Modelling and optimization of well hole cleaning using artificial intelligence techniques

Ineffective hole cleaning in deviated and horizontal well drilling can lead to issues like stuck pipes, reduced rate of penetration (ROP), and drill bit damage, resulting in increased non-productive time (NPT) and operational costs. Traditional methods for assessing hole cleaning rely on experimental and empirical models that often fail to account for all influencing factors and lack real-time applicability. This study aims to improve the accuracy and practicality of hole cleaning assessment by applying Artificial Intelligence (AI) techniques, specifically Artificial Neural Networks (ANN) and Genetic Algorithms (GA), to predict downhole parameters and optimize drilling processes. These AI methods analyze the impact of key drilling parameters—such as weight on bit (WOB), ROP, rock geomechanics, drilling fluid characteristics, and rig hydraulics—on hole cleaning. Results demonstrated that AI-driven models provide high-precision predictions and enable real-time optimization, significantly reducing NPT and enhancing drilling efficiency and safety. In conclusion, AI techniques like ANN and GA offer a robust solution to improve hole cleaning, overcoming limitations of traditional methods and contributing to safer, more cost-effective drilling operations.