US 12,404,762 B2
Drilling system
Riadh Boualleg, Stonehouse (GB); Geoffrey Charles Downton, Stonehouse (GB); Jean Marie Degrange, Sugar Land, TX (US); Steven G. Villareal, Houston, TX (US); Maja Ignova, Stonehouse (GB); Ling Li, Stonehouse (GB); Katharine L. Mantle, Stonehouse (GB); Tao Yu, Beijing (CN); Jia Yao, Beijing (CN); Kai Feng Zhao, Beijing (CN); and Paul Bolchover, Beijing (CN)
Assigned to Schlumberger Technology Corporation, Sugar Land, TX (US)
Appl. No. 17/441,522
Filed by Schlumberger Technology Corporation, Sugar Land, TX (US)
PCT Filed Mar. 20, 2020, PCT No. PCT/US2020/024021
§ 371(c)(1), (2) Date Sep. 21, 2021,
PCT Pub. No. WO2020/191360, PCT Pub. Date Sep. 24, 2020.
Claims priority of provisional application 62/950,934, filed on Dec. 20, 2019.
Claims priority of provisional application 62/849,975, filed on May 20, 2019.
Claims priority of provisional application 62/821,551, filed on Mar. 21, 2019.
Prior Publication US 2022/0170359 A1, Jun. 2, 2022
Int. Cl. E21B 44/00 (2006.01); E21B 7/04 (2006.01); E21B 47/024 (2006.01); G06N 20/20 (2019.01)
CPC E21B 44/00 (2013.01) [E21B 7/04 (2013.01); E21B 47/024 (2013.01); G06N 20/20 (2019.01); E21B 2200/20 (2020.05); E21B 2200/22 (2020.05)] 16 Claims
OG exemplary drawing
 
1. A method comprising:
acquiring, via one or more downhole tool sensors, drilling performance data for a downhole tool;
modeling drilling performance of the downhole tool to generate results;
training a machine learning model using the drilling performance data and the results to generate a trained machine learning model, wherein training the machine learning model includes using the drilling performance data and the results to compute residuals associated with values of a drilling trajectory that comprises a dogleg during drilling;
predicting behavior of the downhole tool using the trained machine learning model;
selecting, based at least on the behavior of the downhole tool, the downhole tool for drilling a borehole;
operating the downhole tool in regularly spaced intervals that are proportioned into neutral periods and bias periods;
cycling a tool face of the downhole tool at a first rate during the neutral periods such that a net trajectory response of the downhole tool is approximately tangent with zero net curvature;
cycling the tool face of the downhole tool at a second rate during the bias periods such that the net trajectory response of the downhole tool has a curvature;
acquiring, via the one or more downhole tool sensors, second drilling performance data for the downhole tool during the drilling of the borehole; and
dynamically adjusting the trained machine learning model in real-time based on the second drilling performance data during the drilling of the borehole.