Integration of Physics Models and ML Algorithms Enhances Automated Sliding Performance

This paper presents a multifaceted approach leveraging precise rig control, physics models, and machine-learning techniques to deliver consistently high performance in a scalable manner for sliding.

February 1, 2026

Journal of Petroleum Technology

Polar coordinate heat maps representing the Von Mises probability densityfor a series of slides, with black lines denoting estimated α. Each ring corresponds to aslide. The left shows individual slide estimates, and the right shows the filtered estimates.

Automated sliding has existed in the industry for years. In early deployments, simply meeting steering requirements with nearly 100% automation, even at the cost of drilling performance, was considered a technical success. Now, algorithms are expected to meet or exceed performance standards set by the best drillers. A multifaceted approach leveraging precise rig control, physics models, and machine-learning techniques aims to deliver consistent high-level performance in a scalable manner.

Introduction

Once tools run through the rotary table, only two aspects of the drilling process have a meaningful effect on the outcome: rate of penetration (ROP) and tool-face control.

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