Machine learning prediction of tortuosity in digital rock

Abstract

Physical rock property measurement is an important stage in energy exploration, both for hydrocarbons and geothermal sources. The value of physical rock properties can provide information about reservoir quality, and one of these properties is tortuosity. Tortuosity is an intrinsic property of porous materials that describes the level of complexity of the porous arrangement when a fluid passes through it. Conventionally, tortuosity values are measured through laboratory analysis and numerical simulation, but these measurements can take a long time. An alternative method for measuring tortuosity is using machine learning with a convolutional neural network (CNN). A CNN is a type of deep neural network designed to analyze multi-channel images and has been applied successfully to classification and non-linear regression problems. By training a CNN on a dataset of digital rock samples that have been simulated using numerical computation to obtain their tortuosity values, it is possible to demonstrate that CNNs can accurately predict the tortuosity of digital rock. The result is that the CNN model can predict tortuosity values with the Xception model being the most accurate with the lowest RMSE value of 0.90962.