US 12,297,732 B2
Drilling control
Nathaniel Wicks, Katy, TX (US); Yingwei Yu, Katy, TX (US); Richard John Meehan, Houston, TX (US); and Darine Mansour, Katy, TX (US)
Assigned to SCHLUMBERGER TECHNOLOGY CORPORATION, Sugar Land, TX (US)
Filed by Schlumberger Technology Corporation, Sugar Land, TX (US)
Filed on Jun. 15, 2021, as Appl. No. 17/304,151.
Prior Publication US 2022/0397029 A1, Dec. 15, 2022
Int. Cl. E21B 44/04 (2006.01); E21B 45/00 (2006.01); G06N 3/044 (2023.01); G06N 3/088 (2023.01)
CPC E21B 44/04 (2013.01) [E21B 45/00 (2013.01); G06N 3/044 (2023.01); G06N 3/088 (2013.01); E21B 2200/22 (2020.05)] 14 Claims
OG exemplary drawing
 
1. A method comprising:
receiving sensor data;
performing training to generate a trained neural network, wherein the training comprises utilizing a trained digital avatar to train the neural network as an agent;
determining a rate of penetration drilling parameter value using the trained neural network and at least a portion of the sensor data, wherein the rate of penetration drilling parameter value comprises a set point value, and wherein the trained neural network is trained using a reward function that comprises a plurality of terms; and
issuing a control instruction for drilling a borehole using the determined rate of penetration drilling parameter value.