US 11,867,055 B2
Method and system for construction of artificial intelligence model using on-cutter sensing data for predicting well bit performance
Guodong Zhan, Dhahran (SA); Arturo Magana-Mora, Dhahran (SA); Timothy Eric Moellendick, Dhahran (SA); Chinthaka P. Gooneratne, Dhahran (SA); and Jianhui Xu, Dhahran (SA)
Assigned to SAUDI ARABIAN OIL COMPANY, Dhahran (SA)
Filed by SAUDI ARABIAN OIL COMPANY, Dhahran (SA)
Filed on Dec. 8, 2021, as Appl. No. 17/643,194.
Prior Publication US 2023/0175394 A1, Jun. 8, 2023
Int. Cl. E21B 49/00 (2006.01); G05B 13/02 (2006.01)
CPC E21B 49/003 (2013.01) [G05B 13/027 (2013.01); E21B 2200/20 (2020.05)] 14 Claims
OG exemplary drawing
 
8. A method, comprising:
measuring drilling performance metrics while performing drilling operations in an offset well using an instrumented cutter of a drill bit comprising an on-cutter sensor;
transmitting the drilling performance metrics to a computing device;
training a machine learning (ML) model hosted by the computing device using processed drilling performance metrics, surface drilling parameters, and characteristics of the instrumented cutter to obtain a trained ML model;
using the trained ML model to optimize the surface drilling parameters and predict drill bit performance while performing drilling operations in a current well;
processing the drill performance metrics, surface drilling parameters, and the characteristics of the instrumented cutter by:
aggregating drill performance metrics from a plurality of on-cutter sensors of the instrumented cutter; and
transforming the drill performance metrics, the surface drilling parameters, and characteristics of the instrumented cutter to remove noise using a Fast Fourier Transform.