Optimizing Drilling Parameters and Drill Bit Selection using machine learning in Hassi messaoud oilfield

Abstract

Improving drilling efficiency is a key focus in the oil and gas industry, particularly in drilling engineering. The goal is to develop technologies that can maximize drilling efficiency, reduce time and costs, and minimize safety and environmental risks. One of the primary factors in improving drilling efficiency are optimizing the rate of penetration (ROP). Traditional ROP models are often empirical and inconsistent in field environments, leading to low predictive accuracy. Machine learning is a new technology that is being used to better predict the impact of different parameters on drilling operations. By applying machine learning techniques, operators can gain a more accurate understanding of how factors like drilling parameters, formation characteristics, and equipment performance affect ROP and overall drilling efficiency. This research proposal focuses on the integration and optimization model for drilling parameters and drill bit selection, and employ machine learning algorithms particularly neural networks, a type of deep learning model, can be used for regression by learning a mapping from input features to the target out put, and data analytics to predict and enhance drilling performance. Additionally, to validate this model we applied it to some wells in Hassi Messaoud field