Predicting shale mineralogical brittleness index from seismic and elastic property logs using interpretable deep learning

The mineralogical brittleness index (MBI) of organic-rich shale formations is one of the key parameters to identify the optimal production well locations and optimize hydraulic fracturing. Since we as a community don't understand the exact physical relationship between the MBI and seismic properties from well logs, we have used traditional approaches like the log-based brittleness index (LBI) and the elastic brittleness index (EBI) to quantify the rock brittleness from seismic data and well logs. The LBI method is easy to use but is empirically derived from the porosity and sonic logs. On the other hand, the EBI method is dependent on the average values of Young's modulus and Poisson's ratio but is not physically meaningful in practice. Therefore, we develop a deep learning approach to obtain a more reliable MBI model from seismic properties and enhance the interpretability with Shapley values. First, we analyze the statistical relationship between the MBI and eight seismic properties from well logs and distinguish the influential input variables for the MBI prediction, such as bulk density, Young's modulus, and Poisson's ratio. Second, we find a multivariate linear regression (MLR) model with three input properties and quantify the relative statistical contribution of each input based on Shapley values. Third, we use a deep neural network technique to derive the nonlinear estimation model with a better fit to the MBI data than the traditional methods. We test and verify our approach on field log and core data from the Wolfcamp shales in the Permian Basin, Texas. In conclusion, this workflow can provide a more interpretable and accurate MBI estimation from seismic properties to enhance unconventional shale reservoir characterization.