Nicolás Javier Cárdenas Pantoja
PhD thesis title: Deep Reinforcement Learning for Field Development Planning in Oil & Gas Projects
Academic Tutor: Francesco Di Maio
Academic Supervisor: Enrico Zio and Piero Baraldi
Affiliate external company or research group: ENI
PhD cycle: 38° (see all student profiles of the same cycle > LINK)
BSc: Industrial Engineering, Universidad Técnica Federico Santa María
MSc: Industrial Engineering, Universidad Técnica Federico Santa María
Thesis abstract
This project explores Deep Reinforcement Learning (Deep RL) strategies for Field Development Optimization. Leveraging Deep RL, the research focuses on optimizing well trajectories, placement, and drilling schedules in uncertain geological environments. Deep RL is capable of processing constraints and uncertainties to enhance decision-making in oil field development. By employing innovative algorithmic approaches and a benchmark dataset, the thesis demonstrates how Deep RL can significantly improve field development plans, yielding higher economic returns and operational efficiency.
Personal interest in my research theme
My interest towards researching Field Development Optimization using Deep RL stems from a profound interest in leveraging artificial intelligence to tackle complex, real-world problems. Deep RL presents a powerful set of tools to address the uncertainties in oil field development, optimizing well placement, and enhancing operational efficiencies. This research area aligns with my keen interest in machine learning techniques and my commitment to advancing intelligent systems, aiming to significantly improve strategic decisions and economic outcomes in the energy sector.
