Dynamic Programming

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Overview

Dynamic programming uses Bellman recurrences to break a large sequential decision or optimization problem into smaller subproblems whose solutions can be reused. Exact methods—including memoization, state-space pruning, constraint propagation, and memory-saving algorithms such as Hirschberg’s—are used in scheduling, clustering, speech alignment, and control; approximate methods use neural value functions, sampling, or Monte Carlo tree search for larger problems. Researchers evaluate these approaches on settings such as stochastic control, dynamic pricing, combinatorial benchmarks, and long speech sequences. A central tension is preserving solution quality and guarantees while controlling the time, memory, and state-space costs that can make exact methods impractical.

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31 papers1 paper in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

Mar 11, 2025

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

The real-time process of directional changes while drilling, known as geosteering, is crucial for hydrocarbon extraction and emerging directional drilling applications such as geothermal energy, civil infrastructure, and CO2 storage. The geo-energy industry seeks an automatic geosteering workflow that continually updates subsurface uncertainties and captures the latest geological understanding, informed by real-time observations. We propose a real-time, AI-driven geosteering workflow that integrates Generative Adversarial Networks (GANs) for geological parameterization, ensemble methods for model updating, and global discrete dynamic programming (DDP) optimization for complex decision-making during directional drilling operations. Our framework relies on offline training of a GAN model to reproduce relevant geology realizations and a Forward Neural Network (FNN) to model the response of Logging-While-Drilling (LWD) tools for a given geomodel. This paper introduces a first-of-its-kind workflow that progressively reduces GAN-geomodel uncertainty around and ahead of the drilling bit and adjusts the well plan accordingly. The workflow automatically integrates real-time around-bit LWD, which, through learned geological correlations, reduces uncertainty in predicted geology ahead of drilling. A DDP-based decision support system leverages probabilistic look-ahead predictions to suggest better steering strategies. We test the workflow prototype on a small yet challenging low-net-to-gross drilling scenario with several possible targets. The results show that the workflow produces meaningful steering recommendations and, through its probabilistic updates, automatically maps formation boundaries along the drilled well.

Sergey Alyaev, Kristian Fossum, Hibat Errahmen Djecta +2

NORCE Norwegian Research Centre AS, Bergen, Norway. · University of Stavanger, Stavanger, Norway. · Heriot-Watt University, Edinburgh, United Kingdom.

Dynamic Programming
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