REINFORCEMENT LEARNING APPLIED TO THE PROCESSES OF INTELLIGENTWELL DRILLING MODE CONTROL

Abstract

This paper considers the problem of intelligent control of oil and gas well drilling operations under conditions of highly dynamic processes, uncertain geological conditions, and strict constraints regarding safety and economic efficiency. It is noted that traditional approaches, based on engineers’ experience and classical control loop controllers, do not fully account for the multidimensional states of the environment and the nonlinear relationships between operating parameters.

The purpose of the scientific research is to analyze the capabilities of reinforcement learning in the context of intelligent drilling mode control, to investigate the suitability of various classes of reinforcement learning algorithms, and to develop a conceptual architecture for an agent capable of optimizing, within a “state–action–reward” framework, the axial load on the drilling bit, rotation speed, flushing parameters, and trajectory, taking into account the well response.

This research summarizes current practical applications of reinforcement learning to wellbore cleaning, directional drilling with bottomhole pressure control within specified limits, geonavigation, trajectory control, and power plant control; it analyzes actor-critic architectures, DQN-like schemes, and offline reinforcement learning with conservative Q-learning, as well as the role of digital twins as a training environment. Special attention is given to the formation and calibration of the reward function, the influence of the discount factor on the balance between immediate benefit and long-term reliability, as well as the optimization of hyperparameters that determine the stability and convergence of agents.

The results of the conceptual analysis suggest that it is feasible to use reinforcement learning as the basis for developing intelligent drilling mode control systems aimed at increasing mechanical drilling speed, reducing drill bit wear, lowering accident rates, and decreasing the proportion of manual labor. Further research prospects include the development and implementation of reinforcement learning agent prototypes in digital twins of drilling systems, the integration of multi-agent and physics-informed solutions, and the experimental verification of their effectiveness using real industrial data.