Abstract
Abstract
The limited availability of shear-wave velocity (Vs) logs in deep-water reservoirs presents a significant challenge for reliable reservoir characterization and elastic property analysis. This study aims to develop and validate a robust machine learning (ML)-based workflow for predicting Vs from conventional well logs and integrating the results into rock physics–driven reservoir characterization. The dataset comprises five wells from two geologically analogous gas-bearing marine reservoirs: four wells from the West Offshore Nile Delta, Egypt, and one well from the Scarborough Gas Field, Australia. All wells include measured Vs data, enabling a structured training and validation strategy in which three wells are used for training, and two wells are reserved for blind validation across different fields. Input logs include gamma-ray (GR), bulk density (RHOB), deep resistivity (LLD), neutron porosity (APLC), and compressional velocity (Vp). Four ensemble ML models, gradient boosting (GB), extreme gradient boosting (XGB), light gradient boosting (LGB), and categorical boosting (CB), were evaluated. The CB model achieved the best performance, with R
2
values of 0.912 for testing and 0.904 for blind validation, along with the lowest RMSE and MAPE. The predicted Vs was subsequently used to derive elastic attributes, including Lambda-Rho and Mu-Rho, and to construct rock physics templates for lithology and fluid discrimination. These elastic properties were further integrated into a three-dimensional geostatistical modelling framework using Gaussian Random Function Simulation (GRFS), constrained by seismic-derived structural surfaces, to generate volumetric distributions of porosity, acoustic impedance, and Lambda-Rho across the reservoir. The results demonstrate that the ML-predicted elastic properties consistently identify gas-bearing sands, brine sands, and shale intervals. Furthermore, the successful application of the workflow across two distinct but analogous reservoir settings highlights its robustness and transferability. This integrated ML–rock physics framework offers an effective solution to enhance reservoir characterization while reducing reliance on costly and limited Vs measurements.