Optimization of Pressure Management Strategies for Geological CO2 Sequestration Using Surrogate Model-based Reinforcement Learning
CoRR(2024)
摘要
Injecting greenhouse gas into deep underground reservoirs for permanent
storage can inadvertently lead to fault reactivation, caprock fracturing and
greenhouse gas leakage when the injection-induced stress exceeds the critical
threshold. Extraction of pre-existing fluids at various stages of injection
process, referred as pressure management, can mitigate associated risks and
lessen environmental impact. However, identifying optimal pressure management
strategies typically requires thousands of full-order simulations due to the
need for function evaluations, making the process computationally prohibitive.
This paper introduces a novel surrogate model-based reinforcement learning
method for devising optimal pressure management strategies for geological CO2
sequestration efficiently. Our approach comprises two steps. Firstly, a
surrogate model is developed through the embed to control method, which employs
an encoder-transition-decoder structure to learn latent dynamics. Leveraging
this proxy model, reinforcement learning is utilized to find an optimal
strategy that maximizes economic benefits while satisfying various control
constraints. The reinforcement learning agent receives the latent state space
representation and immediate reward tailored for CO2 sequestration and choose
real-time controls which are subject to predefined engineering constraints in
order to maximize the long-term cumulative rewards. To demonstrate its
effectiveness, this framework is applied to a compositional simulation model
where CO2 is injected into saline aquifer. The results reveal that our
surrogate model-based reinforcement learning approach significantly optimizes
CO2 sequestration strategies, leading to notable economic gains compared to
baseline scenarios.
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