US 12,454,887 B2
Using artificial intelligence methods to exploit well logging data
Martin E. Poitzsch, Northumberland, NH (US); Chicheng Xu, Houston, TX (US); and Shouxiang Ma, Dhahran (SA)
Assigned to SAUDI ARABIAN OIL COMPANY, Dhahran (SA)
Filed by SAUDI ARABIAN OIL COMPANY, Dhahran (SA)
Filed on Jul. 30, 2024, as Appl. No. 18/788,947.
Claims priority of provisional application 63/516,754, filed on Jul. 31, 2023.
Prior Publication US 2025/0043681 A1, Feb. 6, 2025
Int. Cl. E21B 49/00 (2006.01)
CPC E21B 49/005 (2013.01) [E21B 2200/20 (2020.05); E21B 2200/22 (2020.05)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
obtaining a temporal well depth log and a plurality of temporal logging while drilling (LWD) logs from a drilling operation, wherein the drilling operation comprises a drill bit traversing through a subsurface at a plurality of depths;
obtaining a temporal record of operational drilling parameters from the drilling operation;
determining, using a first machine-learning model, a temporal history of the drilling operation, wherein the first machine-learning model accepts, at least in part, the temporal well depth log and the temporal record of operational drilling parameters;
constructing, using the temporal well depth log and the plurality of temporal LWD logs, a plurality of temporal property logs at each depth in the plurality of depths, wherein each temporal property log comprises a plurality of data points;
identifying and removing outlier data points from each of the temporal property logs based on the temporal history;
processing the plurality of temporal property logs with, at least, a second machine-learning model to form a plurality of corrected temporal property logs;
aggregating the plurality of corrected temporal property logs to form a plurality of high fidelity LWD logs; and
determining a lithology of the subsurface using, at least in part, the plurality of high fidelity LWD logs.