Digital Rock Typing DRT Algorithm Formulation with Optimal Supervised Semantic Segmentation

TL;DR

This paper introduces a novel digital rock typing algorithm using optimal supervised semantic segmentation, leveraging digital imaging techniques to classify rock types without destructive testing, enhancing understanding of heterogeneous carbonate textures.

Contribution

It presents a new digital rock typing process that integrates digital rock physics advances with computer vision for carbonate rocks, improving classification accuracy and insight.

Findings

Developed a digital rock typing algorithm based on semantic segmentation.

Enhanced understanding of carbonate rock heterogeneity.

Achieved non-destructive, digital classification of rock properties.

Abstract

Each grid block in a 3D geological model requires a rock type that represents all physical and chemical properties of that block. The properties that classify rock types are lithology, permeability, and capillary pressure. Scientists and engineers determined these properties using conventional laboratory measurements, which embedded destructive methods to the sample or altered some of its properties (i.e., wettability, permeability, and porosity) because the measurements process includes sample crushing, fluid flow, or fluid saturation. Lately, Digital Rock Physics (DRT) has emerged to quantify these properties from micro-Computerized Tomography (uCT) and Magnetic Resonance Imaging (MRI) images. However, the literature did not attempt rock typing in a wholly digital context. We propose performing Digital Rock Typing (DRT) by: (1) integrating the latest DRP advances in a novel process that honors digital rock properties determination, while; (2) digitalizing the latest rock typing approaches in carbonate, and (3) introducing a novel carbonate rock typing process that utilizes computer vision capabilities to provide more insight about the heterogeneous carbonate rock texture.