Using Machine Learning Techniques to Forecast Cuttings Removal in Hole Cleaning

Abstract

This study addresses the significant challenge of hole cleaning in drilling operations, which is essential for preventing stuck pipe incidents�a major cause of non-productive time and additional costs in drilling. This research aims to develop and validate machine learning models that enhance the prediction and optimization of cuttings removal during drilling. Utilizing a dataset derived from historical drilling operations, we employed regression analysis and neural network models to forecast the presence and height of slurry beds. The models were trained on variables such as borehole dimensions, drilling fluid characteristics, and operational parameters. Our results demonstrate that these models effectively predict conditions that could lead to stuck pipes, allowing for preemptive adjustments to drilling operations. This capability could significantly reduce unplanned downtime and associated costs. The primary contribution of this study lies in its innovative use of machine learning to transform predictive maintenance in drilling operations, offering substantial improvements in efficiency and safety. These advancements represent a crucial step forward in drilling technology, with the potential to mitigate risks and enhance operational decision-making across the industry.