Real-Time Data-Driven Detection of the Rock-Type Alteration During a Directional Drilling

During directional drilling, a bit may sometimes go to a nonproductive rock layer due to the gap about 20 m between the bit and high-fidelity rock-type sensors. The only way to detect the lithotype changes in time is the usage of measurements while drilling (MWD). However, there are no general mathematical modeling approaches that both well reconstruct the rock type based on MWD data and correspond to specifics of the oil and gas industry. In this letter, we present a data-driven procedure that utilizes MWD data for quick detection of changes in rock types. We propose the approach that combines traditional machine learning (ML) based on the solution of the rock-type classification problem with change detection procedures rarely used before in oil and gas industry. The data come from a newly developed oilfield in the north of western Siberia. The results suggest that we can detect a significant part of changes in rock types, reducing the change detection delay from 20 to 1.8 m and the number of false-positive alarms from 43 to 6 per well.