Shear-wave velocity (Vs) log is one of the essential petrophysical well logs for reservoir characterisation in oil and gas exploration. Unfortunately, only a limited number of wells have a ready-to-use shear-wave velocity log. The common way to predict Vs from a Compressional-wave velocity (Vp) log is using empirical equations such as Castagna’s mud-rock line or Greenberg-Castagna equation. However, these methods only work for a specific rock type and are inflexible as every area has a complex and unique petrophysical characteristic relationship. Therefore, the Machine Learning (ML) methods (e.g., Multiple-linear Regression, Polynomial Regression, Support Vector Regression (SVR), Decision Tree, Random Forest, and XGBoost) and the Deep Learning (DL) method (e.g., Deep Neural Network (DNN)) that are suitable for big data analysis are proposed to solve this problem. These proposed methods aim to generate a complex Vs prediction model from multiple log data that can be used for general purposes, either for shale, limestone, sandstone, or other rocks. The study shows that the DNN and XGBoost can generate Vs prediction model with a correlation up to 94% overall in the R2 metric score, better than the empirical calculation for either shale, limestone, sandstone, or other rocks.
Probabilistic neural network tomography across Grane field (North Sea) from surface wave dispersion data
