ECHO: A Hybrid Neural Operator For Coupled Thermo-Hydro-Mechanical Modelling In Enhanced Geothermal Systems

Coupled thermo-hydro-mechanical (THM) modelling is central to geothermal reservoir simulation, but resolving these interactions with high-fidelity numerical simulators is computationally expensive. Existing geomechanical surrogates reduce this cost but do not explicitly account for the distinct spatial and temporal behaviour of discrete fracture networks. We propose ECHO (Embedded Coupled Hybrid Operator), a hybrid surrogate that combines a Fourier neural operator (FNO) for reservoir-scale geomechanical prediction, a residual convolutional network for local near-well correction, and a graph-convolutional recurrent network (GCN–GRU) for fracture-specific dynamics. Evaluated on the Milford coupled reservoir–geomechanics dataset against a 3D U-Net, a full-grid GCN, DeepONet, a standard FNO, and U-FNO, ECHO obtains the lowest free-running fracture-response error in every evaluated operating and temporal regime, reducing elastic-dilation RMSE by 55.5–82.4% and fracture-normal-stress RMSE by 49.0–64.3% relative to the strongest competing architecture, while reaching $R^2>0.99$ on both targets. The evaluation considers held-out operating and material-parameter settings within a single fixed reservoir geometry and fracture network, and therefore measures interpolation within this geological configuration rather than generalisation to unseen geometries. Controlled ablations show that temporal recurrence is the most consistently beneficial architectural component, whereas graph connectivity and message passing contribute more conditionally across regimes. The complete surrogate runs in 6.6 ms per coupling transition, corresponding to a measured $4.1\times 10^3$ replacement-runtime ratio relative to the reference finite-element geomechanics solve, and is integrated with a commercial reservoir simulator through a two-way coupling interface. Within the evaluated setting, these results support representation-specific neural surrogates as an effective approach for accelerating coupled physical simulation when a domain contains sub-regions with qualitatively different spatial structure.