Driving Digital Rock towards Machine Learning: predicting permeability with Gradient Boosting and Deep Neural Networks
This work addresses permeability prediction for geoscience applications, but it is incremental as it applies existing methods to a new dataset.
The study tackled predicting permeability from 3D rock images using machine learning, achieving results that demonstrate its applicability and opening a new research area in Digital Rock.
We present a research study aimed at testing of applicability of machine learning techniques for prediction of permeability of digitized rock samples. We prepare a training set containing 3D images of sandstone samples imaged with X-ray microtomography and corresponding permeability values simulated with Pore Network approach. We also use Minkowski functionals and Deep Learning-based descriptors of 3D images and 2D slices as input features for predictive model training and prediction. We compare predictive power of various feature sets and methods. The later include Gradient Boosting and various architectures of Deep Neural Networks (DNN). The results demonstrate applicability of machine learning for image-based permeability prediction and open a new area of Digital Rock research.