Classification of Digital Rocks by Machine Learning to Discover Micro-to-Macro Relationships and Quantify Their Uncertainty

Recent advances in high-resolution imaging of porous materials have led to a dramatic increase in the collection of digital subsurface rock samples and have stimulated the development of a capability to model the rock microstructures and to calculate macro-scale transport, mechanical and acoustic properties by numerical simulations. These lead to a modelling approach that offers the potential for greatly expanding our database of material properties, without relying on expensive, or in some cases, impossible, laboratory measurements. It is envisaged by many that this approach, when augmented with validation lab measurements and micro-scale physics of concern, can be extended to discover predictive relationships between micro-scale arrangements of voids/solids and macro-scale properties, along with quantification of their uncertainty. This approach offers a potential solution to many applications where the properties of key types of rocks must be estimated from few samples. Waste disposal, CO2 storage and hydrate exploration in the subsurface are good examples within the NERC remit. In those applications, fine-grained rocks are of key concern, since they are assumed to function as barriers preventing substances from escaping into the atmosphere/biosphere. Unfortunately, such materials are expensive to sample and extremely difficult and costly to measure in the laboratory. Hence, an ability to predict fine-grained rock properties reliably and robustly would enable better modelling of macro-scale physical behaviours, assessment of the uncertainty of the behaviours, and understanding of the impacts of such applications to environment and public health. A micro-to-macro predictive relationship is expected to be highly non-linear when the physics becomes complex. Our preliminary investigations [1] on 3D micro images shows that even a single-phase flow property, like permeability, shows a strong non-linear correlation with the geometric and topological features (fig.1). Moreover, a robust non-linear relationship has to be identified from a large collection of samples and validated against new samples. Machine Learning (ML) provides a framework to carry out an automated process in which the knowledge of non-linear relationships can be learnt progressively from the growing collection of samples in a self-supervised manner. Such a process suits this purpose but must be underpinned by a set of smart and efficient tools for data search and retrieval, data-analysis, and data-mining. A basis on which all these tools are based is the ability to classify digital rock samples according to the diverse features of their microstructures as well as measured and/or calculated properties. The objective of this project is to explore the feasibility of constructing a suite of feature-based, content-aware and self-supervised ML classification techniques for digital rocks, within the NERC topic of environmental informatics. This will produce a ML system capable of classifying digital rock samples and macro-scale properties according to pre-defined controlling features. Ultimately, knowledge and experience gained from this pilot project will enable PIs to make fuller proposals to develop a suite of ML-based technologies for identifying predictive relationships between micro- and macro-scale features and predicting macro-scale properties. There is a scope for extending the technologies to other types of natural porous media and impacting across industries and research communities to address engineering and scientific questions about the physical properties of porous materials.