Ebrahimi, Zahed (Department of Electrical and Computer Engineering, Concordia Uni), Selmic, Rastko (Concordia University)

Intelligent Control System for Directional Drilling: A GRU Neural Network Approach

Scheduled for presentation during the Regular Session "Intelligent systems" (WeBD), Wednesday, June 11, 2025, 14:20−14:40, Room C

33rd Mediterranean Conference on Control and Automation, June 10-13, 2025, Tangier, Morocco

This information is tentative and subject to change. Compiled on May 9, 2025

Abstract

As directional drilling technologies evolve, effective control systems are crucial for optimizing drilling trajectories in complex subsurface formations. This paper presents an advanced control strategy using Gated Recurrent Unit (GRU) neural networks to achieve real-time trajectory control in directional drilling operations. The proposed system integrates GRU-based adaptive learning with finite element modeling (FEM) to dynamically update the parameters of a PID controller. By continuously adjusting PID gains based on real-time feedback and error minimization, the system improves adaptability, robustness, and precision in downhole conditions. The GRU network efficiently captures temporal dependencies, enabling predictive control and minimizing trajectory deviations. In addition, real-time data feedback further improves control accuracy and operational efficiency. The simulation results illustrate the effectiveness of the GRU-based adaptive PID control approach, which demonstrates improved trajectory prediction and system stability in complex drilling environments.