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 09/30/2022.
Claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/21/2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 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.
Step 1 (Does this claim fall within at least one statutory category?):
Claims 1-8 are directed to a method.
Claims 9-15 are directed to a method.
Claims 16-20 are directed to a system.
Therefore, claims 1-20 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:
A method implemented on a computer system for executing a plurality of elements of a model of a physical system, the method comprising:
estimating a flux between a portion of the plurality of elements [“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];
communicating state data between the portion of the plurality of elements in response to uncertainty in the model of the physical system [a generic computer element for performing a generic computer function]; and
calculating the flux from state data [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 element of “communicating”. The broadest reasonable interpretation of “communicating” is simply an internet communication between processing units. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application.
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 is simply an internet component that is well-known, routine and conventional that did not amount to significantly more. See: MPEP 2106.05(d)(II), for example- i. receiving or transmitting data over network, e.g., using the internet to gather data.
As per claim 2, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)].
As per claims 3-8, the claims fall into [mathematical concepts].
As per claim 9:
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)):
A method comprising:
executing, on a computer system, a model of a physical system including a plurality of elements each having one or more state variables, the plurality of elements divided into a plurality of partitions [a generic computer element for performing a generic computer function]; and
for each element of the plurality of elements that is on an edge of a first partition of the plurality of partitions that is adjacent a second partition of the plurality of partitions [“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]:
for each first time step of a plurality of first time steps of a plurality of time steps, communicating first state data to each element from the second partition and updating a state of each element according to the state of each element and a first flux value calculated from the state data [a generic computer element for performing a generic computer functions]; and
for each second time step of a plurality of second time steps of the plurality of time steps, estimating an uncertainty in the model of the physical system, determining that the uncertainty in the model of the physical system does not meet a threshold condition[“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], and in response to determining that the uncertainty in the model of the physical system does not meet the threshold condition, estimating a second flux value for each element based on the state of each element and a preceding flux value from a preceding time step of the plurality of time steps and updating the state of each element according to the state of each element and the second flux value [“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].
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 9 recites additional elements of “communicating” and “executing”. The broadest reasonable interpretation of “communicating” is simply an internet communication between processing units. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application. Further the additional element of “executing” may use a computer system merely as a tool to execute 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 is simply an internet component that is well-known, routine and conventional that did not amount to significantly more. See: MPEP 2106.05(d)(II), for example- i. receiving or transmitting data over network, e.g., using the internet to gather data.
As per claim 10, the claim falls into [“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]].
As per Claims 11-15, claims 11-15 recite limitations analogous in scope to those of claims 4-8, and as such are similar rejected.
As per Claims 16-20, claims 16-20 recite limitations analogous in scope to those of claims 1-4 and 8, and as such are similar rejected.
Claim Rejections - 35 USC § 102
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 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US Publication No. 2013/0118736 A1 issued to Usadi et al.
1. Usadi et al discloses a method implemented on a computer system for executing a plurality of elements of a model of a physical system (See: par [0015] The plurality of fine grid models can be simulated using a training simulation to obtain a set of training parameters comprising a potential at each coarse grid cell surrounding the flux interface and a flux across the flux interface. A machine learning algorithm can be used to generate a constitutive relationship that provides a solution to fluid flow through the flux interface. The method also includes simulating the hydrocarbon reservoir using the constitutive relationship and generating a data representation of a physical hydrocarbon reservoir in a non-transitory, computer-readable medium based, at least in part, on the results of the simulation), the method comprising:
estimating a flux between a portion of the plurality of elements (See: Abstract, The method also includes simulating the plurality of fine grid models using a training simulation to obtain a set of training parameters, including a potential at each coarse grid cell surrounding the flux interface and a flux across the flux interface. A machine learning algorithm is used to generate a constitutive relationship that provides a solution to fluid flow through the flux interface. The method also includes simulating the hydrocarbon reservoir using the constitutive relationship and generating a data representation of a physical hydrocarbon reservoir in a non-transitory, computer-readable medium based on the results of the simulation; par [0119] At block 1120, fine grid simulations computed for each of the different mesh scales generated at block 1114 and 1116 may be evaluated to determine an uncertainty estimate for the coarse grid constitutive relationship. This may be done through numerical experiment using a variety of different fine scale parameter distributions. The uncertainty estimate is a measure of the accuracy of the constitutive relationships computed at different coarse scales. The uncertainty estimate may be used to determine an estimated level of geologic feature detail that will provide suitable accuracy during the generation of the training set used to train the neural net);
communicating state data between the portion of the plurality of elements in response to uncertainty in the model of the physical system (See: par [0119] At block 1120, fine grid simulations computed for each of the different mesh scales generated at block 1114 and 1116 may be evaluated to determine an uncertainty estimate for the coarse grid constitutive relationship. This may be done through numerical experiment using a variety of different fine scale parameter distributions. The uncertainty estimate is a measure of the accuracy of the constitutive relationships computed at different coarse scales. The uncertainty estimate may be used to determine an estimated level of geologic feature detail that will provide suitable accuracy during the generation of the training set used to train the neural net; [0141] FIG. 20 is a block diagram of an exemplary cluster computing system 2000 that may be used in exemplary embodiments of the present techniques. The cluster computing system 2000 illustrated has four computing units 2002, each of which may perform calculations for part of the simulation model. However, one of ordinary skill in the art will recognize that the present techniques are not limited to this configuration, as any number of computing configurations may be selected. For example, a small simulation model may be run on a single computing unit 2002, such as a workstation, while a large simulation model may be run on a cluster computing system 2000 having 10, 100, 1000, or even more computing units 2002. In an exemplary embodiment, each of the computing units 2002 will run the simulation for a single subdomain or group of computational cells); and
calculating the flux from state data (See: par [0015] Exemplary embodiments of the present invention provide techniques for using machine learning to model a hydrocarbon reservoir. An exemplary embodiment provides a method for modeling a hydrocarbon reservoir that includes generating a reservoir model that has a plurality of coarse grid cells. The method also includes generating a plurality of fine grid models, each fine grid model corresponding to one of the plurality of coarse grid cells that surround a flux interface. The plurality of fine grid models can be simulated using a training simulation to obtain a set of training parameters comprising a potential at each coarse grid cell surrounding the flux interface and a flux across the flux interface. A machine learning algorithm can be used to generate a constitutive relationship that provides a solution to fluid flow through the flux interface; [0080] To compute the matrix equation solution for the sub region 502, the sub region's boundaries may be partitioned into representative sets by type, for example, flux boundaries and pressure boundaries; [0111] The constitutive relationship between flux and pressure for each coarse grid cell 1010 may be computed using machine learning techniques, for example, using a neural net such as the neural net described in relation to FIG. 4. For example, in accordance with Eqn. 11, the input to the machine learning method such as the neural net may be .PHI..sub.i, geometry, K.sub.v(S.sub.v(t)), and params and the output may be the flux, F, on the boundary of the cell).
2. Usadi et al discloses the method of claim 1, wherein: the plurality of elements is grouped into a plurality of partitions; and the portion of the plurality of elements are on edges of the plurality of partitions (See: [0061] Exemplary embodiments of the present invention provide techniques for using machine learning algorithms to generate solution surrogates for use in simulating a fluid flow in a reservoir such as a hydrocarbon reservoir. A simulation model may be segmented into a plurality of sub regions or coarse cells. Sets of training data may be obtained for a sub region by performing a full-physics simulation of the sub region. The training set may be used to compute the surrogate solution for the sub region through a machine learning algorithm such as a neural net. In some exemplary embodiments, the surrogate solution method may be an approximation of the inverse operator of a matrix equation for the fluid flow through a porous media. In some exemplary embodiments, the surrogate solution may be a formulation of Darcy's law, and supervised machine learning may be used to generate a coarse scale approximation of the phase permeability of a coarse grid cell. In some exemplary embodiments, the surrogate solution may be a constitutive relationship that approximates the flow response at a flux interface of a coarse cell or sub region. Furthermore, a reservoir simulation may include a combination of different types of surrogate solution methods for different regions of space or time; [0080] To compute the matrix equation solution for the sub region 502, the sub region's boundaries may be partitioned into representative sets by type, for example, flux boundaries and pressure boundaries. For each set of boundary condition types, a set of boundary condition values may be specified that mimic the variety of potentially realistic boundary conditions. The matrix element values of each sub region 502 are determined by physical parameters of the system such as rock porosity, phase permeability, and the like. A training simulation may then be used to generate the training set, {b.sub.s, x.sub.s), s=1 . . . S, where the boundary conditions, b.sub.s, may be used as the input 412 to the neural net and, the state variables, x.sub.s may be used as the desired output 416 (FIG. 4); [0085] If, at block 608, it is determined that the sub region 502 is a candidate to be modeled by the machine learning method, the process flow may advance to block 612. At block 612, the boundary of the sub region may be partitioned into a plurality of boundary element types. For the sub-region 102 under consideration, the region's boundary may be partitioned into an appropriate set of boundary elements that will represent the variety of anticipated boundary element types this region is likely to encounter as part of a reservoir simulation model).
3. Usadi et al discloses the method of claim 1, wherein estimating the flux comprises, for each element of the portion of the plurality of elements, calculating the flux based on a previously determined flux value and a state of each element (See: par [0063] Computational cell 202 may be represented by a functional relationship, or "solution surrogate," generated by a machine learning technique, such as a neural net, probability tree, support vector machine, radial basis functions, and the like. Computational cells 202 may interact with adjacent computational cells 202, for example, by having flux properties assigned to a shared border with the adjacent computational cells 202. The flux properties may include heat or mass transfer driven by a difference in temperature or pressure between the adjacent computational cells 202; [0067] At block 306, a linear solver may use a Jacobian matrix to generate an approximate solution for the simulation. Additionally, the approximate solution may be computed using a solution surrogate generated according to the machine learning techniques discussed herein. At block 308, physical properties are calculated from the approximate solution. At block 310, the calculated properties are compared to either previously calculated properties or to measured properties to determine whether a desired accuracy has been reached. In an exemplary embodiment, the determination is made by identifying that the calculated properties have not significantly changed since the last iteration (which may indicate convergence). For example, convergence may be indicated if the currently calculated properties are within 0.01%, 0.1%, 1%, 10%, or more of the previously calculated properties; [0080] To compute the matrix equation solution for the sub region 502, the sub region's boundaries may be partitioned into representative sets by type, for example, flux boundaries and pressure boundaries. For each set of boundary condition types, a set of boundary condition values may be specified that mimic the variety of potentially realistic boundary conditions. The matrix element values of each sub region 502 are determined by physical parameters of the system such as rock porosity, phase permeability, and the like).
4. Usadi et al discloses the method of claim 1, wherein locally estimating the flux comprises calculating the flux using a machine learning model (See: Abstract, A plurality of fine grid models is generated, wherein each fine grid model corresponds to one of the plurality of coarse grid cells that surround a flux interface. The method also includes simulating the plurality of fine grid models using a training simulation to obtain a set of training parameters, including a potential at each coarse grid cell surrounding the flux interface and a flux across the flux interface. A machine learning algorithm is used to generate a constitutive relationship that provides a solution to fluid flow through the flux interface. The method also includes simulating the hydrocarbon reservoir using the constitutive relationship and generating a data representation of a physical hydrocarbon reservoir in a non-transitory, computer-readable medium based on the results of the simulation).
5. Usadi et al discloses the method of claim 4, wherein the machine learning model is a Bayesian neural network (See: A plurality of fine grid models is generated, wherein each fine grid model corresponds to one of the plurality of coarse grid cells that surround a flux interface. The method also includes simulating the plurality of fine grid models using a training simulation to obtain a set of training parameters, including a potential at each coarse grid cell surrounding the flux interface and a flux across the flux interface. A machine learning algorithm is used to generate a constitutive relationship that provides a solution to fluid flow through the flux interface. The method also includes simulating the hydrocarbon reservoir using the constitutive relationship and generating a data representation of a physical hydrocarbon reservoir in a non-transitory, computer-readable medium based on the results of the simulation; 0017] In some embodiments, using the machine learning algorithm to generate the constitutive relationship includes training a neural net using the training parameters, wherein the potential at each coarse gird cell surrounding the flux interface is used as an input to the neural net and the flux across the flux interface is used as a desired output. Another input to the neural net can include geometric parameters of the coarse grid cells surrounding the flux interface).
6. Usadi et al discloses the method of claim 5, wherein the uncertainty in the model comprises an uncertainty in an output of the Bayesian neural network (See: par [0119] At block 1120, fine grid simulations computed for each of the different mesh scales generated at block 1114 and 1116 may be evaluated to determine an uncertainty estimate for the coarse grid constitutive relationship. This may be done through numerical experiment using a variety of different fine scale parameter distributions. The uncertainty estimate is a measure of the accuracy of the constitutive relationships computed at different coarse scales. The uncertainty estimate may be used to determine an estimated level of geologic feature detail that will provide suitable accuracy during the generation of the training set used to train the neural net).
7. Usadi et al discloses the method of claim 1, wherein the uncertainty in the model of the physical system comprises one or more gradients in the model of the physical system satisfying a threshold condition, wherein the one or more gradients include a spatial gradient, a temporal gradient, or both a spatial gradient and a temporal gradient (See: par [0074] The desired output set 414 may be generated by a full-physics simulation of the system under consideration or based on measured characteristics of the system. The objective function computed for one iteration of the neural net computation may be used to alter the weighting values applied to each of the node connections 410 for the next iteration of the neural net computation. The neural net may be iteratively computed and the calculation of the objective function repeated until the objective function is below an acceptable threshold; par [0118] At block 1118, the constitutive relationship between fluid flux and potential gradients for the selected flux interface 1012 may be extracted from the trained neural net 400. In some embodiments, the constitutive relationship used for training may be that of the fine grid solution after it has been averaged or smoothed).
8. Usadi et al discloses the method of claim 1, wherein the uncertainty in the model of the physical system comprises both an uncertainty in an output of a machine learning model used for locally estimating the flux and one or more gradients in the model of the physical system satisfying a threshold condition (See: par [0074] The desired output set 414 may be generated by a full-physics simulation of the system under consideration or based on measured characteristics of the system. The objective function computed for one iteration of the neural net computation may be used to alter the weighting values applied to each of the node connections 410 for the next iteration of the neural net computation. The neural net may be iteratively computed and the calculation of the objective function repeated until the objective function is below an acceptable threshold; par [0118] At block 1118, the constitutive relationship between fluid flux and potential gradients for the selected flux interface 1012 may be extracted from the trained neural net 400. In some embodiments, the constitutive relationship used for training may be that of the fine grid solution after it has been averaged or smoothed).
9. Usadi et al discloses a method comprising:
executing, on a computer system, a model of a physical system including a plurality of elements each having one or more state variables, the plurality of elements divided into a plurality of partitions (See: [0080] To compute the matrix equation solution for the sub region 502, the sub region's boundaries may be partitioned into representative sets by type, for example, flux boundaries and pressure boundaries. For each set of boundary condition types, a set of boundary condition values may be specified that mimic the variety of potentially realistic boundary conditions; par [0085] If, at block 608, it is determined that the sub region 502 is a candidate to be modeled by the machine learning method, the process flow may advance to block 612. At block 612, the boundary of the sub region may be partitioned into a plurality of boundary element types. For the sub-region 102 under consideration, the region's boundary may be partitioned into an appropriate set of boundary elements that will represent the variety of anticipated boundary element types this region is likely to encounter as part of a reservoir simulation model; par [0127] At block 1312 the solution surrogate may be used to compute the state variables such as fluid properties or fluid flow at the interfaces 1212 of the sub region 1202. For example, given an initial set of state variables at the interface 1212, the solution surrogate provides the change in the state variables at the interface 1212 at the end of a given time-step. The flow at the interface 1212 may be governed by any suitable model for pressure and flow change across a boundary, such as the inverse matrix operator, A.sup.-1, Darcy's Law, or a machine learning based constitutive relationship); and
for each element of the plurality of elements that is on an edge of a first partition of the plurality of partitions that is adjacent a second partition of the plurality of partitions (See: [0061] Exemplary embodiments of the present invention provide techniques for using machine learning algorithms to generate solution surrogates for use in simulating a fluid flow in a reservoir such as a hydrocarbon reservoir. A simulation model may be segmented into a plurality of sub regions or coarse cells. Sets of training data may be obtained for a sub region by performing a full-physics simulation of the sub region. The training set may be used to compute the surrogate solution for the sub region through a machine learning algorithm such as a neural net. In some exemplary embodiments, the surrogate solution method may be an approximation of the inverse operator of a matrix equation for the fluid flow through a porous media. In some exemplary embodiments, the surrogate solution may be a formulation of Darcy's law, and supervised machine learning may be used to generate a coarse scale approximation of the phase permeability of a coarse grid cell. In some exemplary embodiments, the surrogate solution may be a constitutive relationship that approximates the flow response at a flux interface of a coarse cell or sub region. Furthermore, a reservoir simulation may include a combination of different types of surrogate solution methods for different regions of space or time; [0080] To compute the matrix equation solution for the sub region 502, the sub region's boundaries may be partitioned into representative sets by type, for example, flux boundaries and pressure boundaries. For each set of boundary condition types, a set of boundary condition values may be specified that mimic the variety of potentially realistic boundary conditions. The matrix element values of each sub region 502 are determined by physical parameters of the system such as rock porosity, phase permeability, and the like. A training simulation may then be used to generate the training set, {b.sub.s, x.sub.s), s=1 . . . S, where the boundary conditions, b.sub.s, may be used as the input 412 to the neural net and, the state variables, x.sub.s may be used as the desired output 416 (FIG. 4); [0085] If, at block 608, it is determined that the sub region 502 is a candidate to be modeled by the machine learning method, the process flow may advance to block 612. At block 612, the boundary of the sub region may be partitioned into a plurality of boundary element types. For the sub-region 102 under consideration, the region's boundary may be partitioned into an appropriate set of boundary elements that will represent the variety of anticipated boundary element types this region is likely to encounter as part of a reservoir simulation model):
for each first time step of a plurality of first time steps of a plurality of time steps,
communicating first state data to each element from the second partition and updating a state of each element according to the state of each element and a first flux value calculated from the state data (See: par [0119] At block 1120, fine grid simulations computed for each of the different mesh scales generated at block 1114 and 1116 may be evaluated to determine an uncertainty estimate for the coarse grid constitutive relationship. This may be done through numerical experiment using a variety of different fine scale parameter distributions. The uncertainty estimate is a measure of the accuracy of the constitutive relationships computed at different coarse scales. The uncertainty estimate may be used to determine an estimated level of geologic feature detail that will provide suitable accuracy during the generation of the training set used to train the neural net; [0141] FIG. 20 is a block diagram of an exemplary cluster computing system 2000 that may be used in exemplary embodiments of the present techniques. The cluster computing system 2000 illustrated has four computing units 2002, each of which may perform calculations for part of the simulation model. However, one of ordinary skill in the art will recognize that the present techniques are not limited to this configuration, as any number of computing configurations may be selected. For example, a small simulation model may be run on a single computing unit 2002, such as a workstation, while a large simulation model may be run on a cluster computing system 2000 having 10, 100, 1000, or even more computing units 2002. In an exemplary embodiment, each of the computing units 2002 will run the simulation for a single subdomain or group of computational cells); and
for each second time step of a plurality of second time steps of the plurality of time steps; estimating an uncertainty in the model of the physical system, determining that the uncertainty in the model of the physical system does not meet a threshold condition (See: [0074] he desired output set 414 may be generated by a full-physics simulation of the system under consideration or based on measured characteristics of the system. The objective function computed for one iteration of the neural net computation may be used to alter the weighting values applied to each of the node connections 410 for the next iteration of the neural net computation. The neural net may be iteratively computed and the calculation of the objective function repeated until the objective function is below an acceptable threshold; par [0119] At block 1120, fine grid simulations computed for each of the different mesh scales generated at block 1114 and 1116 may be evaluated to determine an uncertainty estimate for the coarse grid constitutive relationship. This may be done through numerical experiment using a variety of different fine scale parameter distributions. The uncertainty estimate is a measure of the accuracy of the constitutive relationships computed at different coarse scales. The uncertainty estimate may be used to determine an estimated level of geologic feature detail that will provide suitable accuracy during the generation of the training set used to train the neural net), and
in response to determining that the uncertainty in the model of the physical system does not meet the threshold condition, estimating a second flux value for each element based on the state of each element and a preceding flux value from a preceding time step of the plurality of time steps and updating the state of each element according to the state of each element and the second flux value (See: [0074] A training set including a set of inputs 412 and a set of desired outputs 414 may be used to train the neural net 400, e.g., to set the values of the weights. A set of inputs 412 may be fed into the input layer 404 of the neural net 400. Node values may then be computed for each node in the hidden layer 408. If the neural net includes more than one hidden layer 408, node values are successively computed for each subsequent hidden layer 408. Node values are then computed for the output layer 406 to generate a set of outputs 416 of the neural net. The set of outputs 416 may be compared to a desired output set 414 to determine a measure of the deviation, sometimes referred to as an "objective function" or "cost function," between the set of computed outputs 416 and the desired output set 414. The desired output set 414 may be generated by a full-physics simulation of the system under consideration or based on measured characteristics of the system. The objective function computed for one iteration of the neural net computation may be used to alter the weighting values applied to each of the node connections 410 for the next iteration of the neural net computation. The neural net may be iteratively computed and the calculation of the objective function repeated until the objective function is below an acceptable threshold. After the last iteration of the neural net, the weight values correspond to an approximation of the response function of the system under consideration; [0119] At block 1120, fine grid simulations computed for each of the different mesh scales generated at block 1114 and 1116 may be evaluated to determine an uncertainty estimate for the coarse grid constitutive relationship. This may be done through numerical experiment using a variety of different fine scale parameter distributions. The uncertainty estimate is a measure of the accuracy of the constitutive relationships computed at different coarse scales. The uncertainty estimate may be used to determine an estimated level of geologic feature detail that will provide suitable accuracy during the generation of the training set used to train the neural net).
10. Usadi et al discloses the method of claim 9, further comprising: for each third time step of a plurality of third time steps of the plurality of time steps, (g) estimating an uncertainty in the model of the physical system (See: [0119] At block 1120, fine grid simulations computed for each of the different mesh scales generated at block 1114 and 1116 may be evaluated to determine an uncertainty estimate for the coarse grid constitutive relationship. This may be done through numerical experiment using a variety of different fine scale parameter distributions. The uncertainty estimate is a measure of the accuracy of the constitutive relationships computed at different coarse scales. The uncertainty estimate may be used to determine an estimated level of geologic feature detail that will provide suitable accuracy during the generation of the training set used to train the neural net), (h) determining that the uncertainty in the model of the physical system meets the threshold condition (See: par [0074] A set of inputs 412 may be fed into the input layer 404 of the neural net 400. Node values may then be computed for each node in the hidden layer 408. If the neural net includes more than one hidden layer 408, node values are successively computed for each subsequent hidden layer 408. Node values are then computed for the output layer 406 to generate a set of outputs 416 of the neural net. The set of outputs 416 may be compared to a desired output set 414 to determine a measure of the deviation, sometimes referred to as an "objective function" or "cost function," between the set of computed outputs 416 and the desired output set 414. The desired output set 414 may be generated by a full-physics simulation of the system under consideration or based on measured characteristics of the system. The objective function computed for one iteration of the neural net computation may be used to alter the weighting values applied to each of the node connections 410 for the next iteration of the neural net computation. The neural net may be iteratively computed and the calculation of the objective function repeated until the objective function is below an acceptable threshold. After the last iteration of the neural net, the weight values correspond to an approximation of the response function of the system under consideration), (i) in response to determining that the uncertainty in the model of the physical system meets the threshold condition, communicating second state data to each element from the second partition (See: par [0074] A set of inputs 412 may be fed into the input layer 404 of the neural net 400. Node values may then be computed for each node in the hidden layer 408. If the neural net includes more than one hidden layer 408, node values are successively computed for each subsequent hidden layer 408. Node values are then computed for the output layer 406 to generate a set of outputs 416 of the neural net. The set of outputs 416 may be compared to a desired output set 414 to determine a measure of the deviation, sometimes referred to as an "objective function" or "cost function," between the set of computed outputs 416 and the desired output set 414. The desired output set 414 may be generated by a full-physics simulation of the system under consideration or based on measured characteristics of the system. The objective function computed for one iteration of the neural net computation may be used to alter the weighting values applied to each of the node connections 410 for the next iteration of the neural net computation. The neural net may be iteratively computed and the calculation of the objective function repeated until the objective function is below an acceptable threshold. After the last iteration of the neural net, the weight values correspond to an approximation of the response function of the system under consideration) and (j) updating the state of each element according to the state of each element and a third flux value calculated from the second state data (See: [0067] At block 306, a linear solver may use a Jacobian matrix to generate an approximate solution for the simulation. Additionally, the approximate solution may be computed using a solution surrogate generated according to the machine learning techniques discussed herein. At block 308, physical properties are calculated from the approximate solution. At block 310, the calculated properties are compared to either previously calculated properties or to measured properties to determine whether a desired accuracy has been reached. In an exemplary embodiment, the determination is made by identifying that the calculated properties have not significantly changed since the last iteration (which may indicate convergence). For example, convergence may be indicated if the currently calculated properties are within 0.01%, 0.1%, 1%, 10%, or more of the previously calculated properties. In other embodiments, the determination may be determining if the calculated properties are sufficiently close to measured properties, for example, within 0.01%, 0.1%, 1%, 10%, or more. If the desired accuracy is not reached, process flow returns to block 306 to perform another iteration of the linear solver).
As per Claims 11-20, claims 11-20 recite limitations analogous in scope to those of claims 1-8, and as such are similarly rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
RAISSI et al (US Publication No. 2020/0293594 A1) teaches:
[0037] The methods of the present disclosure provide an interface between probabilistic machine learning and differential equations. In some examples, the methods are driven by data for linear equations using Gaussian process priors tailored to the corresponding integro-differential operators. The only observables are scarce noisy multi-fidelity data for the forces and solutions that are not required to reside on the domain boundary. The resulting predictive posterior distributions quantify uncertainty and lead to adaptive solution refinement via active learning. The methods of the present disclosure circumvent several constraints of numerical discretization as well as the consistency and stability issues of time-integration, and are scalable to high-dimensions. As such, the methods of the present disclosure provide a principled and robust handling of uncertainties due to model inadequacy, parametric uncertainties, and numerical discretization or truncation errors. The methods may further provide a flexible and general platform for Bayesian reasoning and computation.
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KIBROM K. GEBRESILASSIE
Primary Examiner
Art Unit 2189
/KIBROM K GEBRESILASSIE/Primary Examiner, Art Unit 2189 01/14/2026