Data-driven model for the identification of the rock type at a drilling bit

TL;DR

This paper introduces a machine learning-based method for real-time identification of rock types at the drilling bit, improving accuracy and enabling better directional drilling in complex geological conditions.

Contribution

It presents a novel data-driven approach that enhances rock type detection accuracy at the drill bit using sensor data and machine learning, outperforming previous methods.

Findings

Reduced classification error from 13.5% to 9%.

Effective for real-time, precise directional drilling.

Generalizes well to new wells.

Abstract

Directional oil well drilling requires high precision of the wellbore positioning inside the productive area. However, due to specifics of engineering design, sensors that explicitly determine the type of the drilled rock are located farther than 15m from the drilling bit. As a result, the target area runaways can be detected only after this distance, which in turn, leads to a loss in well productivity and the risk of the need for an expensive re-boring operation. We present a novel approach for identifying rock type at the drilling bit based on machine learning classification methods and data mining on sensors readings. We compare various machine-learning algorithms, examine extra features coming from mathematical modeling of drilling mechanics, and show that the real-time rock type classification error can be reduced from 13.5 % to 9 %. The approach is applicable for precise directional drilling in relatively thin target intervals of complex shapes and generalizes appropriately to new wells that are different from the ones used for training the machine learning model.