DETAILED ACTION
The office action is responsive to an application filed on 11/22/23 and is being examined under the first inventor to file provisions of the AIA . Claims 1-20 are pending.
Claim Interpretation
The limitations recited in the independent claims do not include any language that falls
within Step 2A, Prong 1. Accordingly, the claims are not subject to a rejection under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the
basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-8 and 11-19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zafari et al. (U.S. PGPub 2010/0274546) (from IDS dated 4/25/25).
With respect to claim 1, Zafari et al. discloses “A method” as [Zafari et al. (paragraph [0005] “In one aspect, embodiments disclosed herein relate to a method of planning a procedure for cleaning a wellbore by injecting a cleaning fluid from a reservoir into the wellbore”)];
“receiving one or more sets of parameters related to an operation corresponding to a wellbore” as [Zafari et al. (paragraph [0020] “Referring now to FIG. 2, a block diagram of a cleanup simulation system 90 and control unit 85 is shown. The control unit 85 processes information received from the sensor units 50, 51, 52, and converts the information into appropriate data sets for the simulations by the cleanup simulation system 90. The cleanup simulation system 90 receives the data sets from the control unit 85, and performs simulations relating to property changes of the fluid in the wellbore 101 during the cleaning operation.”, Zafari et al. (paragraph [0021] “The data sets prepared in the control unit 85 are categorized into four groups comprising reservoir data, wellbore data, fluid loss data, and a fluid loss profile. The reservoir data represents parameters related to properties of the fluid existing in the reservoir at the initial stage of the cleaning operation, and properties of the reservoir itself. The reservoir data includes, for example, reservoir geometry, rock and fluid properties, initial state of the reservoir at the time of simulation, and the like.”)];
“at a first time, simulating the operation using the one or more sets of parameters; recording pertinent results from the simulation” as [Zafari et al. (paragraph [0033] “At Step 102, using the data sets received from the control unit 85, the cleanup simulation system 90 performs the simulations to find the most preferable parameter settings for the cleaning operation from the perspective of cost efficiency, time efficiency, and the like.”, Fig. 3)];
“and storing results comprising a constraint on the operation in a table” as [Zafari et al. (paragraph [0029] “Integration section 94 integrates simulation results from all of the simulation models by the simulator 91, 92, 93. The integration result by the section 94 may include a time profile in concentration of the cleaning fluid inside the wellbore during the cleaning operation with the most preferable parameter settings from the perspective of cost efficiency, time efficiency, and the like.”, Fig. 3, The examiner considers the time profile to be the table that the results are stored in, since the time profile has the results of the different simulators)];
“at a second time, retrieving the constraint from the table for performance of the operation” as [Zafari et al. (paragraph [0034] “At Step 103, the cleanup simulation system 90 evaluates whether the parameter settings obtained at Step 102 satisfy prescribed constraints. If the evaluation result is positive, at Step 104, the cleanup simulation system 90 constructs a cleaning operation program based on the simulation model with the most preferable parameter settings, which were obtained at Step 102. If the evaluation result is negative, by returning Step 102, the cleanup simulation system 90 re-performs the simulation model with alternative parameter settings so that the simulation result satisfies the constraints. The constraints may be categorized into completion constraints and production constraints.”)];
“and using the constraint to control the operation.” as [Zafari et al. (paragraph [0034] “At Step 103, the cleanup simulation system 90 evaluates whether the parameter settings obtained at Step 102 satisfy prescribed constraints. If the evaluation result is positive, at Step 104, the cleanup simulation system 90 constructs a cleaning operation program based on the simulation model with the most preferable parameter settings, which were obtained at Step 102. If the evaluation result is negative, by returning Step 102, the cleanup simulation system 90 re-performs the simulation model with alternative parameter settings so that the simulation result satisfies the constraints. The constraints may be categorized into completion constraints and production constraints. Regarding the completion constraints, the maximum drawdown needed to produce from the formation will be known.”, Zafari et al. paragraph [0035] “Regarding the production constraints, the minimum bottom hole pressure to lift the cushion fluid to the surface will be known. This leads to whether there is a need for any artificial lift systems or whether the well will flow naturally. The minimum flow rates that can prevent the cushion fluids from slipping back down to the bottom hole are calculated during the simulation.”, Zafari et al. paragraph [0036] “Finally, at Step 105, the control unit 85 conducts the cleaning operation of the wellbore 101 by running the cleaning operation program.”)];
With respect to claim 2, Zafari et al. discloses “wherein the operation comprises a cleanup operation of the wellbore.” as [Zafari et al. (paragraph [0006] “In one aspect, embodiments disclosed herein relate a system for cleaning a wellbore by injecting a cleaning fluid from a reservoir into the wellbore such that the cleaning fluid circulates between the reservoir and the wellbore comprising: a sensor unit that detects properties and conditions of fluids circulating between the reservoir and the wellbore; a control unit that prepares a data set from the detected properties and conditions of the fluids circulating between the reservoir and the wellbore; and a cleanup simulation system that performs: simulating a cleaning operation model of injecting the cleaning fluid into the wellbore based on the data set; determining parameter settings of the simulated cleaning operation model that satisfy prescribed constraints; and producing the procedure for cleaning the wellbore based on the determined parameters.”)];
With respect to claim 3, Zafari et al. discloses “wherein the one or more sets of parameters comprise real-world conditions of the wellbore.” as [Zafari et al. (paragraph [0021] “The data sets prepared in the control unit 85 are categorized into four groups comprising reservoir data, wellbore data, fluid loss data, and a fluid loss profile. The reservoir data represents parameters related to properties of the fluid existing in the reservoir at the initial stage of the cleaning operation, and properties of the reservoir itself. The reservoir data includes, for example, reservoir geometry, rock and fluid properties, initial state of the reservoir at the time of simulation, and the like.”, Zafari et al. paragraph [0022] “The wellbore data represents parameters related to the geometry and trajectory of the wellbore, and characteristics and locations of devices inside the wellbore, such as the drill string assembly 30, and devices on the surface, such as the supply line 43 and the return line 44.”, Zafari et al. paragraph [0023] “The fluid loss data mostly includes volumetric historical information collected during the drilling and completion period, which relates to the amount of fluid lost into the formation.”, Zafari et al. paragraph [0024] “The fluid loss profile includes measurements of fluid loss along the wellbore. The fluid loss profile can be obtained by direct measurements inside the wellbore. These measurements can be conducted, for example, by obtaining resistivity logs, which express the radius of the invaded zone around the wellbore.”)];
With respect to claim 4, Zafari et al. discloses “wherein the one or more sets of parameters comprise constraints on the operation corresponding to the wellbore.” as [Zafari et al. (paragraph [0034] “At Step 103, the cleanup simulation system 90 evaluates whether the parameter settings obtained at Step 102 satisfy prescribed constraints. If the evaluation result is positive, at Step 104, the cleanup simulation system 90 constructs a cleaning operation program based on the simulation model with the most preferable parameter settings, which were obtained at Step 102. If the evaluation result is negative, by returning Step 102, the cleanup simulation system 90 re-performs the simulation model with alternative parameter settings so that the simulation result satisfies the constraints. The constraints may be categorized into completion constraints and production constraints. Regarding the completion constraints, the maximum drawdown needed to produce from the formation will be known.”)];
With respect to claim 5, Zafari et al. discloses “wherein simulating the operation using the one or more sets of parameters comprises simulating the operation using a plurality of coupled simulators.” as [Zafari et al. (paragraph [0025] “The cleanup simulation system 90 includes a porous media multiphase flow simulator 91, a wellbore multiphase simulator 92, and a process component simulator 93, and an integration section 94. The simulator 91, 92, or 93 stores feasible models for simulating dynamic states of fluids with simulation objects.”, Fig. 2)];
With respect to claim 6, Zafari et al. discloses “wherein the plurality of coupled simulators comprises a commercial process facility simulator.” as [Zafari et al. (paragraph [0028] “The process component simulator 93 is designed to simulate how process components throughout the flowline influence the pressure and temperature in the fluid circulation system 40. The process components include, for example, the supply line 43, return line 44, the choke valve 83, the fluid pump 41, and any other components of the fluid circulation system 40, which may significantly influence the pressure and/or temperature thereof.”, The examiner considers the process component simulator to be the commercial process facility simulator, since the process component simulator simulates how process components throughout the flowline influence the pressure and temperature in the fluid circulation system and the commercial process facility simulator simulates and controls the operation of an oilfield, see paragraph [0048] of the specification)];
With respect to claim 7, Zafari et al. discloses “wherein the plurality of coupled simulators comprises a wellbore clean-up simulator.” as [Zafari et al. (paragraph [0026] “For example, the porous media multiphase flow simulator 91 is designed to simulate a multiphase fluid flow in porous media. Specifically, the simulator 91 simulates the interaction between the foreign fluid (the fluid flowing into the reservoir through the return line 40) and the fluid in the reservoir 42.”, Zafari et al. paragraph [0029] “Integration section 94 integrates simulation results from all of the simulation models by the simulator 91, 92, 93. The integration result by the section 94 may include a time profile in concentration of the cleaning fluid inside the wellbore during the cleaning operation with the most preferable parameter settings from the perspective of cost efficiency, time efficiency, and the like.”, The porous media multiphase flow simulator simulates the interaction between the foreign fluid (the fluid flowing into the reservoir through the return line 40) and the fluid in the reservoir. The examiner considers the fluid flowing into the reservoir to be the cleaning fluid, since it is injected into the reservoir and interacts with the fluid already in the reservoir.)];
With respect to claim 8, Zafari et al. discloses “wherein the constraint comprises a drawdown pressure at a formation or erosional velocity.” as [Zafari et al. (paragraph [0034] “At Step 103, the cleanup simulation system 90 evaluates whether the parameter settings obtained at Step 102 satisfy prescribed constraints. If the evaluation result is positive, at Step 104, the cleanup simulation system 90 constructs a cleaning operation program based on the simulation model with the most preferable parameter settings, which were obtained at Step 102. If the evaluation result is negative, by returning Step 102, the cleanup simulation system 90 re-performs the simulation model with alternative parameter settings so that the simulation result satisfies the constraints. The constraints may be categorized into completion constraints and production constraints. Regarding the completion constraints, the maximum drawdown needed to produce from the formation will be known. The maximum drawdown is governed by the maximum drawdown that the downhole completion can handle. For example, in the case of a gravel-packed completion, there is a maximum drawdown that one can apply in the bottom hole. That is, there is a maximum drawdown that can be applied on the formation to prevent collapsing the well.”)];
With respect to claim 11, Zafari et al. discloses “wherein the constraint comprises facility capacity for handling produced gas during a cleanup operation, facility capacity for handling produced oil during the cleanup operation, facility capacity for handling produced water during the cleanup operation, specified limits for combined fluids, maximum solids as mass or volumetric flow, or a combination thereof.” as [Zafari et al. (paragraph [0034] “At Step 103, the cleanup simulation system 90 evaluates whether the parameter settings obtained at Step 102 satisfy prescribed constraints. If the evaluation result is positive, at Step 104, the cleanup simulation system 90 constructs a cleaning operation program based on the simulation model with the most preferable parameter settings, which were obtained at Step 102. If the evaluation result is negative, by returning Step 102, the cleanup simulation system 90 re-performs the simulation model with alternative parameter settings so that the simulation result satisfies the constraints. The constraints may be categorized into completion constraints and production constraints. Regarding the completion constraints, the maximum drawdown needed to produce from the formation will be known. The maximum drawdown is governed by the maximum drawdown that the downhole completion can handle. For example, in the case of a gravel-packed completion, there is a maximum drawdown that one can apply in the bottom hole. That is, there is a maximum drawdown that can be applied on the formation to prevent collapsing the well.”, With there being a maximum drawdown regarding the completion constraint, demonstrates that the constraint comprises facility capacity for handling produced gas during a cleanup operation, facility capacity for handling produced oil during the cleanup operation, facility capacity for handling produced water during the cleanup operation, since the maximum drawdown is the differential pressure that drives fluids from the reservoirs into the wellbore. The maximum drawdown shows that there’s capacity for handling produced gas, oil and water)];
With respect to claim 12, Zafari et al. discloses “A system” as [Zafari et al. (paragraph [0006] “In one aspect, embodiments disclosed herein relate a system for cleaning a wellbore by injecting a cleaning fluid from a reservoir into the wellbore such that the cleaning fluid circulates between the reservoir and the wellbore”)];
“one or more memory devices storing instructions” as [Zafari et al. (paragraph [0018] “The control unit 85 includes, for example, a central processing unit (CPU), a read only memory (ROM), a random access memory (RAM), input/output ports, memory, and the like.”)];
“and one or more processors configured to execute the instructions” as [Zafari et al. (paragraph [0018] “The control unit 85 includes, for example, a central processing unit (CPU), a read only memory (ROM), a random access memory (RAM), input/output ports, memory, and the like.”)];
“receive one or more sets of parameters related to an operation corresponding to a commercial process facility” as [Zafari et al. (paragraph [0028] “The process component simulator 93 is designed to simulate how process components throughout the flowline influence the pressure and temperature in the fluid circulation system 40. The process components include, for example, the supply line 43, return line 44, the choke valve 83, the fluid pump 41, and any other components of the fluid circulation system 40, which may significantly influence the pressure and/or temperature thereof.”, Zafari et al. paragraph [0033] “At Step 102, using the data sets received from the control unit 85, the cleanup simulation system 90 performs the simulations to find the most preferable parameter settings for the cleaning operation from the perspective of cost efficiency, time efficiency, and the like.”, Fig. 3)];
“simulate the operation using a plurality of simulators and the one or more sets of parameters before performing the operation” as [Zafari et al. (paragraph [0025] “The cleanup simulation system 90 includes a porous media multiphase flow simulator 91, a wellbore multiphase simulator 92, and a process component simulator 93, and an integration section 94. The simulator 91, 92, or 93 stores feasible models for simulating dynamic states of fluids with simulation objects.”, Zafari et al. paragraphs [0026] – [0028] “For example, the porous media multiphase flow simulator 91 is designed to simulate a multiphase fluid flow in porous media. Specifically, the simulator 91 simulates the interaction between the foreign fluid (the fluid flowing into the reservoir through the return line 40) and the fluid in the reservoir 42…..The wellbore multiphase simulator 92 is designed to simulate a multiphase flow of the fluids in a wellbore. The wellbore and the porous media are modeled interactively to represent the fluid flow from the reservoir to the wellbore and from the wellbore into the reservoir at all times……The process component simulator 93 is designed to simulate how process components throughout the flowline influence the pressure and temperature in the fluid circulation system 40.”, Zafari et al. paragraph [0033] “At Step 102, using the data sets received from the control unit 85, the cleanup simulation system 90 performs the simulations to find the most preferable parameter settings for the cleaning operation from the perspective of cost efficiency, time efficiency, and the like.”];
“wherein the plurality of simulators comprise a commercial process facility simulator and a wellbore clean-up simulator” as [Zafari et al. (paragraph [0028] “The process component simulator 93 is designed to simulate how process components throughout the flowline influence the pressure and temperature in the fluid circulation system 40. The process components include, for example, the supply line 43, return line 44, the choke valve 83, the fluid pump 41, and any other components of the fluid circulation system 40, which may significantly influence the pressure and/or temperature thereof.”, Zafari et al. (paragraph [0026] “For example, the porous media multiphase flow simulator 91 is designed to simulate a multiphase fluid flow in porous media. Specifically, the simulator 91 simulates the interaction between the foreign fluid (the fluid flowing into the reservoir through the return line 40) and the fluid in the reservoir 42.”, Zafari et al. paragraph [0029] “Integration section 94 integrates simulation results from all of the simulation models by the simulator 91, 92, 93. The integration result by the section 94 may include a time profile in concentration of the cleaning fluid inside the wellbore during the cleaning operation with the most preferable parameter settings from the perspective of cost efficiency, time efficiency, and the like.”, The examiner considers the process component simulator to be the commercial process facility simulator, since the process component simulator simulates how process components throughout the flowline influence the pressure and temperature in the fluid circulation system and the commercial process facility simulator simulates and controls the operation of an oilfield, see paragraph [0048] of the specification. The porous media multiphase flow simulator simulates the interaction between the foreign fluid (the fluid flowing into the reservoir through the return line 40) and the fluid in the reservoir. The examiner considers the fluid flowing into the reservoir to be the cleaning fluid, since it is injected into the reservoir and interacts with the fluid already in the reservoir)];
“record pertinent results from the simulation in one or more tables” as [Zafari et al. paragraph [0029] “Integration section 94 integrates simulation results from all of the simulation models by the simulator 91, 92, 93. The integration result by the section 94 may include a time profile in concentration of the cleaning fluid inside the wellbore during the cleaning operation with the most preferable parameter settings from the perspective of cost efficiency, time efficiency, and the like.”, Zafari et al. (paragraph [0030] “The cleanup simulation system 90 displays the simulation results from the simulators 91, 92, 93, and the integration result from the integration section 94 for job monitoring purposes, and prints the results as documents to be used for scenario selection and optimization algorithms.”, The examiner considers the time profile to be the table that the results are stored in, since the time profile has the results of the different simulators)];
“wherein the pertinent results comprise control parameters that determine whether a constraint is satisfied” as [Zafari et al. (paragraph [0034] “At Step 103, the cleanup simulation system 90 evaluates whether the parameter settings obtained at Step 102 satisfy prescribed constraints. If the evaluation result is positive, at Step 104, the cleanup simulation system 90 constructs a cleaning operation program based on the simulation model with the most preferable parameter settings, which were obtained at Step 102. If the evaluation result is negative, by returning Step 102, the cleanup simulation system 90 re-performs the simulation model with alternative parameter settings so that the simulation result satisfies the constraints. The constraints may be categorized into completion constraints and production constraints.”)];
“during the operation, control the operation using the control parameters based at least in part on the constraint.” as [Zafari et al. (paragraph [0034] “At Step 103, the cleanup simulation system 90 evaluates whether the parameter settings obtained at Step 102 satisfy prescribed constraints. If the evaluation result is positive, at Step 104, the cleanup simulation system 90 constructs a cleaning operation program based on the simulation model with the most preferable parameter settings, which were obtained at Step 102. If the evaluation result is negative, by returning Step 102, the cleanup simulation system 90 re-performs the simulation model with alternative parameter settings so that the simulation result satisfies the constraints. The constraints may be categorized into completion constraints and production constraints.”, Zafari et al. paragraph [0035] “Regarding the production constraints, the minimum bottom hole pressure to lift the cushion fluid to the surface will be known. This leads to whether there is a need for any artificial lift systems or whether the well will flow naturally. The minimum flow rates that can prevent the cushion fluids from slipping back down to the bottom hole are calculated during the simulation.”, Zafari et al. paragraph [0036] “Finally, at Step 105, the control unit 85 conducts the cleaning operation of the wellbore 101 by running the cleaning operation program.”)];
With respect to claim 13, Zafari et al. discloses “wherein controlling the operation comprises changing at least one property of the commercial process facility.” as [Zafari et al. (paragraph [0020] “The control unit 85 processes information received from the sensor units 50, 51, 52, and converts the information into appropriate data sets for the simulations by the cleanup simulation system 90. The cleanup simulation system 90 receives the data sets from the control unit 85, and performs simulations relating to property changes of the fluid in the wellbore 101 during the cleaning operation.”)];
With respect to claim 14, Zafari et al. discloses “wherein the at least one property comprises an orifice size of a variable choke, a pump speed or pressure, or opening or closing a valve.” as [Zafari et al. (paragraph [0028] “The process component simulator 93 is designed to simulate how process components throughout the flowline influence the pressure and temperature in the fluid circulation system 40. The process components include, for example, the supply line 43, return line 44, the choke valve 83, the fluid pump 41, and any other components of the fluid circulation system 40, which may significantly influence the pressure and/or temperature thereof.”, Zafari et al. paragraph [0029] “ The set parameters may include a pumping rate of the fluid pump 41, opening degree of the choke valve 83 during the cleaning operation, concentration of the cleaning fluid reserved in the reservoir 42 at the initial stage of the cleaning operation, and the like.”)];
With respect to claim 15, Zafari et al. discloses “wherein the instructions are configured to cause the one or more processors to history match measurement data to generate a proxy of the wellbore clean-up simulator.” as [Zafari et al. (paragraph [0043] “Second, based on the feedback from the measurements during the cleaning operation, the behavior of the wellbore can be checked against the simulation results and the closest scenario is used for the remainder of the process.”, Zafari et al. paragraph [0044] “At Step 302 of the process, during the operation, observation data are acquired, for example, based on downhole and surface measurements of various parameters, such as rates, cuts, pressures, and Pit Volume Totalizer (PVT), etc. At operationally feasible time intervals, the measurement results are used to update the input parameters used in the present job design (Step 303). For example, if the observed value of fluid density is different from the value applied to the previous simulation, the observed value will be applied to the next simulation as an updated value for the parameter.”)];
With respect to claim 16, Zafari et al. discloses “wherein the instructions are configured to cause the one or more processors to use the proxy of the wellbore clean-up simulator to estimate constraints during the operation.” as [Zafari et al. (paragraph [0025] “The cleanup simulation system 90 includes a porous media multiphase flow simulator 91, a wellbore multiphase simulator 92, and a process component simulator 93, and an integration section 94. The simulator 91, 92, or 93 stores feasible models for simulating dynamic states of fluids with simulation objects. Each model is a function, in which a dependent variable (the simulation object) is a fluid property (for example, the density of the cleaning fluid) and independent variables are various factors that influence dynamic states of the fluid during the cleaning operation.”, Zafari et al. (paragraph [0034] “At Step 103, the cleanup simulation system 90 evaluates whether the parameter settings obtained at Step 102 satisfy prescribed constraints. If the evaluation result is positive, at Step 104, the cleanup simulation system 90 constructs a cleaning operation program based on the simulation model with the most preferable parameter settings, which were obtained at Step 102. If the evaluation result is negative, by returning Step 102, the cleanup simulation system 90 re-performs the simulation model with alternative parameter settings so that the simulation result satisfies the constraints. The constraints may be categorized into completion constraints and production constraints.”)];
With respect to claim 17, Zafari et al. discloses “wherein simulating the operation comprises transmitting at least some parameters from the wellbore clean-up simulator to the commercial process facility simulator.” as [Zafari et al. (paragraph [0026] “The dependent variables of the function in the simulator 91 are dynamic states of the two fluids in the reservoir, and the independent variables are temperature, pressure, and the like in the reservoir 42. Accordingly, the simulator 91 is able to model the fluid flow in different pressure and temperature conditions.”, Zafari et al. paragraph [0028] “The process component simulator 93 is designed to simulate how process components throughout the flowline influence the pressure and temperature in the fluid circulation system 40. The process components include, for example, the supply line 43, return line 44, the choke valve 83, the fluid pump 41, and any other components of the fluid circulation system 40, which may significantly influence the pressure and/or temperature thereof.”, Fig. 2, The porous media multiphase flow simulator 91 and the process component simulator 93 are connected where the pressure and temperature are analyzed as shown in Fig. 2 of the Zafari et al. reference.)];
With respect to claim 18, Zafari et al. discloses “A system” as [Zafari et al. (paragraph [0006] “In one aspect, embodiments disclosed herein relate a system for cleaning a wellbore by injecting a cleaning fluid from a reservoir into the wellbore such that the cleaning fluid circulates between the reservoir and the wellbore”)];
The other limitations of the claim recite the same substantive limitations as claim 12 above and are rejected using the same teachings.
With respect to claim 19, Zafari et al. discloses “wherein the instructions are configured to cause the one or more processors to: determine that the constraint is not satisfied during the operation or the simulation” as [Zafari et al. (paragraph [0034] “At Step 103, the cleanup simulation system 90 evaluates whether the parameter settings obtained at Step 102 satisfy prescribed constraints. If the evaluation result is positive, at Step 104, the cleanup simulation system 90 constructs a cleaning operation program based on the simulation model with the most preferable parameter settings, which were obtained at Step 102. If the evaluation result is negative, by returning Step 102, the cleanup simulation system 90 re-performs the simulation model with alternative parameter settings so that the simulation result satisfies the constraints. The constraints may be categorized into completion constraints and production constraints.”)];
“and pause, delay, or shutdown the operation in response to determining that the constraint is not satisfied.” as [Zafari et al. (paragraph [0034] “At Step 103, the cleanup simulation system 90 evaluates whether the parameter settings obtained at Step 102 satisfy prescribed constraints. If the evaluation result is positive, at Step 104, the cleanup simulation system 90 constructs a cleaning operation program based on the simulation model with the most preferable parameter settings, which were obtained at Step 102. If the evaluation result is negative, by returning Step 102, the cleanup simulation system 90 re-performs the simulation model with alternative parameter settings so that the simulation result satisfies the constraints. The constraints may be categorized into completion constraints and production constraints.”, Fig. 3, The operation is delayed by returning to step 102 to repeat the process)];
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness
rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 9-10 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Zafari et al. (U.S. PGPub 2010/0274546) (from IDS dated 4/25/25) in view of Couet et al. (U.S. PGPub 2011/027059).
With respect to claim 9, Zafari et al. discloses the method of claim 1 above.
While Zafari et al. teaches retrieving a constraint from a table for performance of the operation and using the constraint to control the operation, Zafari et al. does not explicitly disclose “at the second time, computing an additional constraint using at least one simulator used to perform the simulation at the first time, wherein the additional constraint is less compute intensive than the constraint.”
Couet et al. discloses “at the second time, computing an additional constraint using at least one simulator used to perform the simulation at the first time, wherein the additional constraint is less compute intensive than the constraint.” as [Couet et al. (paragraph [0047] “In one or more embodiments, the approximation scheme may be a neural network ("NN") approximation scheme or an RBF approximation scheme. An example RBF approximation is described below with respect to FIG. 4. In the case of the NN approximation scheme, the NN may include an output for each modeled quantity of the simulation. In element 306, constraint proxy model(s) are generated for each of the expensive constraint(s) in the optimization problem using the same approximation scheme described above. Note that the generation of the expensive constraint(s) implies that the nonlinear constraint values are also gathered at each of the initial samples.”)];
Zafari et al. and Couet et al. are analogous art because they are from the same field endeavor of analyzing the fluid within 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 Zafari et al. of retrieving a constraint from a table for performance of the operation and using the constraint to control the operation by incorporating at the second time, computing an additional constraint using at least one simulator used to perform the simulation at the first time, wherein the additional constraint is less compute intensive than the constraint as taught by Couet et al. for the purpose of optimizing expensive functions with expensive nonlinear constraints.
Zafari et al. in view of Couet et al. teaches at the second time, computing an additional constraint using at least one simulator used to perform the simulation at the first time, wherein the additional constraint is less compute intensive than the constraint.
The motivation for doing so would have been because Couet et al. teaches that by optimizing expensive functions with expensive nonlinear constraints, the ability to identify an optimal solution of a function proxy model and a constraint proxy model can be accomplished. This allows for the evaluation of the objective functions to be less time consuming (Couet et al. paragraphs [0002] – [0003] “Typically, real-world optimization problems include objective functions that are based on the output of one or more simulation models. In this case, the underlying processes of the problems may be time and computation intensive, and the objective function is deemed expensive (i.e., central processing unit intensive) to evaluate, etc.”).
With respect to claim 10, the combination of Zafari et al. and Couet et al. discloses the method of claim 9 above, and Couet et al. further discloses “wherein the at least one simulator is able to simulate the additional constraint from real world measurements” as [Couet et al. (paragraph [0046] “ Returning to the method of FIG. 3, in element 302, sample data is selected for initial evaluation of an expensive simulation-based function……In another example, the sample data may be based on N+2 samples, or some other sampling scheme and number thereof. The sample points may be oilfield data (210) as discussed above with respect to FIG. 2. In one or more embodiments, the expensive function may be evaluated at each sample point in the sample data.”, Couet et al. paragraph [0047] “In one or more embodiments, the approximation scheme may be a neural network ("NN") approximation scheme or an RBF approximation scheme. An example RBF approximation is described below with respect to FIG. 4. In the case of the NN approximation scheme, the NN may include an output for each modeled quantity of the simulation. In element 306, constraint proxy model(s) are generated for each of the expensive constraint(s) in the optimization problem using the same approximation scheme described above. Note that the generation of the expensive constraint(s) implies that the nonlinear constraint values are also gathered at each of the initial samples.”, Fig. 3)];
“and the at least one simulator is not able to simulate the constraint without first performing other simulations on the real world measurements.” as [Couet et al. (paragraph [0031] “In one or more embodiments, the simulation tool (220) includes simulation module(s) (222) configured to perform simulations of reservoirs and/or field operations as discussed above with respect to FIG. 1. More specifically, the simulation module(s) (222) may be configured to optimize a problem (e.g., minimize or maximize an objective function given a set of tuneable parameters, the control variables).”, Couet et al. paragraph [0032] “In one or more embodiments, the simulation tool (220) includes a approximation module (223) configured to generate proxy models of expensive functions and nonlinear constraints. The approximation module (223) may generate proxy models using either a neural network ("NN") or radial basis function ("RBF") approximation.”)];
With respect to claim 20, Zafari et al. discloses the system of claim 17 above.
While Zafari et al. teaches retrieving a constraint from a table for performance of the operation and using the constraint to control the operation, Zafari et al. does not explicitly disclose “wherein the instructions are configured to cause the one or more processors to generate a proxy of the wellbore clean-up simulator using neural networking.”
Couet et al. discloses “wherein the instructions are configured to cause the one or more processors to generate a proxy of the wellbore clean-up simulator using neural networking.” as [Couet et al. (paragraph [0032] “In one or more embodiments, the simulation tool (220) includes a approximation module (223) configured to generate proxy models of expensive functions and nonlinear constraints. The approximation module (223) may generate proxy models using either a neural network ("NN") or radial basis function ("RBF") approximation.”)];
Zafari et al. and Couet et al. are analogous art because they are from the same field endeavor of analyzing the fluid within 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 Zafari et al. of retrieving a constraint from a table for performance of the operation and using the constraint to control the operation by incorporating wherein the instructions are configured to cause the one or more processors to generate a proxy of the wellbore clean-up simulator using neural networking as taught by Couet et al. for the purpose of optimizing expensive functions with expensive nonlinear constraints.
Zafari et al. in view of Couet et al. teaches wherein the instructions are configured to cause the one or more processors to generate a proxy of the wellbore clean-up simulator using neural networking.
The motivation for doing so would have been because Couet et al. teaches that by optimizing expensive functions with expensive nonlinear constraints, the ability to identify an optimal solution of a function proxy model and a constraint proxy model can be accomplished. This allows for the evaluation of the objective functions to be less time consuming (Couet et al. paragraphs [0002] – [0003] “Typically, real-world optimization problems include objective functions that are based on the output of one or more simulation models. In this case, the underlying processes of the problems may be time and computation intensive, and the objective function is deemed expensive (i.e., central processing unit intensive) to evaluate, etc.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The relevance of online reference A Recurrent Neural Network–Based Proxy Model for Well-Control Optimization with Nonlinear Output Constraints, written by Kim et al. is developing a recurrent neural network (RNN)–based proxy model to treat constrained production optimization problems.
The relevance of online reference Integrated Approach to Simulation of Near-Wellbore and Wellbore Cleanup, written by Theuveny et al. is investigating the integration of transient simulation of near wellbore multiphase phenomena with complex wellbore dynamics and provides recommendation on cleanup designs.
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/BERNARD E COTHRAN/Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
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