A model-free toolface control strategy for cross-well intelligent directional drilling

Toolface adjustment methods for directional drilling in oil and gas drilling currently rely heavily on manual real-time interventions for continuous adjustment. However, due to the influence of manual experience, these existing methods suffer from unstable effects and high labor costs. As energy consumption continues to rise, the demand for intelligent directional drilling is becoming increasingly pressing. To address the challenges posed by automatic adjustment issues encountered in actual drilling operations under various complex downhole environments, this study presents a model-free online learning adaptive decision strategy for cross-well intelligent adjustment and toolface stabilization, which is applicable for both slide and rotatory steerable systems. A reward function embedded with expert operating experience is developed to learn the orientation strategy from the driller's corrective actions. Additionally, a priority-based experience replay mechanism is introduced to enhance online learning efficiency. To accurately simulate the directional drilling process and pre-train the orientation strategy, a data-driven directional drilling simulation environment is proposed. With the aim of facilitating implementation and widespread adoption in practical engineering, this study also involves the migration of a strategic model, integration of a real-time interaction module, and encapsulation of algorithms for field applications. Simulations and field experiments are conducted to validate the effectiveness of the proposed strategy. The experimental results demonstrate that the strategy can achieve decision-making goals in a short period of time.