A Decision Support System for Multi-target Geosteering
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Abstract (arXiv)
Geosteering is a sequential decision process under uncertainty. The goal of geosteering is to maximize the expected value of the well, which should be defined by an objective value-function for each operation. In this paper we present a real-time decision support system (DSS) for geosteering that aims to approximate the uncertainty in the geological interpretation with an ensemble of geomodel realizations. As the drilling operation progresses, the ensemble Kalman filter is used to sequentially update the realizations using the measurements from real-time logging while drilling. At every decision point a discrete dynamic programming algorithm computes all potential well trajectories for the entire drilling operation and the corresponding value of the well for each realization. Then, the DSS considers all immediate alternatives (continue/steer/stop) and chooses the one that gives the best predicted value across the realizations. This approach works for a variety of objectives and constraints and suggests reproducible decisions under uncertainty. Moreover, it has real-time performance. The system is tested on synthetic cases in a layer-cake geological environment where the target layer should be selected dynamically based on the prior (pre-drill) model and the electromagnetic observations received while drilling. The numerical closed-loop simulation experiments demonstrate the ability of the DSS to perform successful geosteering and landing of a well for different geological configurations of drilling targets. Furthermore, the DSS allows to adjust and reweight the objectives, making the DSS useful before fully automated geosteering becomes reality.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Mar 2019.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 1; 2024: 1; 2025: 1; 2026: 1.
8 papers.
- 2021Direct multi-modal inversion of geophysical logs using deep learningcited by 2
- 2022Probabilistic forecasting for geosteering in fluvial successions using a generative adversarial networkcited by 2
- 2023Optimal Sequential Decision-Making in Geosteering: A Reinforcement Learning Approachcited by 2
- 2024High-Precision Geosteering via Reinforcement Learning and Particle Filterscited by 2
- 2021Deep learning for prediction of complex geology ahead of drillingcited by 1
- 2025A Generative-AI Modeling Framework for Explainable Decision Support in Complex Geosteering Scenarioscited by 1
- 2021Design of borehole resistivity measurement acquisition systems using deep learningcited by 0
- 2026Decision-Driven Geosteering Under Uncertainty: A Unified Framework for Sequential Decision Optimizationcited by 0
- 2026Decision-Driven Geosteering Under Uncertainty: A Unified Framework for Sequential Decision Optimization15 Jun 2026
- 2025A Generative-AI Modeling Framework for Explainable Decision Support in Complex Geosteering Scenarios11 Mar 2025
- 2024High-Precision Geosteering via Reinforcement Learning and Particle Filters9 Feb 2024
- 2023Optimal Sequential Decision-Making in Geosteering: A Reinforcement Learning Approach7 Oct 2023
- 2022Probabilistic forecasting for geosteering in fluvial successions using a generative adversarial network4 Jul 2022
- 2021Direct multi-modal inversion of geophysical logs using deep learning29 Nov 2021
- 2021Deep learning for prediction of complex geology ahead of drilling6 Apr 2021
- 2021Design of borehole resistivity measurement acquisition systems using deep learning12 Jan 2021