A study on the rapid evaluation of injection-production capacity for depleted gas reservoir UGS by PI-GNN

Predicting the injection-production capacity of depleted gas reservoir underground gas storage is schallenging due to the difficulties in combining complex inter-well dynamic coupling with physical consistency, alongside the poor computational efficiency of traditional mechanistic models. To address these issues, a physics-informed graph neural network (PI-GNN) model is proposed, integrating well pattern topological features with the Law of Conservation of Mass. In this approach, injection-production wells are abstracted as graph nodes and gathering pipeline connections as edges to construct a spatial graph structure reflecting the real production topology. Furthermore, hierarchical mass conservation constraints are incorporated into the network loss function to ensure that predictions strictly adhere to the total injection-production balance. Experimental results based on historical dynamic data from the Wei 11 gas storage in Zhongyuan Oilfield demonstrate that, compared to conventional numerical simulations and data-driven models, PI-GNN is about a thousand times faster in computation while maintaining high predictive accuracy. Moreover, it exhibits superior physical rationality and generalization across diverse injection-production scenarios. This research provides a novel pathway for developing high-fidelity, interpretable digital twins and rapid evaluation of gas storage facilities. Ultimately, this helps operators respond faster when gas demand is high.