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arXiv Machine Learning By Sergey Alyaev, Kristian Fossum, Hibat Errahmen Djecta, Jan Tveranger, Ahmed H. Elsheikh

A Generative-AI Modeling Framework for Explainable Decision Support in Complex Geosteering Scenarios

Read the original on arXiv Machine Learning →

The paper presents a real‑time, AI‑driven geosteering workflow that combines Generative Adversarial Networks for geological parameterization, ensemble methods for model updating, and dynamic programming optimization for decision support during directional drilling. The framework uses offline GAN training to generate realistic geology realizations and a Forward Neural Network to predict Logging‑While‑Drilling tool responses, enabling progressive reduction of subsurface uncertainty around the drilling bit. Tested on a low‑net‑to‑gross drilling scenario, the prototype delivers steering recommendations and automatically maps formation boundaries along the well path.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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