A Deep Shale Gas Reservoir Rock Brittleness Index Prediction Method Based on a CNN-BiGRU-Attention Hybrid Model

Hydraulic fracturing is a key technology for the commercial exploitation of deep shale gas reservoirs, and accurate prediction of rock-mechanical parameters is essential for optimizing these operations. Conventional approaches primarily rely on empirical formulas based on longitudinal and transverse wave velocities; however, obtaining transverse wave data is challenging, and these formulas often lack accuracy. Conventional machine learning algorithms also exhibit limited predictive performance and generalization due to the intrinsic heterogeneity of rock-mechanical data. Therefore, to address the extreme heterogeneity and complex nonlinear logging responses inherent in deep shale gas reservoirs in the Zigong (ZG) block, this study proposes a geology-tailored deep learning framework, CNN-BiGRU-AT. Unlike generic machine learning applications, this architecture is specifically designed to decode complex stratigraphic signals: the convolutional neural network (CNN) module extracts multi-scale spatial features to capture abrupt lithological transitions; the bidirectional gated recurrent units (BiGRUs) analyzes the continuous depth-sequential dependencies of overlying and underlying strata; and the attention mechanism (AT) dynamically regulates the weight allocation of critical input geophysical parameters, thereby delivering a geophysically informative and highly robust predictive performance. This paper employs the CNN-BiGRU-AT model to predict the Brittleness index (BI), using the ZG block as an example. The results demonstrate that the coefficient of determination (R2) for the brittleness index on the test dataset achieved 0.969, representing a 12% improvement over conventional models. The high accuracy of this model satisfies the precision requirements for predicting rock-mechanical parameters, thereby offering reliable theoretical support for optimizing hydraulic fracturing operations in deep shale gas reservoirs.