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11.发明授权公开(公告)号：US11493664B2
公开(公告)日：2022-11-08
申请号：US16640300
申请日：2019-03-04
Applicant: LANDMARK GRAPHICS CORPORATION
Inventor： Srinath Madasu , Hanife Meftun Erdogan , Keshava Prasad Rangarajan
Abstract: A system for determining completion parameters for a wellbore includes a sensor and a computing device. The sensor can be positioned at a surface of a wellbore to detect data prior to finishing a completion stage for the wellbore. The computing device can receive the data, perform a history match for simulation and production using the sensor data and historical data, generate inferred data for completion parameters using the historical data identified during the history match, predict stimulated area and production by inputting the inferred data into a neural network model, determine completion parameters for the wellbore using Bayesian optimization on the stimulated area and production from the neural network model, profit maximization, and output the completion parameters for determining completion decisions for the wellbore.
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公开(公告)号：US20210222688A1
公开(公告)日：2021-07-22
申请号：US16763432
申请日：2019-01-31
Applicant: Landmark Graphics Corporation
Inventor： Srinath Madasu , Keshava Prasad Rangarajan
Abstract: The disclosed embodiments include pump systems and methods to improve pump load predictions of pumps. The method includes determining, in a neural network, a pump load of a wellbore pump based on a physics based model of the pump load of the wellbore pump. The method also includes obtaining a measured pump load of the wellbore pump. After initiation of a pump cycle of the wellbore pump, the method further includes predicting a pump load of the wellbore pump based on the physics based model, performing a Bayesian Optimization to reduce a difference between a predicted pump load and the measured pump load to less than a threshold value, and improving a prediction of the pump load based on the Bayesian Optimization.
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公开(公告)号：US20210201160A1
公开(公告)日：2021-07-01
申请号：US16954446
申请日：2019-12-16
Applicant: Landmark Graphics Corporation
Inventor： Srinath Madasu , Keshava Prasad Rangarajan
Abstract: A physics-influenced deep neural network (PDNN) model, or a deep neural network incorporating a physics-based cost function, can be used to efficiently denoise sensor data. To generate the PDNN model, noisy sensor data is used as training data input to a deep neural network and training output is valuated with a cost function that incorporates a physics-based model. An autoencoder can be coupled to the PDNN model and trained with the reduced-noise sensor data which is output from the PDNN during training of the PDNN or with a separate set of sensor data. The autoencoder detects outliers based on the reconstructed reduced-noise sensor data which it generates. Denoising sensor data by leveraging an autoencoder which is influenced by the physics of the underlying domain based on the incorporation of the physics-based model in the PDNN facilitates accurate and efficient denoising of sensor data and detection of outliers.
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14.发明申请公开(公告)号：US20210148213A1
公开(公告)日：2021-05-20
申请号：US16616795
申请日：2017-11-15
Applicant: Landmark Graphics Corporation
Inventor： Srinath Madasu , Keshava Prasad Rangarajan , Nishant Raizada
Abstract: System and methods for optimizing parameters for drilling operations are provided. Real-time data including values for input variables associated with a current stage of a drilling operation along a planned well path are acquired. A neural network model is trained to produce an objective function defining a response value for at least one operating variable of the drilling operation. The response value for the operating variable is estimated based on the objective function produced by the trained neural network model. Stochastic optimization is applied to the estimated response value so as to produce an optimized response value for the operating variable. Values of controllable parameters are estimated for a subsequent stage of the drilling operation, based on the optimized response value of the operating variable. The subsequent stage of the drilling operation is performed based on the estimated values of the controllable parameters.
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公开(公告)号：US20210056447A1
公开(公告)日：2021-02-25
申请号：US17000096
申请日：2020-08-21
Applicant: Landmark Graphics Corporation
Inventor： Keshava Prasad Rangarajan , Raja Vikram R. Pandya , Srinath Madasu , Shashi Dande
Abstract: A system for managing well site operations, the system comprising executable partitions, predictive engines, node system stacks, and a blockchain. The predictive engines comprise an Artificial Intelligence (AI) algorithm to generate earth model variables using a physics model, well log data variables, and seismic data variables. The node system stacks are coupled to the blockchain, sensors, and machine controllers. Each node system stack comprises a Robot Operating System (ROS) based middleware controller, with each coupled to each partition, each node system stack, each predictive engine, and an AI process or processes. The blockchain comprises chained blocks of a distributed network. The distributed network comprises a genesis block and a plurality of subsequent blocks, each subsequent block comprising a well site entry and a hash value of a previous well site entry. The well site entry comprises operation control variables. The operation control variables are based on the earth model variables.
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Patent Agency Ranking
16.发明申请公开(公告)号：US20200320386A1
公开(公告)日：2020-10-08
申请号：US16642452
申请日：2017-12-26
Applicant: Landmark Graphics Corporation
Inventor： Andrey Filippov , Jianxin Lu , Avinash Wesley , Keshava P. Rangarajan , Srinath Madasu
Abstract: System and methods for training neural network models for real-time flow simulations are provided. Input data is acquired. The input data includes values for a plurality of input parameters associated with a multiphase fluid flow. The multiphase fluid flow is simulated using a complex fluid dynamics (CFD) model, based on the acquired input data. The CFD model represents a three-dimensional (3D) domain for the simulation. An area of interest is selected within the 3D domain represented by the CFD model. A two-dimensional (2D) mesh of the selected area of interest is generated. The 2D mesh represents results of the simulation for the selected area of interest. A neural network is then trained based on the simulation results represented by the generated 2D mesh.
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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：US20230116456A1
公开(公告)日：2023-04-13
申请号：US17047109
申请日：2020-06-05
Applicant: Landmark Graphics Corporation
Inventor： Yashas Malur Saidutta , Raja Vikram R Pandya , Srinath Madasu , Shashi Dande , Keshava Rangarajan
Abstract: Systems and methods for automated drilling control and optimization are disclosed. Training data, including values of drilling parameters, for a current stage of a drilling operation are acquired. A reinforcement learning model is trained to estimate values of the drilling parameters for a subsequent stage of the drilling operation to be performed, based on the acquired training data and a reward policy mapping inputs and outputs of the model. The subsequent stage of the drilling operation is performed based on the values of the drilling parameters estimated using the trained model. A difference between the estimated and actual values of the drilling parameters is calculated, based on real-time data acquired during the subsequent stage of the drilling operation. The reinforcement learning model is retrained to refine the reward policy, based on the calculated difference. At least one additional stage of the drilling operation is performed using the retrained model.
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公开(公告)号：US11555394B2
公开(公告)日：2023-01-17
申请号：US16957811
申请日：2018-05-07
Applicant: Landmark Graphics Corporation
Inventor： Srinath Madasu , Keshava Prasad Rangarajan
Abstract: A system and method for controlling a drilling tool inside a wellbore makes use of Bayesian optimization with range constraints. A computing device samples observed values for controllable drilling parameters such as weight-on-bit (WOB) and drill bit rotational speed in RPM and evaluates a selected drilling parameter such a rate-of-penetration (ROP) for the observed values using an objective function. Range constraints can be continuously learned by the computing device as the range constraints change. A Bayesian optimization, subject to the range constraints and the observed values, can produce an optimized value for the controllable drilling parameter to achieve a predicted value for the selected drilling parameter. The system can then control the drilling tool using the optimized value to achieve the predicted value for the selected drilling parameter.
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20.发明授权公开(公告)号：US11319793B2
公开(公告)日：2022-05-03
申请号：US16616795
申请日：2017-11-15
Applicant: Landmark Graphics Corporation
Inventor： Srinath Madasu , Keshava Prasad Rangarajan , Nishant Raizada
Abstract: System and methods for optimizing parameters for drilling operations are provided. Real-time data including values for input variables associated with a current stage of a drilling operation along a planned well path are acquired. A neural network model is trained to produce an objective function defining a response value for at least one operating variable of the drilling operation. The response value for the operating variable is estimated based on the objective function produced by the trained neural network model. Stochastic optimization is applied to the estimated response value so as to produce an optimized response value for the operating variable. Values of controllable parameters are estimated for a subsequent stage of the drilling operation, based on the optimized response value of the operating variable. The subsequent stage of the drilling operation is performed based on the estimated values of the controllable parameters.
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