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 .
Specification
The lengthy specification has not been checked to the extent necessary to
determine the presence of all possible minor errors. Applicant's cooperation is
requested in correcting any errors of which applicant may become aware in the
specification.
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
Claim Objections
Claims 1 and 10 are objected to because of the following informalities:
Claim 1 recites “predicting an initial estimate for a subsequent time step using the obtained current time step information wherein predicting the initial estimate uses a predictive model characterized by one or more model parameters that are pre-determined using a statistical and/or machine learning derived process;”. Examiner propose to correct the limitation as “predicting an initial estimate for a subsequent time step using the obtained current time step information, wherein predicting the initial estimate uses a predictive model characterized by one or more model parameters that are pre-determined using a statistical and/or machine learning derived process;”
Claim 10 recites “wherein the information generated during the current time step comprises at least one of: at least one of the numerical solution for the current time step, 1st derivate information, 2nd derivate information, higher order derivative information, or relating to the number of iterations performed during the iteration process of the current time step; or values for one or more dynamic properties of the real or virtual process or system, wherein the information used during the numerical solving process of the current time step comprises at least one of: information related to a spatial discretization process, physical simulation information or real world information.” Examiner proposed to correct the limitation as Claim 10 recites “wherein the information generated during the current time step comprises at least one of: the numerical solution for the current time step, 1st derivate information, 2nd derivate information, higher order derivative information, or relating to the number of iterations performed during the iteration process of the current time step; or values for one or more dynamic properties of the real or virtual process or system, wherein the information used during the numerical solving process of the current time step comprises : information related to a spatial discretization process, physical simulation information or real world information.”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 17 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 17 recites the limitation "wherein the untrained model parameters are independent of the numerical solving process.” There is insufficient antecedent basis for “the untrained model parameters” in the claim.
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 1-13, 15-18, 21-24, 27, 36 and 41 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. These claims are directed to an abstract idea without significantly more.
As to claim 1,
Step 1: Claim 1 is directed to a method. Therefore, the claim is eligible under Step 1 for being directed to processes.
Step 2A Prong One
Claim 1 recites
obtaining time step information for the current time step; (input data)
predicting an initial estimate for a subsequent time step using the obtained current time step information (mental process)
wherein predicting the initial estimate uses a predictive model characterized by one or more model parameters that are pre-determined using a statistical and/or machine learning derived process; (mere instructions to apply an exception)
and performing a numerical solving process using the numerical solver for the subsequent time step thereby to obtain a numerical solution for the subsequent time step, (math concept)
wherein performing the numerical solving process comprises providing at least the predicted initial estimate to the numerical solver. (mental process)
The claimed concept is a method of determining numerical solution by evaluating time step data based on mathematic relationship directed to “Mental Process” and/or “Mathematical Concepts” grouping. These limitations can be performed in a human mind or using pen and paper.
Therefore, claim 1 is an abstract idea.
Step 2A Prong Two
The collecting data step is recited at a high level of generality (i.e., as a general means of collecting input for use in the evaluation step) and amounts to mere data collecting, which is a form of insignificant extra-solution activity.
The claim did not recite additional elements. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. See applicant’s specification page 28 Fig. 1 for generic computer description.
The judicial exception is not integrated into a practical application.
Step 2B:
The same analysis of Step 2A Prong Two applies here in 2B. The present claim does not recite any limitation that would integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. See MPEP 2106.05(d).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus, claim 1 is not patent eligible. Same conclusion for dependent claims of claim 1. See below.
2. The method of claim 1, wherein the set of equations are representative of one or more aspects of fluid flow based on a known computational fluid dynamics model. (math concept)
3. The method of claim 1, wherein the numerical solving process uses real world information and the method comprises performing one or more measurements to obtain data representative of the real world information. (collecting data)
4. The method of claim 1, wherein the set of equations are representative of at least one aspect of: (data description)
a) an oil, gas or other hydrocarbon reservoir, a geothermal reservoir or a carbon capture sequestration and storage system;
b) a wind turbine, an aerofoil, a water reservoir; or
c) a virtual object or environment that is the subject of a graphic rendering process.
5. The method of claim 1, wherein the set of equations are representative of a subterranean reservoir and wherein the method comprises generating output data from the numerical solving process and processing said output data to determine at least one of: (outputting data)
a) a control parameter for a controllable piece of equipment or apparatus, namely for performing a drilling operation and wherein the method comprises controlling the controllable piece of equipment or apparatus;
b) a dimension of an oil and gas or other subterranean reservoir;
c) a predicted production rate from the subterranean reservoir;
d) a proposed drilling site, wherein the method comprises performing drilling at the proposed drilling site;
e) an operational parameter for one or more aspects of a reservoir and/or for a drilling process, namely at least one of position of wells, number of wells, equipment used in the well completion, injection pressure, location of a platform or choke setting or performing the drilling process in accordance with the operational parameter;
f) a design parameter of a reservoir apparatus, namely at least one of a position of a gas-lift valve, the type of electrical submersible pump or the position of an inflow control device, or wherein the method comprises manufacturing the reservoir apparatus in accordance with the design parameter;
g) an uncertainty in one or more parameters used by the numerical solver process; or
h) performing one or more actions associated with either a subterranean environment or an associated apparatus based on at least the determined numerical solution.
6. The method of claim 1, wherein at least one of a), b) or c): (data description)
a) the numerical solution is representative of at least one physical property or quantity of fluid and/or reservoir or environment, namely at least one of liquid flow rates, pressure, temperature, a fluid property, a mechanical property, or in a subterranean environment; b) the numerical solving process comprises processing physical data and wherein the physical data comprises at least one of porosity, permeability, viscosity; or
c) the numerical solution is representative of at least one of oil pressure, oil saturation, water saturation and gas saturation.
7. The method of claim 1, wherein the numerical solving process is performed on a first processing resource and the numerical solving process is performed on a second processing resource, and the method comprises transmitting the predicted initial estimate for a subsequent time step from the first processing resource to the second processing resource and performing the numerical solving process using the numerical solver in response to receiving the predicted initial estimate. (generic computer functions)
8. The method of claim 1, wherein the time step information comprises information generated during the numerical solving process of the current time step and/or information used by the numerical solver during the numerical solver process of the current time step. (data description)
9. The method of claim 1, wherein obtaining the time step information comprises updating values for one or more properties for the current time step using the solutions from one or more preceding times steps and/or time step information from the previous time step. (mental process)
10. The method of claim 1, wherein the information generated during the current time step comprises at least one of: at least one of the numerical solution for the current time step, 1st derivate information, 2nd derivate information, higher order derivative information, or relating to the number of iterations performed during the iteration process of the current time step; or values for one or more dynamic properties of the real or virtual process or system, wherein the information used during the numerical solving process of the current time step comprises at least one of: information related to a spatial discretization process, physical simulation information or real world information. (data description)
11. The method of claim 1, wherein the current time step information comprises at least the obtained numerical solution for the current time step (data description)
and wherein predicting the initial estimate for the subsequent time step comprises providing the obtained numerical solution for the current time step or a quantity derived from the obtained numerical solution for the current time step to the prediction model. (mental process)
12. The method of claim 1, wherein the numerical solving process at each time step of a sequence of time steps outputs a numerical solution for each successive time step of the sequence of time steps (output data)
and wherein the method further comprises using time step information for each time step to predict an initial estimate for the numerical solving process for the following time step in the sequence of time steps. (mental process)
13. The method of claim 1, wherein the current time step and the subsequent time step are separated by a time step interval, (data description)
wherein the method further comprises performing an adaptive time-step scheme in which the time step interval is determined based on the convergent behaviour of the iteration process, wherein the time step interval for the current or subsequent time step is decreased in response to the solution failing to converge within a desired time or number of iteration steps. (mental process)
15. The method of claim 1, comprising performing a numerical solving process at a time step that is associated with a transient state of the real or virtual system or process. (data description)
16. The method of claim 1, wherein the set of equations are based on a physical model of the one or more fluids in the reservoir or environment, namely a model based on one or more physical principles, rules or constraints and the prediction model is derived using a statistical or machine learning derived process. (data description)
17. The method of claim 1, wherein the prediction model comprises model parameters associated with a model architecture and wherein the untrained model parameters are independent of the numerical solving process. (data description)
18. The method of claim 1, wherein the prediction model comprises at least one of a), b) or c); (data description)
a) an artificial neural network based model;
b) the prediction model is representative of a relationship between one or more input features based on the time step information of the current time step and an output comprising a predicted initial estimate for the subsequent time step and/or wherein the relationship comprises a mathematical transformation between a representation of the one or more input features and a representation of the output; or
c) the prediction model comprises an artificial neural network based model and/or wherein the numerical solver comprises one or more of: a Newton iteration based method, a Runge-Kutta based method.
21. The method of claim 1, wherein at least one of a), b), c), d) or e): (data description)
a) the real or virtual system or comprises one or more of an environment, structure, objects, or a change in one or more of an environmental or structural property; b) the numerical solution is representative of at least one physical property or quantity of one or more aspects of the real or virtual system or process; c) the real or virtual system or process comprises a time-dependent process or system; d) the real or virtual system or process comprises a process that comprises a steady-state or e) the real or virtual system of process comprises a change of state or the real or virtual process is a system or process that does not comprise a steady-state.
36. The method of claim 1, further comprising performing a sensing process using one or more sensors to obtain sensor data associated with the reservoir and wherein the numerical solving process comprises processing at least said sensor data to obtain the numerical solution, (collecting data)
wherein the sensor data comprises at least one of data associated with the subterranean environment, associated apparatus, or object, namely at least one of geometric, pressures, temperatures, flow rates, or material properties. (data description)
41. A computer program product comprising computer-readable instructions that are executable to perform the method of claim 1. (data description)
Same conclusion for independent claims 22, 23, 27 and dependent claims. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. In particular, the claim limitations do not recite a combination of additional elements that tie or “integrate the invention into a practical application”.
Thus, claims 1-13, 15-18, 21-24, 27, 36 and 41 are not patent eligible.
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.
Claim(s) 1-12, 15-18, 21-24, 27, 36 and 41 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Samson et al (US 2020/0202056 A1), hereinafter Samson.
Claim 1. Samson discloses A computer-implemented method of performing a numerical solving process using a numerical solver for one or more times steps to obtain a numerical solution for each of the one or more time steps, wherein performing the numerical solving process at each time step comprises providing an initial estimate to the numerical solver and applying the numerical solver to a set of equations representative of a real or virtual process or system, wherein the method comprises, for a current time step of the one or more time steps:
Samson: [0173-0174] “… As shown, the simulation block 810 involves simulating at a particular time to generate relevant physical properties; the prediction block 820 involves predicting a front position at a next time, which can include characterization of a front as to its orientation; and the discretization block 830 involves applying a discretization technique (e.g., kD-tree) over a region associated with the predicted front to thereby condition a simulator for solving for various values associated with the front at the next time (e.g., per the prediction block). The method 800 can then loop back to the simulation block 810 and continue as appropriate as long as a front exists and/or meets one or more criteria for purposes of prediction and discretization. … As an example, a method can include running a reservoir simulation using a simulator and extracting relevant properties (e.g., of halo cells) at time ti; … and advancing time to the next timestep ti+1 and then iterating until a desired end time step is reached.”
Samson discloses obtaining time step information for the current time step;
Samson: [0203] “As an example, a method of operating a reservoir simulator can include performing a time step of a reservoir simulation using a spatial reservoir model that represents a subterranean environment that includes a reservoir to generate simulation results for a first time where the simulation results include a front defined by at least in part by a gradient at a position between portions of the spatial reservoir model; …”
Samson discloses predicting an initial estimate for a subsequent time step using the obtained current time step information wherein predicting the initial estimate uses a predictive model characterized by one or more model parameters that are pre-determined using a statistical and/or machine learning derived process; and
Samson: [0203] “…predicting a position of the front for a subsequent time step for a corresponding second time using a trained machine model; discretizing the spatial reservoir model locally at the predicted position of the front to generate a locally discretized version of the spatial reservoir model; and performing a time step of the reservoir simulation using the locally discretized version of the spatial reservoir model to generate simulation results for the second time. In such a method, the front can be spatially defined in one or more dimensions. In such a method, multiple fronts may exist, where each front may be a particular type of front or where each front may be of a common type, yet discrete for one or more reasons (e.g., as to injection, as to reactions, as to temperature, as to production, etc.).”
Samson discloses performing a numerical solving process using the numerical solver for the subsequent time step thereby to obtain a numerical solution for the subsequent time step, wherein performing the numerical solving process comprises providing at least the predicted initial estimate to the numerical solver.
Samson: [0173-0174] “… As shown, the simulation block 810 involves simulating at a particular time to generate relevant physical properties; the prediction block 820 involves predicting a front position at a next time, which can include characterization of a front as to its orientation; and the discretization block 830 involves applying a discretization technique (e.g., kD-tree) over a region associated with the predicted front to thereby condition a simulator for solving for various values associated with the front at the next time (e.g., per the prediction block). The method 800 can then loop back to the simulation block 810 and continue as appropriate as long as a front exists and/or meets one or more criteria for purposes of prediction and discretization. … As an example, a method can include running a reservoir simulation using a simulator and extracting relevant properties (e.g., of halo cells) at time ti; … and advancing time to the next timestep ti+1 and then iterating until a desired end time step is reached.”
Claim 2. The method of claim 1, Samson discloses wherein the set of equations are representative of one or more aspects of fluid flow based on a known computational fluid dynamics model.
Samson [0176-0177] “Normally, an application of EOR technique implies a reservoir flooding by a certain agent, which, as explained, could be water of varied salinity, polymer, surfactant, foam, etc. As explained, the agent propagates from an injector with a purpose to visit parts of reservoir which may have remained relatively inaccessible during primary recovery. The simulation of EOR processes can be part of one or more types of workflows. For example, reservoir engineers have a number of methods available to carry out the flooding. Therefore, before starting injection, they can screen to decide which method is most viable. As another example, if the type of EOR flooding is known, simulation can assist to determine an amount of the injected agent, and to predict time of the agent arrival to producers”. See [0041-0042] for additional detail.
Claim 3. The method of claim 1, Samson discloses wherein the numerical solving process uses real world information
Samson [0038] “The Arrhenius equation can be utilized to determine a rate of a chemical reaction and, for example, to calculate an energy of activation. The Arrhenius equation may have some physical justification while some contend that it is a type of empirical relationship, which is supported by real-world data.”
Samson discloses and the method comprises performing one or more measurements to obtain data representative of the real world information.
Samson [0056] “… Entities 122 may include earth entities or geological objects such as wells, surfaces, bodies, reservoirs, etc. In the system 100, the entities 122 can include virtual representations of actual physical entities that are reconstructed for purposes of simulation. The entities 122 may include entities based on data acquired via sensing, observation, etc. (e.g., the seismic data 112 and other information 114). An entity may be characterized by one or more properties (e.g., a geometrical pillar grid entity of an earth model may be characterized by a porosity property). Such properties may represent one or more measurements (e.g., acquired data), calculations, etc.”
Claim 4. The method of claim 1, Samson discloses wherein the set of equations are representative of at least one aspect of:
a) an oil, gas or other hydrocarbon reservoir, a geothermal reservoir or a carbon capture sequestration and storage system;
b) a wind turbine, an aerofoil, a water reservoir; or
c) a virtual object or environment that is the subject of a graphic rendering process.
Samson [0176-0177] “Normally, an application of EOR technique implies a reservoir flooding by a certain agent, which, as explained, could be water of varied salinity, polymer, surfactant, foam, etc. As explained, the agent propagates from an injector with a purpose to visit parts of reservoir which may have remained relatively inaccessible during primary recovery. The simulation of EOR processes can be part of one or more types of workflows. For example, reservoir engineers have a number of methods available to carry out the flooding. Therefore, before starting injection, they can screen to decide which method is most viable. As another example, if the type of EOR flooding is known, simulation can assist to determine an amount of the injected agent, and to predict time of the agent arrival to producers”. See [0041-0042] for additional detail.
Claim 5. The method of claim 1, Samson discloses wherein the set of equations are representative of a subterranean reservoir and wherein the method comprises generating output data from the numerical solving process and processing said output data to determine at least one of:
a) a control parameter for a controllable piece of equipment or apparatus, namely for performing a drilling operation and wherein the method comprises controlling the controllable piece of equipment or apparatus;
b) a dimension of an oil and gas or other subterranean reservoir;
c) a predicted production rate from the subterranean reservoir;
d) a proposed drilling site, wherein the method comprises performing drilling at the proposed drilling site;
e) an operational parameter for one or more aspects of a reservoir and/or for a drilling process, namely at least one of position of wells, number of wells, equipment used in the well completion, injection pressure, location of a platform or choke setting or performing the drilling process in accordance with the operational parameter;
f) a design parameter of a reservoir apparatus, namely at least one of a position of a gas-lift valve, the type of electrical submersible pump or the position of an inflow control device, or wherein the method comprises manufacturing the reservoir apparatus in accordance with the design parameter;
g) an uncertainty in one or more parameters used by the numerical solver process; or
h) performing one or more actions associated with either a subterranean environment or an associated apparatus based on at least the determined numerical solution.
Samson [0041] “… For example, output from a dynamical reservoir simulator can be utilized to determine amounts and/or types of chemical(s) to utilize for chemical EOR, when to and/or not to utilize one or more chemical(s) for chemical EOR, injection rate(s) of chemical(s), etc. As an example, decisions as to types of equipment, types of drilling, types of hydraulic fracturing, etc. may be based at least in part on output from a dynamic reservoir simulator that can characterize dynamic behavior of a reservoir based at least in part on data (e.g., survey data) and one or more models (e.g., that model physical phenomena, etc.).”
Samson [0157] “… For example, a field controller may operate based on output from a simulator's controller that is not yet fully informed by a simulation at a next time step. In such an example, the output may be given a confidence level as it may not be actual simulator results for the next time step. Where the simulator generates the actual simulator results for the next time step, those results may be received by the field generator, for example, with a higher confidence level. Such an approach may be referred to as a multi-tiered approach as a first tier can be based on a dynamic grid controller of the simulator and the second tier can be based on results output by the simulator (e.g., simulator or simulation results).”
Claim 6. The method of claim 1, Samson discloses wherein at least one of a), b) or c): a) the numerical solution is representative of at least one physical property or quantity of fluid and/or reservoir or environment, namely at least one of liquid flow rates, pressure, temperature, a fluid property, a mechanical property, or in a subterranean environment;
Samson: [0173-0174] “… As shown, the simulation block 810 involves simulating at a particular time to generate relevant physical properties; the prediction block 820 involves predicting a front position at a next time, which can include characterization of a front as to its orientation; and the discretization block 830 involves applying a discretization technique (e.g., kD-tree) over a region associated with the predicted front to thereby condition a simulator for solving for various values associated with the front at the next time (e.g., per the prediction block). The method 800 can then loop back to the simulation block 810 and continue as appropriate as long as a front exists and/or meets one or more criteria for purposes of prediction and discretization. … As an example, a method can include running a reservoir simulation using a simulator and extracting relevant properties (e.g., of halo cells) at time ti; … and advancing time to the next timestep ti+1 and then iterating until a desired end time step is reached.”
b) the numerical solving process comprises processing physical data and wherein the physical data comprises at least one of porosity, permeability, viscosity; or c) the numerical solution is representative of at least one of oil pressure, oil saturation, water saturation and gas saturation.
Samson [0056] “… Entities 122 may include earth entities or geological objects such as wells, surfaces, bodies, reservoirs, etc. In the system 100, the entities 122 can include virtual representations of actual physical entities that are reconstructed for purposes of simulation. The entities 122 may include entities based on data acquired via sensing, observation, etc. (e.g., the seismic data 112 and other information 114). An entity may be characterized by one or more properties (e.g., a geometrical pillar grid entity of an earth model may be characterized by a porosity property). Such properties may represent one or more measurements (e.g., acquired data), calculations, etc.”
Claim 7. The method of claim 1, Samson discloses wherein the numerical solving process is performed on a first processing resource and the numerical solving process is performed on a second processing resource,
Samson See example of 1st, 2nd and Nth processing resource in [0214] “In an example embodiment, components may be distributed, such as in the network system 1610. The network system 1610 includes components 1622-1, 1622-2, 1622-3, . . . 1622-N. …”
Samson discloses the method comprises transmitting the predicted initial estimate for a subsequent time step from the first processing resource to the second processing resource
Samson: [0067] “… For example, equipment 152 may include communication circuitry to receive and to transmit information with respect to one or more networks 155. Such information may include information associated with downhole equipment 154, which may be equipment to acquire information, to assist with resource recovery, etc. Other equipment 156 may be located remote from a well site and include sensing, detecting, emitting or other circuitry. Such equipment may include storage and communication circuitry to store and to communicate data, instructions, etc. As an example, one or more satellites may be provided for purposes of communications, data acquisition, etc. …”
Samson discloses performing the numerical solving process using the numerical solver in response to receiving the predicted initial estimate.
Samson: [0173-0174] “… As shown, the simulation block 810 involves simulating at a particular time to generate relevant physical properties; the prediction block 820 involves predicting a front position at a next time, which can include characterization of a front as to its orientation; and the discretization block 830 involves applying a discretization technique (e.g., kD-tree) over a region associated with the predicted front to thereby condition a simulator for solving for various values associated with the front at the next time (e.g., per the prediction block). The method 800 can then loop back to the simulation block 810 and continue as appropriate as long as a front exists and/or meets one or more criteria for purposes of prediction and discretization. … As an example, a method can include running a reservoir simulation using a simulator and extracting relevant properties (e.g., of halo cells) at time ti; … and advancing time to the next timestep ti+1 and then iterating until a desired end time step is reached.”
Claim 8. The method of claim 1, Samson discloses wherein the time step information comprises information generated during the numerical solving process of the current time step and/or information used by the numerical solver during the numerical solver process of the current time step.
Samson: [0173-0174] “… As shown, the simulation block 810 involves simulating at a particular time to generate relevant physical properties; the prediction block 820 involves predicting a front position at a next time, which can include characterization of a front as to its orientation; and the discretization block 830 involves applying a discretization technique (e.g., kD-tree) over a region associated with the predicted front to thereby condition a simulator for solving for various values associated with the front at the next time (e.g., per the prediction block). The method 800 can then loop back to the simulation block 810 and continue as appropriate as long as a front exists and/or meets one or more criteria for purposes of prediction and discretization. … As an example, a method can include running a reservoir simulation using a simulator and extracting relevant properties (e.g., of halo cells) at time ti; … and advancing time to the next timestep ti+1 and then iterating until a desired end time step is reached.”
Claim 9. The method of claim 1, Samson discloses wherein obtaining the time step information comprises updating values for one or more properties for the current time step using the solutions from one or more preceding times steps and/or time step information from the previous time step.
Samson: [0117] “As an example, a method can include adjusting values before performing an iteration, which may be associated with a time increment. As an example, a method can include a reception block for receiving values, an adjustment block for optimizing a quadratic function subject to linear constraints for adjusting at least a portion of the values to provide adjusted values and a simulation block to perform a simulation using at least the portion of the adjusted values.”
See [0115] Pseudo-algorithm for example of updating step.
Claim 10. The method of claim 1, Samson discloses wherein the information generated during the current time step comprises at least one of: at least one of the numerical solution for the current time step, 1st derivate information, 2nd derivate information, higher order derivative information, or relating to the number of iterations performed during the iteration process of the current time step; or values for one or more dynamic properties of the real or virtual process or system,
Samson: [0203] “As an example, a method of operating a reservoir simulator can include performing a time step of a reservoir simulation using a spatial reservoir model that represents a subterranean environment that includes a reservoir to generate simulation results for a first time where the simulation results include a front defined by at least in part by a gradient at a position between portions of the spatial reservoir model; …”
Samson discloses wherein the information used during the numerical solving process of the current time step comprises at least one of: information related to a spatial discretization process, physical simulation information or real world information.
Samson: [0173-0174] “… As shown, the simulation block 810 involves simulating at a particular time to generate relevant physical properties; the prediction block 820 involves predicting a front position at a next time, which can include characterization of a front as to its orientation; and the discretization block 830 involves applying a discretization technique (e.g., kD-tree) over a region associated with the predicted front to thereby condition a simulator for solving for various values associated with the front at the next time (e.g., per the prediction block). The method 800 can then loop back to the simulation block 810 and continue as appropriate as long as a front exists and/or meets one or more criteria for purposes of prediction and discretization. … As an example, a method can include running a reservoir simulation using a simulator and extracting relevant properties (e.g., of halo cells) at time ti; … and advancing time to the next timestep ti+1 and then iterating until a desired end time step is reached.”
Claim 11. The method of claim 1, Samson discloses wherein the current time step information comprises at least the obtained numerical solution for the current time step and wherein predicting the initial estimate for the subsequent time step comprises providing the obtained numerical solution for the current time step or a quantity derived from the obtained numerical solution for the current time step to the prediction model.
Samson: [0173-0174] “… As shown, the simulation block 810 involves simulating at a particular time to generate relevant physical properties; the prediction block 820 involves predicting a front position at a next time, which can include characterization of a front as to its orientation; and the discretization block 830 involves applying a discretization technique (e.g., kD-tree) over a region associated with the predicted front to thereby condition a simulator for solving for various values associated with the front at the next time (e.g., per the prediction block). The method 800 can then loop back to the simulation block 810 and continue as appropriate as long as a front exists and/or meets one or more criteria for purposes of prediction and discretization. … As an example, a method can include running a reservoir simulation using a simulator and extracting relevant properties (e.g., of halo cells) at time ti; … and advancing time to the next timestep ti+1 and then iterating until a desired end time step is reached.”
Claim 12. The method of claim 1, Samson discloses wherein the numerical solving process at each time step of a sequence of time steps outputs a numerical solution for each successive time step of the sequence of time steps and wherein the method further comprises using time step information for each time step to predict an initial estimate for the numerical solving process for the following time step in the sequence of time steps.
Samson: [0157] “…As an example, where a simulator includes a dynamic grid controller that makes predictions as to where a front is to be within a region represented by the grid, that data may be received by a field controller. For example, a field controller may operate based on output from a simulator's controller that is not yet fully informed by a simulation at a next time step. In such an example, the output may be given a confidence level as it may not be actual simulator results for the next time step. Where the simulator generates the actual simulator results for the next time step, those results may be received by the field generator, for example, with a higher confidence level. Such an approach may be referred to as a multi-tiered approach as a first tier can be based on a dynamic grid controller of the simulator and the second tier can be based on results output by the simulator (e.g., simulator or simulation results).”
Claim 15. The method of claim 1, Samson discloses comprising performing a numerical solving process at a time step that is associated with a transient state of the real or virtual system or process.
Samson: [0173-0174] “… As shown, the simulation block 810 involves simulating at a particular time to generate relevant physical properties; the prediction block 820 involves predicting a front position at a next time, which can include characterization of a front as to its orientation; and the discretization block 830 involves applying a discretization technique (e.g., kD-tree) over a region associated with the predicted front to thereby condition a simulator for solving for various values associated with the front at the next time (e.g., per the prediction block). The method 800 can then loop back to the simulation block 810 and continue as appropriate as long as a front exists and/or meets one or more criteria for purposes of prediction and discretization. … As an example, a method can include running a reservoir simulation using a simulator and extracting relevant properties (e.g., of halo cells) at time ti; … and advancing time to the next timestep ti+1 and then iterating until a desired end time step is reached.”
Claim 16. The method of claim 1, Samson discloses wherein the set of equations are based on a physical model of the one or more fluids in the reservoir or environment, namely a model based on one or more physical principles, rules or constraints and the prediction model is derived using a statistical or machine learning derived process.
Samson: [0203] “…predicting a position of the front for a subsequent time step for a corresponding second time using a trained machine model; discretizing the spatial reservoir model locally at the predicted position of the front to generate a locally discretized version of the spatial reservoir model; and performing a time step of the reservoir simulation using the locally discretized version of the spatial reservoir model to generate simulation results for the second time. In such a method, the front can be spatially defined in one or more dimensions. In such a method, multiple fronts may exist, where each front may be a particular type of front or where each front may be of a common type, yet discrete for one or more reasons (e.g., as to injection, as to reactions, as to temperature, as to production, etc.).”
Claim 17. The method of claim 1, Samson discloses wherein the prediction model comprises model parameters associated with a model architecture and wherein the untrained model parameters are independent of the numerical solving process.
Samson: [0145] “FIG. 5 shows an example of a method 500 of operating a reservoir simulator that includes a performance block 510 for performing a time step of a reservoir simulation using a spatial reservoir model that represents a subterranean environment that includes a reservoir to generate simulation results for a first time where the simulation results include a front defined by at least in part by a gradient at a position between a first portion and a second portion of the spatial reservoir model; [correspond to the untrained model parameters are independent of the numerical solving process] a prediction block 520 for predicting a position of the front for a subsequent time step for a corresponding second time using a trained machine model; a discretization block 530 for discretizing the spatial reservoir model locally at the predicted position of the front to generate a locally discretized version of the spatial reservoir model; and a performance block 540 for performing a time step of the reservoir simulation using the locally discretized version of the spatial reservoir model to generate simulation results for the second time.”
Claim 18. The method of claim 1, Samson discloses wherein the prediction model comprises at least one of a), b) or c);
a) an artificial neural network based model;
b) the prediction model is representative of a relationship between one or more input features based on the time step information of the current time step and an output comprising a predicted initial estimate for the subsequent time step and/or wherein the relationship comprises a mathematical transformation between a representation of the one or more input features and a representation of the output; or
c) the prediction model comprises an artificial neural network based model and/or wherein the numerical solver comprises one or more of: a Newton iteration based method, a Runge-Kutta based method.
Samson: [0203] “…predicting a position of the front for a subsequent time step for a corresponding second time using a trained machine model; discretizing the spatial reservoir model locally at the predicted position of the front to generate a locally discretized version of the spatial reservoir model; and performing a time step of the reservoir simulation using the locally discretized version of the spatial reservoir model to generate simulation results for the second time. In such a method, the front can be spatially defined in one or more dimensions. In such a method, multiple fronts may exist, where each front may be a particular type of front or where each front may be of a common type, yet discrete for one or more reasons (e.g., as to injection, as to reactions, as to temperature, as to production, etc.).” See [0173-0174] for additional detail.
Claim 21. The method of claim 1, Samson discloses wherein at least one of a), b), c), d) or e):
a) the real or virtual system or comprises one or more of an environment, structure, objects, or a change in one or more of an environmental or structural property; b) the numerical solution is representative of at least one physical property or quantity of one or more aspects of the real or virtual system or process; c) the real or virtual system or process comprises a time-dependent process or system; d) the real or virtual system or process comprises a process that comprises a steady-state or e) the real or virtual system of process comprises a change of state or the real or virtual process is a system or process that does not comprise a steady-state.
Samson: [0203] “As an example, a method of operating a reservoir simulator can include performing a time step of a reservoir simulation using a spatial reservoir model that represents a subterranean environment that includes a reservoir to generate simulation results for a first time where the simulation results include a front defined by at least in part by a gradient at a position between portions of the spatial reservoir model; …”
Regarding Claim 22, the same ground of rejection is made as discussed above for substantially similar rationale of claim 1.
In addition, Claim 22 recites “an apparatus comprising a processing resource”.
Samson discloses an apparatus comprising a processing resource on [0087] Fig. 2
Claim 23. A computer-implemented method of training a prediction model for use in a numerical solving process comprising:
Samson discloses obtaining training data comprising time step information for a plurality of time steps,
Samson: [0203] “As an example, a method of operating a reservoir simulator can include performing a time step of a reservoir simulation using a spatial reservoir model that represents a subterranean environment that includes a reservoir to generate simulation results for a first time where the simulation results include a front defined by at least in part by a gradient at a position between portions of the spatial reservoir model; …” See [0104] for additional detail.
Samson discloses wherein the time step information comprises at least a numerical solution obtained using a numerical solving process for each time step;
Samson: [0173-0174] “… As shown, the simulation block 810 involves simulating at a particular time to generate relevant physical properties; the prediction block 820 involves predicting a front position at a next time, which can include characterization of a front as to its orientation; and the discretization block 830 involves applying a discretization technique (e.g., kD-tree) over a region associated with the predicted front to thereby condition a simulator for solving for various values associated with the front at the next time (e.g., per the prediction block). The method 800 can then loop back to the simulation block 810 and continue as appropriate as long as a front exists and/or meets one or more criteria for purposes of prediction and discretization. … As an example, a method can include running a reservoir simulation using a simulator and extracting relevant properties (e.g., of halo cells) at time ti; … and advancing time to the next timestep ti+1 and then iterating until a desired end time step is reached.”
Samson discloses performing a statistical or machine learning derived process using the time step information to determine model parameters for a prediction model such that the determined model parameters form a prediction model relating time step information for a current time step to an initial estimate for a numerical solving process for a subsequent time step;
Samson: [0203] “…predicting a position of the front for a subsequent time step for a corresponding second time using a trained machine model; discretizing the spatial reservoir model locally at the predicted position of the front to generate a locally discretized version of the spatial reservoir model; and performing a time step of the reservoir simulation using the locally discretized version of the spatial reservoir model to generate simulation results for the second time. In such a method, the front can be spatially defined in one or more dimensions. In such a method, multiple fronts may exist, where each front may be a particular type of front or where each front may be of a common type, yet discrete for one or more reasons (e.g., as to injection, as to reactions, as to temperature, as to production, etc.).”
Samson discloses storing the plurality of values for the model parameters of the predictive model.
Samson [0213] “FIG. 16 shows components of an example of a computing system 1600 and an example of a networked system 1610, either of which may be utilized in one or more systems, methods, etc., as described herein. The system 1600 includes one or more processors 1602, memory and/or storage components 1604, one or more input and/or output devices 1606 and a bus 1608. In an example embodiment, instructions may be stored in one or more computer-readable media (e.g., memory/storage components 1604). Such instructions may be read by one or more processors (e.g., the processor(s) 1602) via a communication bus (e.g., the bus 1608), which may be wired or wireless. The one or more processors may execute such instructions to implement (wholly or in part) one or more attributes (e.g., as part of a method)….”
Claim 24. The method of claim 23, Samson discloses further comprising at least one of a), b) or c); a) performing a refinement process of the model parameters in response to receiving further training data thereby to update the values of the model parameters; b) obtaining training data from one or more computing resources over a network, and providing the determined model parameters to one or more remote computing resources; or c) wherein the numerical solving process comprises an iterative process using the numerical solver and/or the numerical solving process forms part of a physics-based simulation.
Samson: [0203] “…predicting a position of the front for a subsequent time step for a corresponding second time using a trained machine model; discretizing the spatial reservoir model locally at the predicted position of the front to generate a locally discretized version of the spatial reservoir model; and performing a time step of the reservoir simulation using the locally discretized version of the spatial reservoir model to generate simulation results for the second time. In such a method, the front can be spatially defined in one or more dimensions. In such a method, multiple fronts may exist, where each front may be a particular type of front or where each front may be of a common type, yet discrete for one or more reasons (e.g., as to injection, as to reactions, as to temperature, as to production, etc.).”
Regarding Claim 27, the same ground of rejection is made as discussed above for substantially similar rationale of claim 23.
In addition, Claim 27 recites “wherein the system further comprises: a memory for storing”.
Samson discloses wherein the system further comprises: a memory for storing resource on [0087] Fig. 2
Claim 36. The method of claim 1, Samson discloses further comprising performing a sensing process using one or more sensors to obtain sensor data associated with the reservoir and wherein the numerical solving process comprises processing at least said sensor data to obtain the numerical solution,
Samson: [0166] As an example, the computerized control equipment 780 can include one or more processors, memory that store instructions executable by a processor, and one or more interfaces, which can include interfaces for transmission of information and/or receipt of information from one or more pieces of equipment in the system 700, which may include one or more sensors, one or more actuators, etc.
Samson discloses wherein the sensor data comprises at least one of data associated with the subterranean environment, associated apparatus, or object, namely at least one of geometric, pressures, temperatures, flow rates, or material properties.
Samson: [0168] “… As an example, a simulator may account for chemicals that are surface active and that, due to such properties, interact with one or more types of rock (e.g., reservoir rock). For example, a chemical can have an affinity for one or more types of minerals found in reservoirs, causing adsorption of chemicals from solution onto the rock in various quantities. A simulator can estimate subsurface conditions and can control one or more pieces of equipment, optionally in real-time and optionally with feedback data as acquired by one or more sensors that are subsurface and/or one or more sensors associated with producing fluid and/or processing fluid (e.g., consider determinations as to water fraction, oil fraction, state of chemicals, microemulsions, etc.).”
Claim 41. Samson discloses A computer program product comprising computer-readable instructions that are executable to perform the method of claim 1.
Samson [0087] Fig. 2
Allowable Subject Matter
Claim 13 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and to overcome the rejection(s) under 35 U.S.C. 101.
The following is a statement of reasons for the indication of allowable subject matter:
Samson et al (US2020/0202056 A1) teaches a method of operating a reservoir simulator. The steps include performing a time step of a reservoir simulation using a spatial reservoir model that represents a subterranean environment that includes a reservoir to generate simulation results for a first time where the simulation results include a front defined by at least in part by a gradient at a position between portions of the spatial reservoir model; predicting a position of the front for a subsequent time step for a corresponding second time using a trained machine model; discretizing the spatial reservoir model locally at the predicted position of the front to generate a locally discretized version of the spatial reservoir model; and performing a time step of the reservoir simulation using the locally discretized version of the spatial reservoir model to generate simulation results for the second time.
Shaalan et al (US 2022/0283336 A1) teaches a method for obtaining grid model data for a geological region of interest and well data for various wells in the geological region of interest.
Raissi et al (US2020/0293594 A1) teaches a method or analyzing an object includes modeling the object with a differential equation, such as a linear partial differential equation (PDE), and sampling data associated with the differential equation. The method uses a probability distribution device to obtain the solution to the differential equation.
These references taken either alone or in combination with the prior art of record fail to disclose limitations, including:
Claim 13. The method of claim 1, wherein the current time step and the subsequent time step are separated by a time step interval, wherein the method further comprises performing an adaptive time-step scheme in which the time step interval is determined based on the convergent behaviour of the iteration process, wherein the time step interval for the current or subsequent time step is decreased in response to the solution failing to converge within a desired time or number of iteration steps.
in combination with the remaining elements and features of the claimed invention.
Conclusion
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/CHUEN-MEEI GAN/Primary Examiner, Art Unit 2189
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