Computational prediction of the drilling rate of penetration (ROP): A comparison of various machine learning approaches and traditional models

Abstract

This thesis explores several important aspects of the oil and gas industry, focusing on drilling techniques and selecting appropriate bits, as well as the use of artificial intelligence to enhance exploration and production operations in oil fields. The thesis also provides general information about drilling bits and highlights the crucial role they play in successful drilling operations, as well as the complexities of selecting the right tools for various drilling conditions. Additionally, the thesis delves into the exploration of artificial intelligence applications in the oil and gas industry, indicating the significant opportunities these technologies offer to improve production processes and reduce costs through big data analysis and the implementation of machine learning algorithms. Furthermore, the thesis addresses productivity related to the rate of penetration and the selection of drilling tools based on this rate, emphasizing the importance of understanding drilling dynamics and using performance indicators to make informed decisions. Overall, this thesis underscores the importance of advanced technologies such as artificial intelligence in enhancing the efficiency of the oil and gas industry and promoting sustainable resource extraction methods.