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

## TL;DR

This paper introduces a data-driven machine learning approach combined with change detection techniques to rapidly identify rock type changes during directional drilling, significantly reducing detection delay and false alarms.

## Contribution

It presents a novel integration of machine learning and change detection methods tailored for real-time lithotype change detection in the oil and gas industry.

## Key findings

- Reduced change detection delay from 20m to 1.8m
- Lowered false-positive alarms from 43 to 6 per well
- Demonstrated effectiveness on data from Siberian oilfield

## Abstract

During the directional drilling, a bit may sometimes go to a nonproductive rock layer due to the gap about 20m 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 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 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 the 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 $1.8$ meters and the number of false-positive alarms from $43$ to $6$ per well.