公开(公告)号：US11643913B2

公开(公告)日：2023-05-09

申请号：US17257234

申请日：2019-04-30

Applicant: Landmark Graphics Corporation

Inventor： Srinath Madasu ,  Keshava Prasad Rangarajan

IPC: E21B43/26 ,  G06F30/23 ,  G06F30/27 ,  E21B44/02 ,  G06N3/08 ,  G06N3/044

CPC classification number: E21B43/26 ,  E21B44/02 ,  G06F30/23 ,  G06F30/27 ,  G06N3/044 ,  G06N3/08

Abstract: System and methods for simulating fluid flow during downhole operations are provided. Measurements of an operating variable at one or more locations within a formation are obtained from a downhole tool disposed in a wellbore within the formation during a current stage of a downhole operation being performed along the wellbore. The obtained measurements are applied as inputs to a hybrid model of the formation. The hybrid model includes physics-based and machine learning models that are coupled together within a simulation grid. Fluid flow within the formation is simulated, based on the inputs applied to the hybrid model. A response of the operating variable is estimated for a subsequent stage of the downhole operation along the wellbore, based on the simulation. Flow control parameters for the subsequent stage are determined based on the estimated response. The subsequent stage of the operation is performed according to the determined flow control parameters.

公开(公告)号：US11492892B2

公开(公告)日：2022-11-08

申请号：US16652336

申请日：2019-05-16

Applicant: Landmark Graphics Corporation

Inventor： Mahdi Parak ,  Srinath Madasu ,  Egidio Marotta

IPC: E21B44/00 ,  E21B44/02 ,  E21B45/00 ,  G06N5/00

Abstract: Systems and methods can automatically and dynamically determine an optimum frequency for data being input into a drilling optimization tool in order to provide predictive modeling for well drilling operations. The methods and systems selectively input sets of data having different frequencies into the drilling optimization tool to build different predictive models at different frequencies. An optimization algorithm such as Bayesian optimization is then applied to the models to identify in real time an optimum frequency for the data sets being input into the drilling optimization tool based on current operational and environmental parameters.

公开(公告)号：US11492890B2

公开(公告)日：2022-11-08

申请号：US16631542

申请日：2017-08-21

Applicant: LANDMARK GRAPHICS CORPORATION

Inventor： Srinath Madasu ,  Keshava Prasad Rangarajan ,  Robello Samuel ,  Nishant Raizada

IPC: E21B44/00 ,  E21B7/04

Abstract: A system for real-time steering of a drill bit includes a drilling arrangement and a computing device in communication with the drilling arrangement. The system iteratively, or repeatedly, receives new data associated the wellbore. At each iteration, a model, for example an engineering model, is applied to the new data to produce an objective function defining the selected drilling parameter. The objective function is modified at each iteration to provide an updated value for the selected drilling parameter and an updated value for at least one controllable parameter. In one example, the function is modified using Bayesian optimization The system iteratively steers the drill bit to obtain the updated value for the selected drilling parameter by applying the updated value for at least one controllable parameter over the period of time that the wellbore is being formed.

公开(公告)号：US11488025B2

公开(公告)日：2022-11-01

申请号：US16954446

申请日：2019-12-16

Applicant: Landmark Graphics Corporation

Inventor： Srinath Madasu ,  Keshava Prasad Rangarajan

IPC: G06N3/08 ,  G06N3/04

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.

公开(公告)号：US20220316278A1

公开(公告)日：2022-10-06

申请号：US17619784

申请日：2020-02-10

Applicant: Landmark Graphics Corporation

Inventor： Raja Vikram Raj Pandya ,  Srinath Madasu ,  Keshava Prasad Rangarajan ,  Shashi Dande ,  Yashas Malur Saidutta

IPC: E21B7/04

Abstract: Geosteering can be used in a drilling operation to create a wellbore that is used to extract hydrocarbons from a defined zone within the subterranean formation. According to some aspects, generating paths for the wellbore may include using path-planning protocols and pure-pursuit protocols. The pure-pursuit protocol may be executed to output a plurality of candidate drilling paths. The output may also include control parameters for controlling the drill bit. A trajectory optimizer may determine a result of multi-objective functions for each candidate path. A cost function may represent a cost or loss associated with a candidate path. Additionally, the trajectory optimizer may perform an optimization protocol, such as Bayesian optimization, on the cost functions to determine which candidate path to select. The selected candidate path may correspond to new control parameters for controlling the drill bit to reach the target location.

公开(公告)号：US20220298907A1

公开(公告)日：2022-09-22

申请号：US17619304

申请日：2019-12-31

Applicant: Landmark Graphics Corporation

Inventor： Venugopal Devarapalli ,  Srinath Madasu ,  Shashi Dande ,  Keshava Prasad Rangarajan

IPC: E21B44/00 ,  G06N3/08 ,  G06N3/04 ,  E21B7/04

Abstract: Certain aspects and features relate to a system for trajectory planning and control for new wellbores. Data can be received for multiple existing wells associated with a subterranean reservoir and used to train a deep neural network model to make accurate well property projections at any other location in the reservoir. A model of features for specific well locations based on seismic attributes of the well location can be automatically generated, and the model can be used in drilling trajectory optimization. In some examples, the system builds a deep neural network (DNN) model based on the statistical features, and trains the DNN model using Bayesian optimization to produce an optimized DNN model. The optimized model can be used to provide drilling parameters to produce an optimized trajectory for a new well.

公开(公告)号：US11441404B2

公开(公告)日：2022-09-13

申请号：US17043129

申请日：2018-04-12

Applicant: Landmark Graphics Corporation

Inventor： Srinath Madasu ,  Yogendra Narayan Pandey ,  Keshava Rangarajan

IPC: E21B43/26 ,  E21B47/06 ,  G06N3/04 ,  G06N3/063

Abstract: A method for fracturing a formation is provided. Real-time fracturing data is acquired from a well bore during fracturing operation. The real-time fracturing data is processed using a recurrent neural network trained using historical data from analogous wells. A real-time response variable prediction is determined using the processed real-time fracturing data. Fracturing parameters for the fracturing operation are adjusted in real-time based on the real-time response variable prediction. The fracturing operation is performed using the fracturing parameters that were adjusted based on the real-time response variable prediction.

公开(公告)号：US11269100B2

公开(公告)日：2022-03-08

申请号：US16610031

申请日：2017-12-21

Applicant: Landmark Graphics Corporation

Inventor： Youli Mao ,  Raja Vikram Pandya ,  Bhaskar Mandapaka ,  Keshava Prasad Rangarajan ,  Srinath Madasu ,  Satyam Priyadarshy ,  Ashwani Dev

IPC: G01V1/30 ,  G01V1/50 ,  G06K9/62 ,  G06N7/00 ,  G06N20/00

Abstract: A method includes receiving a training selection of a first set of faults located in a first subset of a seismic dataset for a subsurface geologic formation, detecting a second set of faults in the seismic dataset based on fault interpretation operations using a first set of interpretation parameters, and determining a difference between the first set of faults and the second set of faults. The method also includes generating a second set of interpretation parameters for the fault interpretation operations based on the difference between the first set of faults and the second set of faults, and determining a feature of the subsurface geologic formation based on fault interpretation operations using the second set of interpretation parameters.

公开(公告)号：US20220034220A1

公开(公告)日：2022-02-03

申请号：US17276985

申请日：2018-11-30

Applicant: Landmark Graphics Corporation

Inventor： Srinath Madasu ,  Ashwani Dev ,  Keshava Prasad Rangarajan ,  Satyam Priyadarshy

IPC: E21B47/12 ,  E21B47/07 ,  E21B47/10 ,  E21B43/12

Abstract: A system for determining real time cluster efficiency for a pumping operation in a wellbore includes a pump, a surface sensor, a downhole sensor system, and a computing device. The pump can pump slurry or diverter material in the wellbore. The surface sensor can be positioned at a surface of the wellbore to detect surface data about the pump. The downhole sensor system can be positioned in the wellbore to detect downhole data about an environment of the wellbore. The computing device can receive the surface data from the surface sensor, receive the downhole data from the downhole sensor system, apply the surface data and the downhole data to a long short-term memory (LSTM) neural network to produce a predicted cluster efficiency associated with operational settings of the pump, and control the pump using the operational settings to achieve the predicted cluster efficiency.

公开(公告)号：US20210372259A1

公开(公告)日：2021-12-02

申请号：US16883328

申请日：2020-05-26

Applicant: Landmark Graphics Corporation

Inventor： Shashi Dande ,  Srinath Madasu ,  Keshava Prasad Rangarajan

IPC: E21B44/02 ,  E21B45/00 ,  E21B47/12

Abstract: Aspects and features of a system for real-time drilling using automated data quality control can include a computing device, a drilling tool, sensors, and a message bus. The message bus can receive current data from a wellbore. The computing device can generate and use a feature-extraction model to provide revised data values that include those for missing data, statistical outliers, or both. The model can be used to produce controllable drilling parameters using highly accurate data to provide optimal control of the drilling tool. The real-time message bus can be used to apply the controllable drilling parameters to the drilling tool.