DETAILED ACTION
1. This office action is in responsive to the applicant’s arguments filed on 10/16/25.
2. The present application is being examined under the first inventor to file provisions of the AIA .
3. Claims 1, 3 and 5-17 are currently pending.
4. Claims 1, 3 and 5-17 are amended.
Response to Arguments
Response: 35 U.S.C. § 101
5. Examiner Response:
Applicant’s arguments, see pages 7-8, filed 7/8/21, with respect to the 35 U.S.C. 101
rejections have been fully considered. The amendment to the claims implement the cited abstract idea into a practical application, where training data is created and a trained neural network is generated by training a neural network to identify premature terminations of the computer simulations by recognizing trends, patterns, and correlations between characteristics of the training data for each reservoir. With the abstract idea being integrated into a practical application, the claim is no longer directed to an abstract idea. The 35 U.S.C. 101 rejections of claims 1, 3 and 5-17 has been withdrawn.
Response: 35 U.S.C. § 103
6. Applicants argue:
The applicant argues that the Ozgen reference doesn’t teach the recently amended limitation of claim 1 that states “determining, using the trained neural network, a first metric representing a likelihood that a first computer simulation of the first reservoir is executable to completion without prematurely terminating due to a processing failure, wherein first metric is determined using a computer model and the first data, wherein the first metric is determined prior to a performance of the first computer simulation of the first reservoir by a computer system”. The applicant argues that the phrase “history matching error” of the Ozgen reference doesn’t correspond to the recently amended limitation of claim 1 shown above.
7. Examiner Response:
The examiner notes that the Ozgen reference it teaches generating a correlation model that can provide a correlation between dependent and independent variables, wherein the independent variables can be varied using a neural network that is trained. Also, within the Ozgen reference, it teaches calculating a history matching error based on the simulation model performance and historical performance data. The examiner considers the close fit (history matching error) of the simulation model and the historical performance data to be the likelihood that a first computer simulation of the first reservoir is executable to completion without prematurely terminating due to a processing failure, since the close fit between the simulation model and the historical performance data results in reliable prediction, see paragraphs [0079] and [0150] – [0151] of the Ozgen reference. Also, the examiner considers the reliable prediction to be the simulation of the reservoir being performed to completion without prematurely terminating due to a failure, since by having a prediction that is reliable to the actual reservoir production, the simulation will be within an acceptable tolerance range, see paragraphs [0082], [0086], [0142] and Fig. 12 of the Ozgen reference. With the simulation being within a tolerance range, demonstrates that the simulation is being performed to completion, since the simulation is in a range where the most desirable results are obtained. Also, in paragraph [0003] of the specification, it states that parameters of the computer simulation can be modified, where the simulation is repeated until the output falls within an acceptable tolerance range “[0003] In some implementations, a computer system can perform one or more iterative calculation processes to simulate the characteristics of a physical environment. As an example, a computer system can retrieve input data regarding known properties of the reservoir. Further, the computer simulation can iteratively perform calculations based on the input data to simulate the flow of fluid in the reservoir. In each iteration, the parameters of the computer simulation can be modified, and the calculations can be repeated until the output of the calculations falls within an acceptable tolerance range. This may be referred to as the computer simulation "converging" onto a solution.”.
8. The examiner’s response regarding the applicant’s arguments to the newly added limitations are shown below.
Claim Objections
Claims 5 and 6 are objected to because of the following informalities: Claim 5 recites the
limitation "the notification" in line 6 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 6 recites the limitation "the notification" in line 6 of the claim. There is
insufficient antecedent basis for this limitation in the claim. Appropriate correction is required.
Claim Rejections - 35 USC § 103
10. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35
U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3 and 5-17 are rejected under 35 U.S.C. 103 as being unpatentable over
Ozgen (U.S. PGPub 2007/0016389) in view of Buchan (U.S. PGPub 2004/0153437).
With respect to claim 1, Ozgen discloses “A computer-implemented method” as [Ozgen (paragraph [0009] “In one aspect of the present invention, there is provided a method and software system for utilizing data and associated analysis of history matching error to improve the predicted recovery of reservoir fluids”, Ozgen paragraph [0150] “The trained neural network may be installed on the computer to provide analysis of expected changes in the simulation model corresponding to changes in any one or combination of the independent variables.”)];
“generating industrial process data characterizing variable parameters of system components during execution of industrial processes at a plurality of reservoirs” as [Ozgen (paragraph [0079] “In one embodiment, the present invention first receives, processes, and interprets well bore and reservoir data, such as oil, gas, and water production history, pressure information, permeability, porosity, for the construction of a simulation model of the reservoir”, Ozgen paragraph [0143] “In accordance with another aspect of the present invention, once the historical well bore and/or reservoir data and simulation model data have been provided and history matching errors between the historical and theoretical model data have been determined, there is provided a method and system of minimizing the history matching error using a neural network”)];
“executing computer simulations of the industrial processes to simulate the industrial
process data” as [Ozgen (paragraph [0142] “Once it is determined that the absolute value of the calculated history matching error between the simulated model and the actual historical data for the well is within an acceptable range or below a predetermined value from Simulation History 14 using History Match Loop 18, the future performance of the reservoir from well to well, including gas, oil, and water production outputs and pressure-time plots, or any other reservoir performance characteristic known to one skilled in the art can be calculated by Simulation Predictor 16. Because the simulated model has been run and rerun to ensure that it is able to reproduce historical data for the well, the present invention provides predicted information that is tested, reliable, and more closely relates to the actual performance of the fluid reservoir.”, Fig. 12)];
“creating training data comprising the industrial process data and outcomes of computer simulations of the industrial processes using the industrial process data” as [Ozgen (paragraph [0082] “As shown in FIG. 12, in a first embodiment of the system and method of the present invention includes Data Analysis 10 for receiving and interpreting data, Simulation Model Development 12 for consolidating the data into a simulated reservoir model, Simulation History Matching 14 for carrying out interactive history matching or history matching error determination, History Match Loop 16 for modifying a parameter(s) of the original data to iteratively arrive at a simulated model which substantially matches the historical output data for the reservoir, Simulation Predictor 18 for predicting future performance of the reservoir, and Graph (not shown) for representing the calculated history matching error as a graphical (plot or map) representation once a history matching error has been calculated.”, Ozgen paragraph [0142] “Once it is determined that the absolute value of the calculated history matching error between the simulated model and the actual historical data for the well is within an acceptable range or below a predetermined value from Simulation History 14 using History Match Loop 18, the future performance of the reservoir from well to well, including gas, oil, and water production outputs and pressure-time plots, or any other reservoir performance characteristic known to one skilled in the art can be calculated by Simulation Predictor 16. Because the simulated model has been run and rerun to ensure that it is able to reproduce historical data for the well, the present invention provides predicted information that is tested, reliable, and more closely relates to the actual performance of the fluid reservoir.”, Fig. 12)];
“generating a trained neural network by training a neural network to identify premature terminations of the computer simulations by recognizing trends, patterns, and correlations between characteristics of the training data for each reservoir of plurality of reservoirs, and a respective outcome of a respective computer simulation that was performed using the industrial process data, wherein the neural network comprises a plurality of interconnected nodes” as [Ozgen (paragraph [0144] “Neural networks are a well-recognized mathematical technique that analyze large quantities of data and develop relationships between the independent variables in the data and the dependent variables in the data. These relationships can be used to predict or improve upon (optimize) the dependent variable results given a set of independent variables. Typically, the neural network uses a training data set to build a system of neural interconnects and weighted links between an input layer (independent variable), a hidden layer(s) of neural interconnects, and an output layer (the results, i.e. dependant variables).”, Ozgen paragraph [0145] “The present invention utilizes neural network technology to aid the user in determining the relationship(s) between a number of user-selected independent variables and the difference in the simulator's predicted versus historical production and pressure results, mismatches, based on particular values of those parameters. The terms "history matching parameter" and "independent variable" for the neural network are used interchangeably herein. The history matching parameters are any characteristic of the well bore, well bores and/or reservoirs described herein and include such characteristics as porosity, permeability, residual oil saturation, and the like. The mathematical relationship between the independent and dependent variables is referred to as a "correlation model." The correlation model developed by the neural network of the present invention enables the user to determine which independent variables most impact the dependent variables more rapidly than the known "run/analyze/change/analyze" processes. In addition, the neural network of the present invention provides the user with the best combination of the independent variable values that minimizes the history matching error (dependent variables) between the historical (observed) data (oil, water, gas production and reservoir pressure) and the simulated values of these same data.”)];
“obtaining first data indicating a plurality of properties of the first reservoir” as [Ozgen (paragraph [0079] “In one embodiment, the present invention first receives, processes, and interprets well bore and reservoir data, such as oil, gas, and water production history, pressure information, permeability, porosity, for the construction of a simulation model of the reservoir.”, Ozgen paragraph [0081] “The software may be stored on any medium, typically optical or magnetic, that is capable of retaining the software code and readable by or executed on a general purpose computer system”., Ozgen paragraph [0143] “In accordance with another aspect of the present invention, once the historical well bore and/or reservoir data and simulation model data have been provided and history matching errors between the historical and theoretical model data have been determined, there is provided a method and system of minimizing the history matching error using a neural network”, The examiner considers the reservoir data to be the first data, since the reservoir data includes properties of a reservoir such as pressure information, permeability, etc.)];
“determining, using the trained neural network, a first metric representing a likelihood that a first computer simulation of the first reservoir is executable to completion without prematurely terminating due to a processing failure, wherein first metric is determined using a computer model and the first data” as [Ozgen (paragraph [0079] “A history matching error is calculated based on the simulated model performance and historical performance data, and any inconsistencies between the simulation model and actual performance data are reconciled and adjustments are made to the received data and resulting simulated model. Once a close fit between the simulation model performance and the historical performance data is achieved, the future performance of the well bore and/or reservoir can be reliably predicted. At any time, the calculated history matching error between the simulated and historical performance may be represented as a graphical representation in the form of a plot or map, for example.”, Ozgen paragraph [0082] ““As shown in FIG. 12, in a first embodiment of the system and method of the present invention includes Data Analysis 10 for receiving and interpreting data, Simulation Model Development 12 for consolidating the data into a simulated reservoir model, Simulation History Matching 14 for carrying out interactive history matching or history matching error determination, History Match Loop 16 for modifying a parameter(s) of the original data to iteratively arrive at a simulated model which substantially matches the historical output data for the reservoir, Simulation Predictor 18 for predicting future performance of the reservoir, and Graph (not shown) for representing the calculated history matching error as a graphical (plot or map) representation once a history matching error has been calculated.”, Ozgen paragraph [0150] “It is understood that the neural network can be trained using changes in any one or any combination of any of the independent and dependent variables previously described. The trained neural network may be installed on the computer to provide analysis of expected changes in the simulation model corresponding to changes in any one or combination of the independent variables.”, Ozgen paragraph [0151] “The present invention further comprises using the neural network to provide a correlation, i.e. a correlation model, to provide a correlation between the dependent and independent variables, wherein the independent variables can be varied using the neural network 108. By varying the particular selection of independent variables and a value therefore, one or more sets of independent variables can be provided which will minimize the history matching error. The correlation model can rapidly analyze, and in one embodiment, graphically display the sensitivity of the history matching error to the entire value range of each independent variable.”, The examiner considers the close fit (history matching error) of the simulation model and the historical performance data to be the likelihood that a first computer simulation of the first reservoir can be performed to completion without prematurely terminating due to a failure, since the close fit between the simulation model and the historical performance data results in reliable prediction. Also, the examiner considers the reliable prediction to be the simulation of the reservoir being performed to completion without prematurely terminating due to a failure, since by having a prediction that is reliable to the actual reservoir production, the simulation will be within an acceptable tolerance range. With the simulation being within a tolerance range, demonstrates that the simulation is being performed to completion, since the simulation is in a range where the most desirable results are obtained. Also, in paragraph [0003] of the specification, it states that parameters of the computer simulation can be modified, where the simulation is repeated until the output falls within an acceptable tolerance range “[0003] In some implementations, a computer system can perform one or more iterative calculation processes to simulate the characteristics of a physical environment. As an example, a computer system can retrieve input data regarding known properties of the reservoir. Further, the computer simulation can iteratively perform calculations based on the input data to simulate the flow of fluid in the reservoir. In each iteration, the parameters of the computer simulation can be modified, and the calculations can be repeated until the output of the calculations falls within an acceptable tolerance range. This may be referred to as the computer simulation "converging" onto a solution.”.)];
“wherein the first metric is determined prior to a performance of the first computer simulation of the first reservoir by a computer system” as [Ozgen (paragraph [0079] “A history matching error is calculated based on the simulated model performance and historical performance data, and any inconsistencies between the simulation model and actual performance data are reconciled and adjustments are made to the received data and resulting simulated model. Once a close fit between the simulation model performance and the historical performance data is achieved, the future performance of the well bore and/or reservoir can be reliably predicted.”, Ozgen paragraph [0150] “It is understood that the neural network can be trained using changes in any one or any combination of any of the independent and dependent variables previously described. The trained neural network may be installed on the computer to provide analysis of expected changes in the simulation model corresponding to changes in any one or combination of the independent variables.”, Ozgen paragraph [0151] “The present invention further comprises using the neural network to provide a correlation, i.e. a correlation model, to provide a correlation between the dependent and independent variables, wherein the independent variables can be varied using the neural network 108. By varying the particular selection of independent variables and a value therefore, one or more sets of independent variables can be provided which will minimize the history matching error. The correlation model can rapidly analyze, and in one embodiment, graphically display the sensitivity of the history matching error to the entire value range of each independent variable.”, The examiner considers the close fit (history matching error) of the simulation model and the historical performance data to be the first metric representing a likelihood that a first computer simulation of the first reservoir can be performed to completion. This process of close fit (history matching error) is occurring before the reliable prediction is occurring. As stated above in section 6 of the current office action, the examiner considers the reliable prediction to be the simulation of the reservoir being performed to completion, since by having a prediction that is reliable to the actual reservoir production, the simulation will be within an acceptable tolerance range. Therefore, the limitation of claim 1 shown above that states “wherein the first metric is determined prior to a performance of the first computer simulation of the first reservoir by a computer system”, is being taught by the Ozgen reference, where the close fit (history matching error) of the simulation model and the historical performance data is determined prior to the reliable prediction)];
“a plurality of output nodes, at least some of the output nodes representing the first metric” as Ozgen paragraph [0079] “A history matching error is calculated based on the simulated model performance and historical performance data, and any inconsistencies between the simulation model and actual performance data are reconciled and adjustments are made to the received data and resulting simulated model. Once a close fit between the simulation model performance and the historical performance data is achieved, the future performance of the well bore and/or reservoir can be reliably predicted. At any time, the calculated history matching error between the simulated and historical performance may be represented as a graphical representation in the form of a plot or map, for example”, Ozgen paragraph [0144] “Neural networks are a well-recognized mathematical technique that analyze large quantities of data and develop relationships between the independent variables in the data and the dependent variables in the data. These relationships can be used to predict or improve upon (optimize) the dependent variable results given a set of independent variables. Typically, the neural network uses a training data set to build a system of neural interconnects and weighted links between an input layer (independent variable), a hidden layer(s) of neural interconnects, and an output layer (the results, i.e. dependent variables).”)];
“and a plurality of weighted nodes interconnecting the plurality of input nodes and the plurality of output nodes, the weight nodes representing respective relationships between the interconnected inputs nodes and output nodes” as [Ozgen (paragraph [0144] “Neural networks are a well-recognized mathematical technique that analyze large quantities of data and develop relationships between the independent variables in the data and the dependent variables in the data. These relationships can be used to predict or improve upon (optimize) the dependent variable results given a set of independent variables. Typically, the neural network uses a training data set to build a system of neural interconnects and weighted links between an input layer (independent variable), a hidden layer(s) of neural interconnects, and an output layer (the results, i.e. dependent variables).”)];
“determining, using the one or more processors, that the first metric is less than a threshold level” as [Ozgen (paragraph [0151] “The present invention further comprises using the neural network to provide a correlation, i.e. a correlation model, to provide a correlation between the dependent and independent variables, wherein the independent variables can be varied using the neural network 108. By varying the particular selection of independent variables and a value therefore, one or more sets of independent variables can be provided which will minimize the history matching error. The correlation model can rapidly analyze, and in one embodiment, graphically display the sensitivity of the history matching error to the entire value range of each independent variable.”, The examiner considers the minimizing the history matching error as determining a first metric is less than a threshold level, since particular independent variables and a value are selected and varied to minimize the history matching error. Knowing that there’s an error from the correlation between the dependent and independent variables, results in varying the independent variables and a value)];
“preventing, based on determining that the first metric is less than the threshold
level, the computer system from performing the first computer simulation
using the computer model and the first data to avoid encountering a premature termination of the first computer simulation after initiating the first computer simulation” as [Ozgen (paragraph [0085] “In one embodiment, the observed data may include monthly production or injection volumes of phases, (static) reservoir pressure from build-up tests or shut-in observation wells, and (dynamic) bottom-hole pressure data from flowing wells, for example. Preferably, the monthly production and injection volumes are kept as vectors, so the amount of data has minimal affect on the performance (speed) of the method and system. However, since pressure-related information may be considered "spot data," a large amount of data adversely impacts performance. Because of this, it may be appropriate to reduce the amount of flowing bottom hole pressure (BHP) data before loading.”, Ozgen paragraph [0086] “Data Analysis 10 includes various analyses functionalities that enable any suitable well bore and/or reservoir data to be easily integrated and transferred to Simulation Model Development 12 in a form suitable for the Simulation Model Development 12.”, The Data Analysis component (component 10 of Fig. 12) of the system includes various analyses functionalities that enable any suitable well bore and/or reservoir data to be easily integrated and transferred to Simulation Model Development
component of the system in a form suitable for the Simulation Model Development. One of
functions is modifying and correlating stratigraphic units. The examiner considers the large
amount of pressure related data that can impact the performance of a reservoir that can be used
as input into a simulation, as being preventing computer system from performing the first
computer simulation using the computer model and the first data to avoid encountering a
premature termination of the first computer simulation after initiating the first computer
simulation, since if the large amount of pressure data impacts the performance of the reservoir in
an adverse way where the wellbore/ reservoir is not suitable. As mention above, the Data
Analysis component includes various analyses functionalities that enable any suitable well bore
and/or reservoir data. With the reservoir data not being suitable with the large amount of
pressure data, the simulation will be prevented.)];
While Ozgen teaches obtaining first data indicating a plurality of properties of the first reservoir, Ozgen does not explicitly disclose “obtaining, from sensors of a first reservoir, first data indicating a plurality of properties of the first reservoir”
Buchan discloses “obtaining, from sensors of a first reservoir, first data indicating a plurality of properties of the first reservoir” as [Buchan (paragraph [0006] “The apparatus includes a distributed control system to receive readings from facility sensor devices and transmit control signals to actuated elements to monitor and control the process, and a process parameter data historical database interfaced with the distributed control system.”)];
Ozgen and Buchan are analogous art because they are from the same field
endeavor of analyzing the production of a reservoir.
Before the effective filing date of the invention, it would have been obvious to a person
of ordinary skill in the art to modify the teachings of Ozgen of obtaining first data indicating a plurality of properties of the first reservoir by incorporating obtaining, from sensors of a first reservoir, first data indicating a plurality of properties of the first reservoir as taught by Buchan for the purpose of alerting a user of the status of the reservoir.
Ozgen and Buchan teaches obtaining, from sensors of a first reservoir, first data indicating a plurality of properties of the first reservoir.
The motivation for doing so would have been because Buchan teaches that by alerting a user of the status of a reservoir, the user has the ability to input request to resolve the production losses (Buchan (paragraph [0007])).
With respect to claim 3, the combination of Ozgen and Buchan discloses the method of claim 1 above, and Buchan further discloses “wherein the computer system is a distributed computer system” as [Buchan [0064] “Information about an asset can exist in multiple sources and systems, including process control systems, enterprise management systems, computerized maintenance management systems (CMMS), technical and engineering document management systems, vendor data including both procurement and technical vendor data, equipment monitoring systems, logistics tracking systems, individual staff applications, and so on”)];
With respect to claim 5, the combination of Ozgen and Buchan discloses the method of claim 1 above, and Ozgen further discloses “identifying one or more portions of the first data that are likely to prevent the first computer simulation of the first reservoir from being performed to completion using the computer model” as [Ozgen (paragraph [0150] “It is understood that the neural network can be trained using changes in any one or any combination of any of the independent and dependent variables previously described. The trained neural network may be installed on the computer to provide analysis of expected changes in the simulation model corresponding to changes in any one or combination of the independent variables.”, Ozgen paragraph [0151] “The present invention further comprises using the neural network to provide a correlation, i.e. a correlation model, to provide a correlation between the dependent and independent variables, wherein the independent variables can be varied using the neural network 108. By varying the particular selection of independent variables and a value therefore, one or more sets of independent variables can be provided which will minimize the history matching error. The correlation model can rapidly analyze, and in one embodiment, graphically display the sensitivity of the history matching error to the entire value range of each independent variable.”, The independent variables and a value are varied, demonstrates that portions of the first data are identified)];
Buchan discloses “wherein the notification indicates the one or more identified portions of the first data” as [Buchan (paragraph [0006] “A telecommunication device can be linked to the expert system to transmit the expert alerts or other reports to a remote user. The access portal can include a graphical user interface to display the expert reports and to input requests to resolve the expert alerts or other reports.”, Buchan paragraph [0007] “An expert system is interfaced with the historical database to generate and transmit expert status reports to a user interface device, and expert trend reports to a user via an access portal.”)];
With respect to claim 6, the combination of Ozgen and Buchan discloses the method of claim 5 above, and Ozgen further discloses “determining one or more modifications to the one or more portions of the first data that would enable the first computer simulation of the first reservoir to be performed to completion using the computer model” as [Ozgen (paragraph [0150] “It is understood that the neural network can be trained using changes in any one or any combination of any of the independent and dependent variables previously described. The trained neural network may be installed on the computer to provide analysis of expected changes in the simulation model corresponding to changes in any one or combination of the independent variables.”, Ozgen paragraph [0151] “The present invention further comprises using the neural network to provide a correlation, i.e. a correlation model, to provide a correlation between the dependent and independent variables, wherein the independent variables can be varied using the neural network 108. By varying the particular selection of independent variables and a value therefore, one or more sets of independent variables can be provided which will minimize the history matching error. The correlation model can rapidly analyze, and in one embodiment, graphically display the sensitivity of the history matching error to the entire value range of each independent variable.”)];
Buchan discloses “wherein the notification indicates the one or more modifications” as [Buchan (paragraph [0006] “A telecommunication device can be linked to the expert system to transmit the expert alerts or other reports to a remote user. The access portal can include a graphical user interface to display the expert reports and to input requests to resolve the expert alerts or other reports.”, Buchan paragraph [0007] “An expert system is interfaced with the historical database to generate and transmit expert status reports to a user interface device, and expert trend reports to a user via an access portal.”)];
With respect to claim 7, the combination of Ozgen and Buchan discloses the method of claim 6 above, and Ozgen further discloses “modifying the first data according to the one or more determined modifications” as [Ozgen (paragraph [0151] “The present invention further comprises using the neural network to provide a correlation, i.e. a correlation model, to provide a correlation between the dependent and independent variables, wherein the independent variables can be varied using the neural network 108. By varying the particular selection of independent variables and a value therefore, one or more sets of independent variables can be provided which will minimize the history matching error. The correlation model can rapidly analyze, and in one embodiment, graphically display the sensitivity of the history matching error to the entire value range of each independent variable.”)];
With respect to claim 8, the combination of Ozgen and Buchan discloses the method of claim 1 above, and Ozgen further discloses “wherein the trained neural network is trained based on a plurality of sets of training data regarding a plurality of additional reservoirs” as [Ozgen (paragraph [0150] “It is understood that the neural network can be trained using changes in any one or any combination of any of the independent and dependent variables previously described. The trained neural network may be installed on the computer to provide analysis of expected changes in the simulation model corresponding to changes in any one or combination of the independent variables.”, Ozgen paragraph [0151] “The present invention further comprises using the neural network to provide a correlation, i.e. a correlation model, to provide a correlation between the dependent and independent variables, wherein the independent variables can be varied using the neural network 108. By varying the particular selection of independent variables and a value therefore, one or more sets of independent variables can be provided which will minimize the history matching error. The correlation model can rapidly analyze, and in one embodiment, graphically display the sensitivity of the history matching error to the entire value range of each independent variable.”)];
“where each of the sets of training data comprises: an indication of a plurality of properties of a respective one of the additional reservoirs” as [Ozgen (paragraph [0079] “In one embodiment, the present invention first receives, processes, and interprets well bore and reservoir data, such as oil, gas, and water production history, pressure information, permeability, porosity, for the construction of a simulation model of the reservoir.”, Ozgen (paragraph [0150] “It is understood that the neural network can be trained using changes in any one or any combination of any of the independent and dependent variables previously described. The trained neural network may be installed on the computer to provide analysis of expected changes in the simulation model corresponding to changes in any one or combination of the independent variables.”)];
“and an indication whether an additional computer simulation of that additional reservoirs was previously performed to completion using the computer model” as [Ozgen (paragraph [0151] “The present invention further comprises using the neural network to provide a correlation, i.e. a correlation model, to provide a correlation between the dependent and independent variables, wherein the independent variables can be varied using the neural network 108. By varying the particular selection of independent variables and a value therefore, one or more sets of independent variables can be provided which will minimize the history matching error. The correlation model can rapidly analyze, and in one embodiment, graphically display the sensitivity of the history matching error to the entire value range of each independent variable.”)];
With respect to claim 9, the combination of Ozgen and Buchan discloses the method of claim 1 above, and Ozgen further discloses “wherein the properties of the first reservoir comprise at least one of: a characteristic of rock at a particular location of the reservoir, a physical geometry of the reservoir at the particular location, “a permeability of the reservoir at the particular location” as [Ozgen (paragraph [0079] “In one embodiment, the present invention first receives, processes, and interprets well bore and reservoir data, such as oil, gas, and water production history, pressure information, permeability, porosity, for the construction of a simulation model of the reservoir.”)], or a characteristics of fluid at the particular location of the reservoir.
With respect to claim 10, the combination of Ozgen and Buchan discloses the method of claim 1 above, and Ozgen further discloses “wherein the first data further indicates one or more characteristics of an industrial process performed at the reservoir” as [Ozgen (paragraph [0079] “In one embodiment, the present invention first receives, processes, and interprets well bore and reservoir data, such as oil, gas, and water production history, pressure information, permeability, porosity, for the construction of a simulation model of the reservoir”, Ozgen paragraph [0143] “In accordance with another aspect of the present invention, once the historical well bore and/or reservoir data and simulation model data have been provided and history matching errors between the historical and theoretical model data have been determined, there is provided a method and system of minimizing the history matching error using a neural network”)];
With respect to claim 11, the combination of Ozgen and Buchan discloses the method of claim 10 above, and Ozgen further discloses “wherein the industrial process is at least one of a well production process or a fluid injection process.” as [Ozgen (paragraph [0012] “constructing a theoretical production output model for the well bore and/or reservoir using the received data”)];
With respect to claim 12, the combination of Ozgen and Buchan discloses the method of claim 1 above, and Ozgen further discloses “wherein the first data further indicates one or more tolerances of the computer simulation” as [Ozgen (paragraph [0142] “Once it is determined that the absolute value of the calculated history matching error between the simulated model and the actual historical data for the well is within an acceptable range or below a predetermined value from Simulation History 14 using History Match Loop 18”)];
With respect to claim 13, the combination of Ozgen and Buchan discloses the method of claim 1 above, and Ozgen further discloses “wherein the first computer simulation simulates a time dependent flow of fluid through the reservoir” as [Ozgen (paragraph [0101] “In constructing a simulation model, the present invention may perform any of the following functions, for example: …. the creation of time dependent data for the simulation results set”)];
With respect to claim 14, the combination of Ozgen and Buchan discloses the method of claim 1 above, and Ozgen further discloses “determining a spatial grid for performing the computer simulation of the reservoir, wherein the spatial grid comprises a plurality of grid blocks, and wherein each of the grid blocks corresponds to a different respective spatial region of the reservoir” as [Ozgen (paragraph [0100] “Once data analysis and loading has been completed the data may be integrated and transferred into a reservoir simulation model using Simulation Model Development 12 with a specific grid and layer system.”, Ozgen [0102] “The present invention may also calculate the intersection of any surface (i.e. unconformity, volcanic intrusions, etc.) with the simulation grid, locate the grid blocks that intersect the surface, determine the direction that the surface intersects through the grid blocks, and create transmissibility traces for the intersecting surface, for example.”)];
“and determining, for each of the grid blocks, a second metric for that grid block, wherein each of the second metrics represents a likelihood that the properties of the first reservoir at a corresponding one of the spatial regions would cause the first computer simulation to terminate prior to completion due to one or more failure conditions.” as [Ozgen (paragraph [0108] “In another embodiment, the method and system of the present invention performs quality and consistency checks on the production and completion data and provides an interface to correct the inconsistencies manually or automatically. The present invention thus enables complex completion histories for fields with large numbers of wells to be quickly handled after data has been imported via spreadsheet or text formats into a database. This corrected information is utilized together with the simulation grid and well markers to create the time dependent data for the simulation data set.”, Ozgen [0109] “Moreover, consistency checks are optionally performed between the production and completion data by scanning through all the production and perforation information and detecting the inconsistencies (production or injection during period where no completions are defined). This functionality is fast and user friendly in the present invention, and also reports inconsistencies and displays production and perforation information graphically for the problem well bores so that the nature of the inconsistency can be visually detected.”)];
With respect to claim 15, the combination of Ozgen and Buchan discloses the method of claim 14 above, and Ozgen further discloses “wherein the one or more failure conditions comprises a convergence failure in performing an iterative process of the first computer simulation.” as [Ozgen (paragraph [0205] “A variant of Genetic Algorithms is used to solve for the global minimum of the desired objective function. Finding the optimized solution requires multiple generations (iterations)”)];
With respect to claim 16, Ozgen discloses “A system” as [Ozgen (paragraph [0009] “In one aspect of the present invention, there is provided a method and software system for utilizing data and associated analysis of history matching error to improve the predicted recovery of reservoir fluids”)];
“one or more processors; and one or more non-transitory computer readable media storing instructions” as [Ozgen (paragraph [0081] “The software may be stored on any medium, typically optical or magnetic, that is capable of retaining the software code and readable by or executed on a general purpose computer system”, Ozgen [0150] “The trained neural network may be installed on the computer to provide analysis of expected changes in the simulation model corresponding to changes in any one or combination of the independent variables”)];
The other limitations of the claim recite the same substantive limitations as claim 1 above, and are rejected using the same teachings.
With respect to claim 17, Ozgen discloses “One or more non-transitory computer readable media storing instructions that, when executed by one or more processors” as [Ozgen (paragraph [0081] “The software may be stored on any medium, typically optical or magnetic, that is capable of retaining the software code and readable by or executed on a general purpose computer system”, Ozgen [0150] “The trained neural network may be installed on the computer to provide analysis of expected changes in the simulation model corresponding to changes in any one or combination of the independent variables”)];
The other limitations of the claim recite the same substantive limitations as claim 1 above, and are rejected using the same teachings.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/BERNARD E COTHRAN/Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188