Real-time data-driven detection of the rock type alteration during a directional drilling

During the 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) data. However, there are no mathematical modeling approaches that reconstruct the rock type based on MWD data with high accuracy. In this article, we present a data-driven procedure that utilizes MWD data for quick detection of changes in rock type. We propose the approach that combines traditional machine learning based on the solution of the rock type classification problem with change detection procedures rarely used before in Oil & 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 type reducing the change detection delay from 20 to 2.6 m and the number of false positive alarms from 71 to 7 per well.