# High-Precision Geosteering via Reinforcement Learning and Particle Filters

arXiv: https://arxiv.org/abs/2402.06377 · HTML: https://mlsanity.com/paper/2402.06377/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 9 Feb 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 9 of 9)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Decision Support System for Multi-target Geosteering](https://mlsanity.com/paper/1903.03933/) (2019)
- [Error Control and Loss Functions for the Deep Learning Inversion of Borehole Resistivity Measurements](https://mlsanity.com/paper/2005.08868/) (2020)
- [A Deep Learning Approach to the Inversion of Borehole Resistivity Measurements](https://mlsanity.com/paper/1810.04522/) (2018)
- [Inversion of 1D frequency- and time-domain electromagnetic data with convolutional neural networks](https://mlsanity.com/paper/1912.00612/) (2019)
- [An interactive sequential-decision benchmark from geosteering](https://mlsanity.com/paper/2011.00733/) (2020)
- [Probabilistic model-error assessment of deep learning proxies: an application to real-time inversion of borehole electromagnetic measurements](https://mlsanity.com/paper/2205.12684/) (2022)
- [Direct multi-modal inversion of geophysical logs using deep learning](https://mlsanity.com/paper/2201.01871/) (2021)
- [Optimal Sequential Decision-Making in Geosteering: A Reinforcement Learning Approach](https://mlsanity.com/paper/2310.04772/) (2023)

## Built on by (most cited first, 2 of 2)

- [A Generative-AI Modeling Framework for Explainable Decision Support in Complex Geosteering Scenarios](https://mlsanity.com/paper/2503.08509/) (2025)
- [Decision-Driven Geosteering Under Uncertainty: A Unified Framework for Sequential Decision Optimization](https://mlsanity.com/paper/2606.17331/) (2026)

## Abstract (arXiv)

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.