公开(公告)号：US20220236707A1

公开(公告)日：2022-07-28

申请号：US17619341

申请日：2020-03-16

Applicant: Landmark Graphics Corporation

Inventor： Keshava Rangarajan ,  Shashi Dande ,  Rohan Lewis ,  Siddhartha Kazuma Rangarajan ,  Aditya Chemudupaty

IPC: G05B19/042

Abstract: A system for autonomous operation and management of oil and gas fields includes at least one autonomous vehicle. The system also includes a processor communicatively couplable to the plurality of autonomous vehicles and a non-transitory memory device including instructions that are executable by the processor to cause the processor to perform operations. The operations include receiving field analytics data of an oil and gas field and producing at least one hydrocarbon field model based on the field analytics data. Additionally, the operations include deploying the at least one hydrocarbon field model to a sensor trap appliance using the at least one autonomous vehicles and collecting well sensor data from the sensor trap appliance. Further, the operations include detecting an anomaly using the at least one hydrocarbon field model and the well sensor data and triggering an operational process based on detecting the anomaly.

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.

公开(公告)号：US12189354B2

公开(公告)日：2025-01-07

申请号：US17619341

申请日：2020-03-16

Applicant: Landmark Graphics Corporation

Inventor： Keshava Rangarajan ,  Shashi Dande ,  Rohan Lewis ,  Siddhartha Kazuma Rangarajan ,  Aditya Chemudupaty

IPC: G05B19/042

Abstract: A system for autonomous operation and management of oil and gas fields includes at least one autonomous vehicle. The system also includes a processor communicatively couplable to the plurality of autonomous vehicles and a non-transitory memory device including instructions that are executable by the processor to cause the processor to perform operations. The operations include receiving field analytics data of an oil and gas field and producing at least one hydrocarbon field model based on the field analytics data. Additionally, the operations include deploying the at least one hydrocarbon field model to a sensor trap appliance using the at least one autonomous vehicles and collecting well sensor data from the sensor trap appliance. Further, the operations include detecting an anomaly using the at least one hydrocarbon field model and the well sensor data and triggering an operational process based on detecting the anomaly.

公开(公告)号：US12091959B2

公开(公告)日：2024-09-17

申请号：US17616227

申请日：2019-07-10

Applicant: Landmark Graphics Corporation

Inventor： Srinath Madasu ,  Shashi Dande ,  Keshava Prasad Rangarajan

IPC: E21B44/02

CPC classification number: E21B44/02 ,  E21B2200/22

Abstract: A system and method for controlling multiple drilling tools inside wellbores makes use of Bayesian optimization with range constraints. A computing device samples observed values for controllable drilling parameters such as weight-on-bit, mud flow rate and drill bit rotational speed in RPM and evaluates a selected drilling parameter such a rate-of-penetration and hydraulic mechanical specific energy for the observed values using an objective function. Range constraints including the physical drilling environment and the total power available to all drilling tools within the drilling environment 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 parameters to achieve a predicted value for the drilling parameters. The system can then control the drilling tool using the optimized value to achieve the predicted value for the selected drilling parameter.

公开(公告)号：US11982171B2

公开(公告)日：2024-05-14

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

IPC: E21B44/00 ,  G06N3/092

CPC classification number: E21B44/00 ,  G06N3/092 ,  E21B2200/20 ,  E21B2200/22

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.

公开(公告)号：US11643918B2

公开(公告)日：2023-05-09

申请号：US16883328

申请日：2020-05-26

Applicant: Landmark Graphics Corporation

Inventor： Shashi Dande ,  Srinath Madasu ,  Keshava Prasad Rangarajan

IPC: E21B44/00 ,  E21B44/02 ,  E21B45/00 ,  E21B47/12 ,  G06N20/00 ,  G06N3/088

CPC classification number: E21B44/00 ,  E21B44/02 ,  E21B45/00 ,  E21B47/12 ,  G06N3/088 ,  G06N20/00 ,  E21B2200/20 ,  E21B2200/22

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.

公开(公告)号：US20220235645A1

公开(公告)日：2022-07-28

申请号：US17616227

申请日：2019-07-10

Applicant: Landmark Graphics Corporation

Inventor： Srinath Madasu ,  Shashi Dande ,  Keshava Prasad Rangarajan

IPC: E21B44/02

Abstract: A system and method for controlling multiple drilling tools inside wellbores makes use of Bayesian optimization with range constraints. A computing device samples observed values for controllable drilling parameters such as weight-on-bit, mud flow rate and drill bit rotational speed in RPM and evaluates a selected drilling parameter such a rate-of-penetration and hydraulic mechanical specific energy for the observed values using an objective function. Range constraints including the physical drilling environment and the total power available to all drilling tools within the drilling environment 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 parameters to achieve a predicted value for the drilling parameters. The system can then control the drilling tool using the optimized value to achieve the predicted value for the selected drilling parameter.

公开(公告)号：US20210355805A1

公开(公告)日：2021-11-18

申请号：US16651859

申请日：2019-12-05

Applicant: Landmark Graphics Corporation

Inventor： Keshava Prasad Rangarajan ,  Raja Vikram R. Pandya ,  Srinath Madasu ,  Shashi Dande

IPC: E21B44/00 ,  E21B49/00 ,  E21B7/06 ,  G06N5/02 ,  G06N20/00 ,  G01V99/00

Abstract: A system for controlling operations of a drill in a well environment. The system comprises a predictive engine, a ML engine, a controller, and a secure, distributed storage network. The predictive engine receives a variables associated with surface and sub-surface sensors and predicts an earth model based on the variables, predictor variable(s), outcome variable(s), and relationships between the predictor variable(s) and the outcome variable(s). The predictive engine is also configured to predict a drill path(s) ahead of the drill based on using stochastic modeling, an outcome variable(s), the predicted earth model, and a drilling model(s). The controller is configured to generate a system response(s) based on the predicted drill path(s) and a current state of the drill. The ML engine stores the earth model, the drill path(s), and the variables in the distributed storage network, trains data, and creates the drilling model(s).

公开(公告)号：US20210055442A1

公开(公告)日：2021-02-25

申请号：US17000117

申请日：2020-08-21

Applicant: Landmark Graphics Corporation

Inventor： Keshava Prasad Rangarajan ,  Raja Vikram R. Pandya ,  Srinath Madasu ,  Shashi Dande

IPC: G01V1/40 ,  H04L9/06 ,  G06K9/62 ,  G06N20/00

Abstract: A system, for controlling well site operations, comprising a machine learning engine, a predictive engine, a node system stack, and a blockchain. The learning engine includes a machine learning algorithm, an algorithmically generated earth model, and control variables. The learning algorithm generates a trained data model using the algorithmically generated earth model. The predictive engine includes an Artificial Intelligence (AI) algorithm. The AI algorithm generates a trained AI algorithm using the trained data model and earth model variables using the trained AI algorithm. The system stack is communicable coupled to the predictive engine, the learning engine, the blockchain, sensors, and a machine controller. The blockchain having a genesis block and a plurality of subsequent blocks. Each subsequent block comprising a well site entry and a hash of a previous entry. The well site entry comprises transacted operation control variables. The transacted variables are based on the generated earth model variables.