Incepting Caspian AI-Driven Well Trajectory Design: Simultaneous Multi-Objective Optimization for Near Real-Time Well Path Construction

Abstract Optimizing well trajectory planning in complex offshore fields is critical for balancing the interdependent objectives of reservoir management, drilling efficiency, safety, and cost control. Conventional manual methods often fail to address these complexities, leading to suboptimal designs, increased risks, and inefficiencies. This paper introduces the Artificial Intelligence Well Trajectory Builder (AIWT), a pioneering AI-driven approach to simultaneous multi-objective optimization of well paths designed for the challenging Caspian Sea offshore drilling environment. The AIWT framework leverages advanced Artificial Intelligence (AI) techniques like Genetic Algorithms (GA) and real-time optimization capabilities for rapid and precise trajectory planning. The AIWT integrates critical engineering constraints, such as maximum inclination, azimuth, dogleg severity (DLS), fault angles, and section lengths, into the optimization process. Furthermore, it can incorporate any risk factor, such as wellbore tortuosity, sections of high stuck pipe, or fluid loss, to ensure the generated trajectories are safe and operationally efficient. Unlike traditional methods that rely on manual iterations, AIWT explores the entire design space, offering trajectories that balance reservoir access, drilling constraints, and completion practices. A key feature of AIWT is its collaborative framework, which enables seamless integration of insights from the Drilling, Reservoir, and Completion teams. AIWT continuously adapts to new information and evolving operational needs by incorporating multidisciplinary expertise. This collaboration ensures that trajectory designs meet reservoir objectives while adhering to physical and safety constraints. AIWT’s ability to generate optimized trajectories in less than an hour significantly reduces planning time, enabling near real-time trajectory construction for multiple wells during drilling campaigns. Results from implementing AIWT in the Caspian Sea offshore field demonstrated efficiency improvements, especially when the well path was reduced by >5%, reducing the drilling operation cost. In addition, the AIWT system reduces trajectory planning time by up to 80%. While its multi-objective optimization capabilities enabled the generation of robust well paths, taking into consideration all the objectives and constraints simultaneously with highly interactive near-real-time optimization and, at the same time, honoring adding new constraints and new targets without limitation in a way that provides a creative tool in the hand of reservoir, drilling, and completion engineers to keep enhancing the trajectories without losing historical rules and settings. This paper highlights AIWT as a novel and transformative tool for well-trajectory optimization. By addressing the limitations of traditional manual planning methods and integrating advanced AI capabilities, AIWT represents a step-change in precision, efficiency, and risk management. The system sets a new standard for offshore well trajectory planning and provides a scalable framework for future global applications in complex field developments.