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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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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：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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