Brownian Bridge Augmented Surrogate Simulation and Injection Planning for Geological CO2 Storage

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Publication Date: 1/22/2026

Event: The 40th Annual AAAI Conference on Artificial Intelligence (AAAI-26)

Reference: pp. 1-13, 2026

Authors: Haoyue Bai, Arizona State University; Guodong Chen, Arizona State University; Wangyang Ying, Arizona State University; Xinyuan Wang, Arizona State University; Nanxu Gong, Arizona State University; Sixun Dong, Arizona State University; Giulia Pedrielli, Arizona State University; Haoyu Wang, NEC Laboratories America, Inc.; Haifeng Chen, NEC Laboratories America, Inc.; Yanjie Fu, Arizona State University

Abstract: Geological CO2 storage (GCS) involves injecting captured CO2 into deep sub-surface formations to support climate goals. The effective management of GCS relies on adaptive injection planning to dynamically control injection rates and well pressures to balance both storage safety and efficiency. Prior literature, including numerical optimization methods and surrogate-optimization methods, is limited by real-world GCS requirements of smooth state transitions and goal-directed planning within limited time. To address these limitations, we propose a Brownian Bridge — augmented framework for surrogate simulation and injection planning in GCS and develop two insights (i) Brownian bridge as smooth state regularizer for better surrogate simulator; (ii) Brownian bridge as goal-time-conditioned planning guidance for better injection planning. Our method has three stages: (i) learning deep Brownian bridge representations with contrastive and reconstructive losses from historical reservoir and utility trajectories, (ii) incorporating Brownian bridge-based next state interpolation for simulator regularization (iii) guiding injection planning with Brownian utility-conditioned trajectories to generate high-quality injection plans. Experimental results across multiple datasets collected from diverse GCS settings demonstrate that our framework consistently improves simulation fidelity and planning effectiveness while maintaining low computational overhead.

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