Enhanced prediction of S-wave velocity and geomechanical properties using depth-ordered recurrent neural networks — A case study in West Texas

ABSTRACT Because pore fluids have less influence on shear waves, S-wave velocity (VS) directly measures a rock frame’s stiffness. However, direct VS measurements are scarce in the vintage wells of the Delaware Basin. Although empirical and traditional machine-learning methods have been used to predict VS from conventional well logs, their estimates often depend on specific geologic formations or boundary conditions. In this study, we develop a self-attention, bidirectional long short-term memory (BiLSTM) model with depth-ordered sequences and automated hyperparameter optimization to overcome these limitations, requiring no prior geologic information. Trained on P-wave velocity, density, total porosity, and gamma-ray logs from 123 wells, the model captures nonlinear relationships in the data, achieving an R2 of 0.85 and surpassing existing empirical and regression-based approaches. Further validation in blind tests with eight wells in different counties demonstrates accurate VS predictions throughout the basin where direct VS logs are missing. To interpret the model’s “black box,” we compute Shapley values to quantify each input’s contribution to VS predictions. In addition, the BiLSTM’s superior performance extends to geomechanical properties, such as bulk modulus (K), shear modulus (G), Young’s modulus (E), and Poisson’s ratio (ν), highlighting its practical utility in seismic applications such as energy exploration and development, geothermal energy, carbon and hydrogen storage, and induced seismicity monitoring.