# Digital Rock Typing DRT Algorithm Formulation with Optimal Supervised Semantic Segmentation

arXiv: https://arxiv.org/abs/2112.15068 · HTML: https://mlsanity.com/paper/2112.15068/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 32 references that are in the map.
- First posted on arXiv 30 Dec 2021.

## Builds on (most cited first, 2 of 2)

- [Machine Learning Guided 3D Image Recognition for Carbonate Pore and Mineral Volumes Determination](https://mlsanity.com/paper/2111.04612/) (2021)
- [Morphology Decoder: A Machine Learning Guided 3D Vision Quantifying Heterogenous Rock Permeability for Planetary Surveillance and Robotic Functions](https://mlsanity.com/paper/2111.13460/) (2021)

## Built on by (most cited first, 1 of 1)

- [3D Adapted Random Forest Vision (3DARFV) for Untangling Heterogeneous-Fabric Exceeding Deep Learning Semantic Segmentation Efficiency at the Utmost Accuracy](https://mlsanity.com/paper/2203.12469/) (2022)

## Abstract (arXiv)

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.