Application of machine learning algorithms for optimizing the trajectory of inclined wells

Machine learning, a subset of artificial intelligence, allows computers to learn and improve from data without explicit programming. Machine learning algorithms are categorized into supervised, unsupervised, and reinforcement learning, each serving different applications such as classification, clustering, and decision-making. In the oil and gas industry, machine learning is applied to drilling processes, reservoir characterization, and exploration. It improves efficiency in predicting reservoir properties, optimizing drilling parameters, and detecting anomalies. The methodology for analyzing well trajectory includes evaluating survey data with calculation methods like tangential, balanced tangential, average angle, radius of curvature, and minimum curvature. These methods help optimize wellbore paths. This study outlines control criteria essential for optimizing borehole trajectory management in oil and gas well drilling. The deviation correction criterion aims to maintain the borehole path within a designated trajectory, minimizing deviation from the planned design profile. Optimal control conditions are defined through mathematical criteria involving radius vectors and design trajectory alignment. The control framework incorporates efficiency measures, calculating the trajectory's costeffectiveness and operational constraints. Machine learning algorithms facilitate these control strategies, focusing particularly on zenith angle correction for trajectory stabilization. These methods provide adaptable options for deflector angle and correction length, ensuring alignment with target intervals. The approach enhances trajectory accuracy, minimizes costs, and complies with technical, geological, and technological constraints inherent to drilling. Software incorporating machine learning and these methods was developed and tested, demonstrating improvements in analyzing survey data and optimizing well trajectory, contributing to more efficient drilling operations and reduced costs. Keywords: machine learning; mathematical models; control criteria; well trajectory optimization; survey data analysis; calculation methods.