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

arXiv: https://arxiv.org/abs/1903.11436 · HTML: https://mlsanity.com/paper/1903.11436/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2019.

## Built on by (most cited first, 5 of 5)

- [Similarity learning for wells based on logging data](https://mlsanity.com/paper/2202.05583/) (2022)
- [Non-contrastive representation learning for intervals from well logs](https://mlsanity.com/paper/2209.14750/) (2022)
- [InDiD: Instant Disorder Detection via Representation Learning](https://mlsanity.com/paper/2106.02602/) (2021)
- [Deep learning model solves change point detection for multiple change types](https://mlsanity.com/paper/2204.07403/) (2022)
- [Usage of specific attention improves change point detection](https://mlsanity.com/paper/2204.08175/) (2022)

## Abstract (arXiv)

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.