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

IPC: E21B44/00 ,  G06N3/092

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：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.