Physics-informed surrogate modeling for hydro-fracture geometry prediction based on deep learning

Hydro-fracture geometry prediction is of great practical importance for optimizing construction parameters and evaluating stimulation effects. Existing physical simulation methods are computationally intensive. Deep learning-based methods offer fast model inference, yet typically require a large amount of field data for accurate model training and lack model interpretability in explaining the complex physical processes. This work presents a physics-informed surrogate modeling method for hydro-fracture geometry prediction. The proposed method encodes the hydro-fracture physical laws, in the form of partial differential equations, as a loss term to govern the training process of the surrogate model, aiming to alleviate the data requirement for model training. Experimental studies demonstrate that the proposed modeling method effectively reduces the training data requirement and improves model accuracy and interpretability.