A connectivity-guided phase-aware graph-temporal surrogate for CO₂ flooding and geological storage optimization

Abstract

Efficient optimization of carbon dioxide (CO₂) flooding and geological storage is constrained by the high computational cost of compositional simulation and the limited capability of conventional surrogates to represent inter-well coupling and phase-dependent dynamics. This study develops a connectivity-guided phase-aware graph-temporal surrogate that explicitly embeds reservoir connectivity into Topology Adaptive Graph Convolution (TAGConv), couples graph message passing with recurrent temporal modeling, and introduces phase-specific prediction branches with reservoir-behavior-inspired temporal regularization. Unlike conventional surrogate models that mainly learn temporal or input–output mappings, the proposed framework jointly represents inter-well spatial dependency, breakthrough-sensitive multiphase dynamics, and optimization-oriented reservoir responses. Multi-seed comparisons show that the proposed model achieves the lowest active-region prediction errors for Gas, Oil, and CO₂ among the evaluated recurrent, convolutional, transformer-based, and graph-recurrent baselines, with the most evident advantage for CO₂ breakthrough dynamics. When embedded into Non-dominated Sorting Genetic Algorithm II (NSGA-II), the surrogate-assisted workflow increases cumulative oil production by approximately 7.5% and CO₂ sequestration by 4.9% relative to the baseline strategy, while providing an estimated 10.1× computational speedup over direct ECLIPSE-based optimization. High-fidelity ECLIPSE re-simulation further confirms the main response trends of the selected strategy. The framework therefore provides a connectivity-aware and phase-sensitive surrogate approach for efficient multi-objective CO₂ flooding and storage optimization.