Related papers: High-Precision Geosteering via Reinforcement Learning and Particle Filters

High-Precision Geosteering via Reinforcement Learning and Particle Filters

URL: http://arxiv.org/abs/2402.06377v1
Date: Fri, 9 Feb 2024 12:54:34 GMT
Title: High-Precision Geosteering via Reinforcement Learning and Particle Filters
Authors: Ressi Bonti Muhammad, Apoorv Srivastava, Sergey Alyaev, Reidar Brumer Bratvold, Daniel M. Tartakovsky
Abstract summary: Geosteering is a key component of drilling operations and traditionally involves manual interpretation of various data sources such as well-log data. Academic attempts to solve geosteering decision optimization with greedy optimization and Approximate Dynamic Programming (ADP) showed promise but lacked adaptivity to realistic diverse scenarios. We propose Reinforcement learning (RL) to facilitate optimal decision-making through reward-based iterative learning.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Geosteering, a key component of drilling operations, traditionally involves manual interpretation of various data sources such as well-log data. This introduces subjective biases and inconsistent procedures. Academic attempts to solve geosteering decision optimization with greedy optimization and Approximate Dynamic Programming (ADP) showed promise but lacked adaptivity to realistic diverse scenarios. Reinforcement learning (RL) offers a solution to these challenges, facilitating optimal decision-making through reward-based iterative learning. State estimation methods, e.g., particle filter (PF), provide a complementary strategy for geosteering decision-making based on online information. We integrate an RL-based geosteering with PF to address realistic geosteering scenarios. Our framework deploys PF to process real-time well-log data to estimate the location of the well relative to the stratigraphic layers, which then informs the RL-based decision-making process. We compare our method's performance with that of using solely either RL or PF. Our findings indicate a synergy between RL and PF in yielding optimized geosteering decisions.