A Novel Method for Formation Energy Assessment in Fractured-Vuggy Carbonate Reservoirs Based on the Kolmogorov-Arnold Network

Fractured-vuggy carbonate reservoirs are characterised by extreme heterogeneity induced by multi-scale pore-cave-fracture systems, which poses substantial challenges to accurate formation energy assessment—a prerequisite for optimising reservoir development strategies. Traditional methods (e.g., material balance, well-test analysis) are constrained by oversimplified pore space characterisation, failing to quantify the nonlinear impacts of multi-scale media on energy transfer and depletion. To address this critical research gap, this work develops an innovative formation energy evaluation framework that couples the Kolmogorov-Arnold Network (KAN) deep learning architecture with the Whale Optimisation Algorithm (WOA) for adaptive hyperparameter tuning. The proposed model was trained and validated using multi-source geological, engineering, and production datasets from 311 wells in the FM oilfield, Tarim Basin. Comparative results demonstrate that the WOA-KAN model achieves a 42.1% reduction in formation pressure prediction error relative to conventional neural networks, with a final coefficient of determination (R2) of 0.955. SHapley Additive exPlanations (SHAP) analysis identifies cumulative oil production, production time, and large-scale cave development index as the primary drivers of formation energy depletion, while injection-production ratio and natural fracture density exert significant positive effects on energy maintenance. This study provides a data-driven, interpretable tool for dynamic energy assessment in highly heterogeneous carbonate reservoirs, supporting the formulation of efficient energy replenishment strategies.