STTR Phase I: A Reinforcement Learning-Based Automated Self-Guiding Drilling Tool

Award ID: 2108048

The broader impact/commercial potential of this Small Business Technology Transfer Program Phase I (STTR) project is to provide an environmentally friendly, sustainable, and cost-effective access to energy by improving information flow during drilling processes. The US geothermal energy industry is expected to grow by 48% to $6.8 billion by 2026 and will benefit from the significant savings of drilling activities, which currently constitute 40-60% of the costs of the entire geothermal plant and field development. In addition, oil and gas (O&G) drilling operations will benefit from faster and cleaner operations. This STTR Phase I project addresses the inability to transmit large amounts of data from downhole to the surface by performing most of the processing at the drill-bit. The proposed state-of-the-art drilling tool incorporates advanced processing power and intelligence downhole. It is built on physics-based machine learning algorithms centered on Reinforcement Learning (RL) techniques, used previously in complex problems such as autonomous vehicles and lunar landing. The system will model the highly complex drilling environment as a Markov Decision Process, then trained on supercomputing facilities and validated in a laboratory-scale drilling setup using off-the-shelf electronics. This project will lead to an automated sequential decision-making process in drilling. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Anna Brady-Estevez