Exploration demonstrates that multi-agent systems improve production scheduling and reservoir simulation scalability, suggesting efficiency in resource management.
This paper explores the integration of multi-agent system (MAS) models into reservoir characterization and production prediction to address limitations of conventional deterministic and data-driven approaches. A hybrid workflow combining multiagent reinforcement learning (MARL), reservoir simulation, and ensemble-based history matching was developed, where autonomous agents representing distinct reservoir processes collaborated via a centralized reward mechanism. Geological agents interpreted seismic and well-log data to generate stochastic property distributions, while engineering agents optimized production schedules using Monte Carlo-derived uncertainty scenarios. MAS resolved conflicting data interpretations via adaptive negotiation; decentralized decision-making enabled faster response to real-time sensor updates and scalability improved with reservoir complexity. MAS framework explicitly tailored for integrated characterization and prediction, it offers novel insights for deploying adaptive AI in autonomous reservoir management, with open-source implementation and benchmarks provided for intelligent digital twin research.
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Wang et al. (2025) studied this question.
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