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公开(公告)号：US20210027144A1
公开(公告)日：2021-01-28
申请号：US16981080
申请日：2018-05-15
Applicant: LANDMARK GRAPHICS CORPORATION
Inventor： Srinath Madasu , Yevgeniy Zagayevskiy , Terry Wong , Dominic Camilleri , Charles Hai Wang , Courtney Leeann Beck , Hanzi Mao , Hui Dong , Harsh Biren Vora
Abstract: Using production data and a production flow record based on the production data, a deep neural network (DNN) is trained to model a proxy flow simulation of a reservoir. The proxy flow simulation of the reservoir is performed, using an ensemble Kalman filter (EnKF), based on the trained DNN. The EnKF assimilates new data through updating a current ensemble to obtain history matching by minimizing a difference between a predicted production output from the proxy flow simulation and measured production data from a field. Using the updated current ensemble, a second proxy flow simulation of the reservoir is performed based on the trained DNN. The assimilating and the performing are repeated while new data is available for assimilating. Predicted behavior of the reservoir is determined based on the proxy flow simulation of the reservoir. An indication of the predicted behavior is provided to facilitate production of fluids from the reservoir.
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2.发明授权公开(公告)号：US10233736B2
公开(公告)日：2019-03-19
申请号：US15117434
申请日：2015-03-12
Applicant: Landmark Graphics Corporation
Inventor： Terry Wong , Graham Fleming
IPC: G06G7/48 , E21B43/16 , E21B43/12 , E21B41/00 , E21B47/12 , E21B47/00 , E21B49/00 , E21B49/08 , G06F17/50
Abstract: System and methods of simulating fluid production in a multi-reservoir system with a common surface network are provided. Black oil data is matched with an equation of state (EOS) model representing different fluid components of each reservoir in the multi-reservoir system. The black oil data is converted into a two-component black oil model for each reservoir, based on the EOS model. Fluid production in the multi-reservoir system is simulated for at least one simulation point in the common surface network, based in part on the two-component black oil model of each reservoir. When fluids produced at the simulation point are determined to be from different reservoirs, properties of the fluids are calculated based on weaved EOS models of the different reservoirs. Otherwise, properties of the fluids are calculated using the two-component black oil model for the reservoir from which the fluids are produced.
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3.发明申请公开(公告)号：US20170091359A1
公开(公告)日：2017-03-30
申请号：US15115607
申请日：2015-03-12
Applicant: Landmark Graphics Corporation
Inventor： Terry Wong , Graham C. Fleming
CPC classification number: G06F17/5018 , E21B41/0092 , E21B43/14 , E21B47/06 , E21B47/065 , E21B47/10 , E21B49/08 , G01F1/74 , G05B17/02 , G06F17/11 , G06F17/16 , G06F2217/16
Abstract: System and methods of simulating fluid production in a multi-reservoir system with a common surface network are provided. Black oil data is matched with a common equation of state (EOS) model for each of a plurality of reservoirs. The black oil data representing fluids within each reservoir. At least one multi-dimensional black oil table representing a mix of the fluid components to be produced from each of the plurality of reservoirs via the common surface network is generated based on the EOS model that matches the one or more black oil tables for each reservoir. Properties of the fluids in the mix during a simulation of fluid production from the plurality of reservoirs are calculated based on the generated multi-dimensional black oil table for each reservoir.
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公开(公告)号：US11927717B2
公开(公告)日：2024-03-12
申请号：US17047152
申请日：2018-05-09
Applicant: Landmark Graphics Corporation
Inventor： Yevgeniy Zagayevskiy , Hanzi Mao , Harsh Biren Vora , Hui Dong , Terry Wong , Dominic Camilleri , Charles Hai Wang , Courtney Leeann Beck
CPC classification number: G01V99/005 , E21B49/00 , G06N20/20 , G06Q10/06 , G06Q50/02 , E21B2200/20 , G01V2210/663 , G01V2210/665 , G06F17/00 , G06Q10/04
Abstract: A method for history matching a reservoir model based on actual production data from the reservoir over time generates an ensemble of reservoir models using geological data representing petrophysical properties of a subterranean reservoir. Production data corresponding to a particular time instance is acquired from the subterranean reservoir. Normal score transformation is performed on the ensemble and on the acquired production data to transform respective original distributions into normal distributions. The generated ensemble is updated based on the transformed acquired production data using an ensemble Kalman filter (EnKF). The updated generated ensemble and the transformed acquired production data are transformed back to respective original distributions. Future reservoir behavior is predicted based on the updated ensemble.
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公开(公告)号：US20210270998A1
公开(公告)日：2021-09-02
申请号：US17260541
申请日：2018-08-30
Applicant: LANDMARK GRAPHICS CORPORATION
Inventor： Srinath Madasu , Keshava Prasad Rangarajan , Terry Wong
Abstract: A history-matched oilfield model that facilitates well system operations for an oilfield is generated using a Bayesian optimization of adjustable parameters based on an entire production history. The Bayesian optimization process includes stochastic modifications to the adjustable parameters based on a prior probability distribution for each parameter and a model error generated using historical production measurement values and corresponding model prediction values for various sets of test parameters.
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Patent Agency Ranking
6.发明申请公开(公告)号：US20220205354A1
公开(公告)日：2022-06-30
申请号：US17136895
申请日：2020-12-29
Applicant: Landmark Graphics Corporation
Inventor： Soumi Chaki , Honggeun Jo , Terry Wong , Yevgeniy Zagayevskiy , Dominic Camilleri
IPC: E21B47/022 , E21B47/12 , G06N3/08 , G01V1/46 , G01V1/48
Abstract: A system is described for estimating well production and injection rates of a subterranean reservoir using machine learning models. The system may include a processor and a non-transitory computer-readable medium comprising instructions that are executable by the processor to cause the processor to perform various operations. The processor may receive a set of static geological data about at least one subterranean reservoir in a subterranean formation. The processor may apply a trained convolutional neural network to the set of static geological data and data on initial states of dynamic reservoir properties to determine dynamic outputs of the subterranean reservoir. The processor may determine well data by extracting the set of static geological data and the dynamic outputs at well trajectories. And, the processor may apply a trained artificial neural network to the well data and subterranean grid information about the subterranean reservoir to generate estimated well production and injection rates.
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公开(公告)号：US10400548B2
公开(公告)日：2019-09-03
申请号：US15116189
申请日：2015-03-12
Applicant: Landmark Graphics Corporation
Inventor： Terry Wong , Graham Fleming
Abstract: System and methods of modeling fluids in a simulation of fluid production in a multi-reservoir system with a common surface network are provided. Pressure-volume-temperature (PVT) data is determined for fluids in each of a plurality of reservoirs coupled to the common surface network. A shared equation of state (EOS) characterization representing each of the fluids across the plurality of reservoirs is generated based on the corresponding PVT data. Data representing properties of the fluids in each reservoir is calculated based on the shared EOS characterization of the fluids. When the calculated data is determined not to match the PVT data associated with the fluids in each reservoir, to the shared EOS characterization is adjusted based on a difference between the calculated data and the PVT data.
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8.发明申请公开(公告)号：US20180018412A1
公开(公告)日：2018-01-18
申请号：US15545865
申请日：2015-09-17
Applicant: Landmark Graphics Corporation
Inventor： Zhiqiang GU , Terry Wong
CPC classification number: G06F17/5009 , E21B43/16 , G01V1/345 , G01V99/005 , G06F2217/16
Abstract: A method includes modeling a fluid flow network, the fluid flow network having a surface pipeline network connected between a plurality of well perforation nodes and a common outlet or inlet. The method also includes generating a plurality of two-phase envelopes for the modeled fluid flow network, where each two-phase envelope has at least some interpolated values and corresponds to a section of the modeled fluid flow network with a constant flow composition. The method also includes determining phase equilibrium information for the modeled fluid flow network based on the generated two-phase envelopes. The method also includes applying the determined phase equilibrium information to production or simulation related to the fluid flow network.
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公开(公告)号：US20170009558A1
公开(公告)日：2017-01-12
申请号：US15116189
申请日：2015-03-12
Applicant: LANDMARK GRAPHICS CORPORATION
Inventor： Terry Wong , Graham Fleming
CPC classification number: E21B41/0092 , E21B43/00 , E21B43/122 , E21B47/06 , E21B47/065 , E21B49/08 , G01N33/2823 , G05B17/02 , G06F17/5009
Abstract: System and methods of modeling fluids in a simulation of fluid production in a multi-reservoir system with a common surface network are provided. Pressure-volume-temperature (PVT) data is determined for fluids in each of a plurality of reservoirs coupled to the common surface network. A shared equation of state (EOS) characterization representing each of the fluids across the plurality of reservoirs is generated based on the corresponding PVT data. Data representing properties of the fluids in each reservoir is calculated based on the shared EOS characterization of the fluids. When the calculated data is determined not to match the PVT data associated with the fluids in each reservoir, to the shared EOS characterization is adjusted based on a difference between the calculated data and the PVT data.
Abstract translation: 提供了在具有共同表面网络的多储层系统中的流体生产仿真中对流体进行建模的系统和方法。 确定耦合到公共表面网络的多个储存器中的每一个中的流体的压力 - 体积 - 温度（PVT）数据。 基于对应的PVT数据生成代表多个储层中的每个流体的共同方程（EOS）表征。 基于流体的共同EOS表征计算表示每个储层中流体性质的数据。 当计算的数据被确定为不匹配与每个储存器中的流体相关联的PVT数据时，基于计算的数据和PVT数据之间的差异来调整共享的EOS表征。
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公开(公告)号：US12050981B2
公开(公告)日：2024-07-30
申请号：US16981080
申请日：2018-05-15
Applicant: LANDMARK GRAPHICS CORPORATION
Inventor： Srinath Madasu , Yevgeniy Zagayevskiy , Terry Wong , Dominic Camilleri , Charles Hai Wang , Courtney Leeann Beck , Hanzi Mao , Hui Dong , Harsh Biren Vora
Abstract: Using production data and a production flow record based on the production data, a deep neural network (DNN) is trained to model a proxy flow simulation of a reservoir. The proxy flow simulation of the reservoir is performed, using an ensemble Kalman filter (EnKF), based on the trained DNN. The EnKF assimilates new data through updating a current ensemble to obtain history matching by minimizing a difference between a predicted production output from the proxy flow simulation and measured production data from a field. Using the updated current ensemble, a second proxy flow simulation of the reservoir is performed based on the trained DNN. The assimilating and the performing are repeated while new data is available for assimilating. Predicted behavior of the reservoir is determined based on the proxy flow simulation of the reservoir. An indication of the predicted behavior is provided to facilitate production of fluids from the reservoir.
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