Intelligent classification and diagnosis of carbonate reservoir fracturing integrating GEI-Based static priors and CNN-based dynamic response recognition

Carbonate reservoirs exhibit strong heterogeneity because of their complex pore–fracture–vug systems, leading to varied hydraulic-fracturing responses. This study proposes a Geological–Engineering Index (GEI)-guided dynamic-response classification workflow using data from 56 wells in Block M. Pearson correlation and distance correlation were jointly applied to select 18 geological, logging, and engineering attributes for GEI construction. Based on the GEI distribution and supplementary geological interpretations, thresholds of 0.3 and 0.6 were adopted to classify the reservoirs as vuggy, fracture–vug, and fractured. The GEI–production relationship (R2 = 0.8679) provided an internal consistency check, while core, imaging-log, and structural evidence agreed with the GEI-defined classes for 49 of the 56 wells (87.5%). Time-synchronized tubing-pressure, casing-pressure, and pump-rate curves were mapped onto the R, G, and B channels, respectively, to construct RGB images. The dataset was partitioned at the well level into 45 training wells and 11 validation wells, with mild online augmentation applied only during training. ConvNeXt-Tiny achieved the strongest overall classification performance among the evaluated CNN backbones under the current data split. Grad-CAM++ analysis indicated recurring differences in response morphology among the three reservoir classes, with vuggy-reservoir responses tending to exhibit localized event-dominated attribution, fracture–vug responses showing multiple temporally separated response regions, and fractured-reservoir responses exhibiting more broadly distributed trend-dominated attribution. Although application to other blocks requires GEI recalibration and CNN fine-tuning, the proposed workflow provides an interpretable and practical framework for reservoir classification and fracturing-response diagnosis in heterogeneous carbonate reservoirs.