from raw field telemetry, completely eliminating the need for a developer to manually guess the correct equation degree or mathematical shape beforehand.
As to dependent Claim 18,
it is a non-transitory computer-readable medium claim that contains similar limitations of Claim 5 and thus rejected under the same rationale.
Claims 8, 9, 10 are rejected under 35 U.S.C. 103 as being unpatentable over Adesanwo et al. (Adesanwo), Non-Patent Literature, “Interpreting Downhole Pressure and Temperature Data from ESP Wells byUse of Inversion-Based Methods in Samabri Biseni Field”, published on August 2019, 11 Pages, in view of Kobzar et al. (Kobzar), Non-Patent Literature, “A new Approach to Creating a Digital Twin of Well for Production Monitoring in Western Siberia Fields”, published on October 2023, 19 Pages.
As to independent Claim 8,
Adesanwo teaches a system comprising:
an oil and gas field comprising an oil and gas well and a reservoir, wherein operation of the oil and gas field is defined, at least in part, by a set of operation parameters (Adesanwo, Pg2, Introduction, Paragraph1, Lines8-11, "The ESP-based well system can be modeled based on well fluid properties, well temperature profile, well survey, ESP equipment performance, reservoir inflow performance (IPR), pump setting depth, tubing and casing size, tubing pressure, casing pressure and desired flow rate" and Adesanwo, Pg3, Paragraph4, Lines1-3, "ESP operational data such as pump intake pressure, pump discharge pressure, motor amperage, well head tubing pressure and ESP frequency are used to infer variables such as water cut (WC), GOR, Oil API, Water Specific Gravity (WaterSG), Gas Density and Productivity Index (PI)", wherein the data all collected are from the well and the reservoir which is equivalent to the claimed invention);
a plurality of field devices disposed throughout the oil and gas field, the plurality of field devices gathering field data (Adesanwo, Pg3, Paragraph4, Lines1-3, "ESP operational data such as pump intake pressure, pump discharge pressure, motor amperage, well head tubing pressure and ESP frequency are used to infer variables such as water cut (WC), GOR, Oil API, Water Specific Gravity (WaterSG), Gas Density and Productivity Index (PI)" and Pg1, Abstract, Lines9-12, "Correct interpretation of temperature and pressure data can lead to improved accuracy of continuous downhole flow performance characteristics and reservoir properties such as static reservoir pressure and productivity index, which are key information to control and optimize ESP-based well production", wherein all the data collected from the well and the reservoir to control the well system inherently indicates there are devices or sensors to collect them);
a control system configured to adjust one or more field devices in the plurality of field devices (Pg1, Abstract, Lines9-12, "Correct interpretation of temperature and pressure data can lead to improved accuracy of continuous downhole flow performance characteristics and reservoir properties such as static reservoir pressure and productivity index, which are key information to control and optimize ESP-based well production" and Pg10, Conclusion, Paragraph2, Lines2-6, "Interpretation of these data stream can be used to determine well flow rates and provide critical information on well performance such as water breakthrough, gas coning and production characteristic. Such information on well flow rates in real time will allow well control decisions to be implemented that are capable of optimizing current production and long-term recovery")
Adesanwo, however, does not teach a plurality of field devices disposed throughout the oil and gas field, the plurality of field devices gathering field data. In the same field of endeavor, Kobzar teaches this limitation (Kobzar, pg17, Conclusion, Lines16-17, “A further development of this approach is the consistent refinement of the model components and the use of additional measurements with low-cost clamp-on metering devices”, wherein the clam-on metering devices are deployed in the fields to gather the corresponding field data which is equivalent to the claimed invention.)
Adesanwo and Kobzar are analogous to the claimed invention as they are from the same field of endeavor of virtual flow metering, production surveillance, and hybrid digital twin-modeling for oil production wells equipped with electrical submersible pumps by combining physics-based engineering principles with statistical machine learning algorithms. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the data-driven hybrid surrogate modeling framework of Adesanwo with the hybrid digital twin architecture and hardware implementation of Kobzar. The motivation is as recited by Kobzar (Kobzar, Pg2, Lines2-3, “The application of various reasonable combinations of physical and statistical models leads to increased economic performance of oil field development”) such that incorporating Kobzar’s hybrid digital twin approach and tactical field layout to capture complex downhole equipment degradation while physically distributing specialized hardware assets, such as low-cost clamp-on metering devices, across the production infrastructure allows the control system to gather enriched multi-point telemetry, dynamically recalibrate the hybrid machine learning models against unmodeled physical uncertainties, and automatically execute optimized operational adjustments to maximize the economic performance and production efficiency of the oil and gas field.
As to dependent Claim 9,
The combination of Adesanwo and Kobzar teaches, as mentioned above, all the limitations of Claim 8. The combination teaches about obtaining data from the field which the data is delivered to the hybrid model to compute IPR for the better performance of the well system.
Adesanwo further teaches the system of claim 9:
wherein the set of operation parameters comprises well control parameters defining the operation of the well (Adesanwo, Pg3, Paragraph4, Lines1-3, "ESP operational data such as pump intake pressure, pump discharge pressure, motor amperage, well head tubing pressure and ESP frequency are used to infer variables such as water cut (WC), GOR, Oil API, Water Specific Gravity (WaterSG), Gas Density and Productivity Index (PI).")
As to dependent Claim 10,
The combination of Adesanwo and Kobzar teaches, as mentioned above, all the limitations of Claim 8. The combination teaches about obtaining data from the field which the data is delivered to the hybrid model to compute IPR for the better performance of the well system.
Adesanwo further teaches the system of claim 8:
wherein the field data comprises well data, reservoir data, and fluid data describing fluid in the well and the reservoir (Adesanwo, Pg2, Introduction, Paragraph1, Lines8-11, "The ESP-based well system can be modeled based on well fluid properties, well temperature profile, well survey, ESP equipment performance, reservoir inflow performance (IPR), pump setting depth, tubing and casing size, tubing pressure, casing pressure and desired flow rate.", Adesanwo, Pg3, Paragraph4, Lines1-3, "ESP operational data such as pump intake pressure, pump discharge pressure, motor amperage, well head tubing pressure and ESP frequency are used to infer variables such as water cut (WC), GOR, Oil API, Water Specific Gravity (WaterSG), Gas Density and Productivity Index (PI).", Pg2, Paragraph3, Lines5-7, "The paper also challenges the ability to generalize the surrogate model, by exploring the impact of reservoir, completion, design and operating inputs on model accuracy when training the surrogate model.", wherein the well system uses all this incoming data including well fluid properties as well as the reservoir data as it is exploring the impact of the reservoir, which is equivalent to the claimed invention.)
Claims 11, 13 are rejected under 35 U.S.C. 103 as being unpatentable over Adesanwo and Kobzar as mentioned in Claim 8 in further view of Kumari et al. (Kumari), Non-Patent Literature, “Remaining useful life prediction using hybrid neural network and genetic algorithm approaches”, published on 2021, Pages: 6.
As to dependent Claim 11,
The combination of Adesanwo and Kobzar teaches, as mentioned above, all the limitations of Claim 8. The combination teaches about obtaining data from the field which the data is delivered to the hybrid model to compute IPR for the better performance of the well system.
Adesanwo further teaches the system of claim 8:
wherein the hybrid ML model comprises a first ML model that determines at least one mathematical function describing a predicted flow rate, based on, at least, the field data (Adesanwo, Pg2, Introduction, Paragraph3, Lines1-2, "In this paper, hybrid surrogate and computational intelligence models are developed for estimating multiphase flow rates in production wells", Adesanwo, Pg3, Development of Flow estimation models, Paragraph1, Lines1-3, "The steps to build a surrogate model are: ... (3) fit the results to an approximate function" and Pg7, Equation1, "Flow rate of phase I at time k = F(static variables, dynamic variables)", wherein the hybrid model mentioned in Claim1 constructs the approximate function or the corresponding mathematical function which describes the flow rate or the corresponding predicted flow rate using the obtained data mentioned above, which is functionally equivalent to the claimed invention.)
While Adesanwo teaches about computing the IPR using the functions above and the data mentioned in Claim 1. However, Adesanwo does not teach about that the function constructed is delivered to the second ML model for further computations. In the same field of endeavor, Kumari teaches this limitation (Kumari, Pg2, Left Column, Paragraph2, Lines1-2, "The recent research indicates the increasing trend of hybridizing GA(genetic algorithm) and ANN", Kumari, Pg3, Left Column, Equation1, "As explained above, the inputs with their allotted weights, multiply each input in with the corresponding weight in eq1. So that we get
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. Where Y, is the resulting desired output. Eq.1 is taken as the fitness function for the GA after finding the optimal solution; it will import as an input in ANN", wherein the architecture of importing the function constructed by GA or the genetic algorithm to the second model ANN. Thus, once the IPR is computed using the functions and data from the first model within this second model, it is functionally equivalent to the claimed invention.)
Adesanwo, Kobzar and Kumari are analogous to the claimed invention as they are from the same field of endeavor of hybrid artificial intelligence modeling and data-driven computational techniques that utilize operational sensor measurements to monitor, diagnose, and predict the performance and condition of complex engineering systems and machinery. Therefore, it would have been obvious, before the effective filing date, to combine the physics-based data-driven surrogate modeling framework of Adesanwo and the hybrid digital twin architecture and hardware implementation of Kobzar with the cascaded hybrid network architecture of Kumari, wherein an optimizing Genetic Algorithm model explicitly selects the best subset of input variables, formulates a mathematical function, and imports it a direct input into an ANN to automate structure optimization. The motivation is as recited by Kumari (Kumari, Pg1, Abstract, Lines8-15, “But these methods involve uncertainties in RUL(remaining useful life) prediction due to the inability to select the best input and suboptimal Artificial Neural Network (ANN) structures. The manual method of optimizing the ANN structure is time taking preprocessing to formulate the prediction model. To sort out these issues, this paper proposes a hybrid ANN and Genetic Algorithm approach to select the best input and optimize the ANN structure for higher accuracy”) such that in implementing Adesanwo’s data-driven monitoring model such that the data is efficiently gathered by the devices of Kobzar, managing massive surveillance data fusion and manually tweaking the neural network parameters creates a highly complex, slow, and non-automated trial-and-error process for engineering experts, which risks introducing suboptimal structures and prediction uncertainties. Thus, the combination of the two can evolves and imports the most optimal mathematical functions and feature subsets directly into the neural network.
As to dependent Claim 13,
The combination of Adesanwo, Kobzar and Kumari teaches, as mentioned above, all the limitations of Claim 11. The combination teaches about the hybrid model comprising two ML models such that the first model constructs or determines the mathematical functions describing the flow rate while the second model utilizes those functions to compute the predicted IPR.
Adesanwo, however, does not teach about the second model and it being an artificial neural network. In the same field of endeavor, Kumari teaches this limitation (Kumari, Pg3, Left Column, Equation1, "As explained above, the inputs with their allotted weights, multiply each input in with the corresponding weight in eq1. So that we get
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. Where Y, is the resulting desired output. Eq.1 is taken as the fitness function for the GA after finding the optimal solution; it will import as an input in ANN", wherein the input, the functions, is imported to the second model, the ANN or the artificial neural network.)
Adesanwo, Kobzar and Kumari are analogous to the claimed invention as they are from the same field of endeavor of hybrid artificial intelligence modeling and data-driven computational techniques that utilize operational sensor measurements to monitor, diagnose, and predict the performance and condition of complex engineering systems and machinery. Therefore, it would have been obvious, before the effective filing date, to combine the physics-based data-driven surrogate modeling framework of Adesanwo and the hybrid digital twin architecture and hardware implementation of Kobzar with the cascaded hybrid network architecture of Kumari, wherein an optimizing Genetic Algorithm model explicitly selects the best subset of input variables, formulates a mathematical function, and imports it a direct input into an ANN to automate structure optimization. The motivation is as recited by Kumari (Kumari, Pg1, Abstract, Lines8-15, “But these methods involve uncertainties in RUL(remaining useful life) prediction due to the inability to select the best input and suboptimal Artificial Neural Network (ANN) structures. The manual method of optimizing the ANN structure is time taking preprocessing to formulate the prediction model. To sort out these issues, this paper proposes a hybrid ANN and Genetic Algorithm approach to select the best input and optimize the ANN structure for higher accuracy”) such that in implementing Adesanwo’s data-driven monitoring model such that the data is efficiently gathered by the devices of Kobzar, managing massive surveillance data fusion and manually tweaking the neural network parameters creates a highly complex, slow, and non-automated trial-and-error process for engineering experts, which risks introducing suboptimal structures and prediction uncertainties. Thus, the combination of the two can evolves and imports the most optimal mathematical functions and feature subsets directly into the neural network.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Adesanwo, Kobzar and Kumari as mentioned in Claim 11 in further view of Kamal et al. (Kamal), Non-Patent Literature, “Solving Curve Fitting problems using Genetic Programming”, published on 2002, Pages: 6.
As to dependent Claim 12,
The combination of Adesanwo, Kobzar and Kumari teaches, as mentioned above, all the limitations of Claim 11. The combination teaches about the hybrid model comprising two ML models such that the first model constructs or determines the mathematical functions describing the flow rate while the second model utilizes those functions to compute the predicted IPR.
Adesanwo teaches about constructing or determining the mathematical functions using the field data as mentioned in Claim4, but does not teach that this construction being done using the genetic programming techniques. Although Kumari teaches about creating the functions using the genetic algorithms, it is not identical to the claimed genetic programming. In the same field of endeavor, Kamal teaches this limitation (Kamal, Pg316, Abstract, Lines11-16, "Curve Fitting problems used to be solved by assuming the equation shape or degree then searching for the parameter values as done in regression techniques. This paper demonstrates the Curve fitting problems can be solved using GP(genetic programming) without need to assume the equation shape" and Pg316, Abstract, Lines2-7, "The main difference between genetic programming and genetic algorithms is the representation of the solution. Genetic programming creates computer programs in LISP computer language as the solution where genetic algorithms create a string of numbers that represent the solution", wherein the claim 4 above already discloses about finding the functions and the associated flow rate. Also, the architecture of the hybrid model which the first model uses genetic algorithm to be fed into the second model, thus using the genetic programming described here into the genetic algorithm, will be functionally equivalent to the claimed invention.)
Adesanwo, Kobzar, Kumari and Kamal are analogous to the claimed invention as they are from the same field of endeavor of data-driven computational modeling, machine learning, and evolutionary optimization techniques used to perform curve fitting, formulate mathematical relationships, and predict operational profiles or performance characteristics of complex engineering machinery from measured observational datasets. Therefore, it would have been obvious, before the effective filing date, to combine the data-driven surrogate modeling framework of Adesanwo, the hybrid digital twin architecture and hardware implementation of Kobzar and the cascaded hybrid network architecture of Kumari with the genetic programming curve-fitting technique of Kamal. The motivation is as recited by Kamal (Kamal, Pg316, Abstract, Lines11-16, "Curve Fitting problems used to be solved by assuming the equation shape or degree then searching for the parameter values as done in regression techniques. This paper demonstrates the Curve fitting problems can be solved using GP(genetic programming) without need to assume the equation shape” and Pg319, Left Column, Lines2-8, “Genetic programming technique needs no assumption for an equation before starting solve the problem. GP is used to find both the equation and its parameters. We just give the program a set of mathematical functions “+, -, Sin, Exp, …” and a terminal value “X” then let GP evolves the equations and compute the fitness of each individual”) such that when implementing Adesanwo’s machine learning workflow to monitor dynamic fluid systems and map downhole flow characteristics, engineers traditionally face the limitation of having to predefine the empirical mathematical model or pre-assume a specific equation shape before running parameter optimization or training algorithms using the devices of Kobzar to collect enriched data of the field. While Kumari’s cascaded hybrid framework provides a distinct structural solution by automatically importing an optimized function from a first evolutionary model into a secondary neural network to minimize the human configuration errors, its use of standard optimization constrains still confines the initial search to rigid parameter subsets. By incorporating Kamal’s genetic programming paradigm into the first machine learning model stage of the pipeline, it is possible to break free from these structural constraints. This integration enables the unified framework to automatically discover, generate and evolve a highly flexible and diverse plurality of valid tree-structured flow-rate equations directly from raw field telemetry, completely eliminating the need for a developer to manually guess the correct equation degree or mathematical shape beforehand.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/DONG YOON JUNG/ Examiner, Art Unit 2145
/CHAU T NGUYEN/ Primary Examiner, Art Unit 2145
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