Machine learning optimization of cluster pad structure and well design

The optimization of cluster pad structures and drilling planning is a key factor in enhancing efficiency and reducing costs in oil and gas field development. This study focuses on the application of machine learning algorithms, mathematical modeling, and digital technologies for the rational placement of well clusters. By integrating production wells within a single infrastructure zone, cluster drilling improves resource allocation, minimizes environmental impact, and optimizes field development. The study also explores modern well trajectory control techniques to minimize wellbore intersections and reduce drilling complexity, ensuring operational safety and cost-effectiveness. A major challenge in well clustering is the lack of universal criteria for well grouping. This study proposes a clustering methodology that considers these factors, enabling optimal well placement while reducing infrastructure costs. The research also investigates offshore field development, determining the optimal number of fixed platforms for efficient drilling using machine learning techniques. The results demonstrate that machine learning algorithms significantly enhance the accuracy of optimal well pad placement. Systematic modeling and scenario analysis have reduced the number of required fixed platforms without compromising production efficiency, leading to substantial capital savings. A standardized well grouping methodology has been developed, ensuring a structured approach to well pad design and minimizing drilling risks. These findings contribute to the advancement of modern drilling technologies and provide a framework for improving efficiency in field development. The proposed solutions offer practical applications for reducing costs, optimizing well distribution, and ensuring sustainable oil and gas production. Keywords: well clustering; modeling; cluster pad; optimization; drilling; machine learning.