Numerical Well Testing of Ultra-Deep Fault-Controlled Carbonate Reservoirs: A Geological Model-Based Approach with Machine Learning Assisted Inversion

Ultra-deep fault-controlled carbonate reservoirs exhibit strong heterogeneity, multi-scale fracture–cavity systems, and complex geological controls, which render conventional analytical well testing methods inadequate. This study proposes a geological model-based numerical well testing framework incorporating adaptive meshing, noise reduction, and machine-learning-assisted inversion. A multi-step workflow was established, including (i) single-well geological model extraction with localized grid refinement to capture near-wellbore flow behavior, (ii) pressure data denoising and preprocessing using low-pass filtering, and (iii) surrogate-assisted parameter inversion and sensitivity analysis using particle swarm optimization (PSO) to construct diagnostic type curves for different fracture–cavity control modes. The methodology was applied to different wells, yielding inverted fracture permeabilities ranging from approximately 140 to 480 mD and cavity permeabilities between about 110 and 220 mD. Results show that the numerical well testing method achieved an 85.7% interpretation accuracy, outperforming conventional approaches. Distinct parameter sensitivities were identified for single-, double-, and multi-cavity systems, providing a systematic basis for production allocation strategies. This integrated approach enhances the reliability of reservoir characterization and offers practical guidance for efficient development of ultra-deep carbonate reservoirs.