Predicting Drilling problems Using Machine Learning techniques

Abstract

Stuck pipe incidents are among the most critical operational challenges in oil and gas well drilling. They often lead to significant downtime, increased operational costs, and potential loss of expensive equipment. Due to the complex and dynamic nature of drilling environments, early prediction of such events remains a significant challenge. This project leverages Machine Learning techniques to develop a predictive model capable of identifying early warning signs of stuck pipe incidents during drilling operations. The model is based on the analysis of real-time historical drilling data, including parameters such as torque, rate of penetration (ROP), flow rate, and downhole pressure. Data preprocessing and advanced machine learning algorithms were applied to uncover patterns and correlations between variables. This enabled the construction of a highly accurate predictive model that can anticipate stuck pipe events in advance, allowing operators to take preventive actions. The results of this work contribute to improved safety, reduced operational costs, and enhanced drilling efficiency by providing an intelligent decision support system. This ultimately increases the reliability and performance of drilling operations in the oil and gas industry