Interpreting the Subsurface Lithofacies at High Lithological Resolution by Integrating Information From Well-Log Data and Rock-Core Digital Images

Abstract

Spectral facies interpretation and classification methods have been proposed to improve the sophistication of interpretation of the subsurface heterogeneity. In the spectral facies interpretations, the intensity values of the RGB spectrum and the local entropy from rock-core digital images are used, and the results are compared to conventional electrofacies and expert petrophysical interpretations. During the classification, a practically applicable model that identifies the more detailed types of lithofacies is constructed by using a multilayer neural network model, with the interpreted spectral facies and well-log data from the corresponding depths used as response and explanatory variables, respectively. Core digital images and five types of well-log data from the Satyr 5 well in Western Australia are applied for the actual implementation. Through comparative interpretations, three spectral facies are identified as separable lithofacies (i.e., shale, shaly-sandstone, and sandstone lithofacies), which is supported by detailed HyLogger mineralogy along the tested cores. On the other hand, two electrofacies (i.e., shale-dominant and sand-dominant facies) are identified by a conventional method. In the classification based on the spectral facies, the trained multilayer neural network model showed high prediction accuracy for all the lithofacies. Based on these observations, it is confirmed that more precise lithofacies interpretation and classification can be conducted with the developed methods. The developed methods have the potential to improve subsurface characterization when high lithological resolution is essential.