Morphology Decoder to Predict Heterogeneous Rock Permeability with Machine Learning Guided 3D Vision
preprintposted on 10.12.2021, 17:44 by Omar AlfarisiOmar Alfarisi, Aikifa Raza, Djamel Ouzzane, Mohamed Sassi, Hongtao Zhang, Hongxia Li, Khalil Ibrahim, Hamed Alhashmi, Hamdan Alhammadi, TieJun Zhang
Permeability has a dominant influence on the flow behavior of a natural fluid, and without proper quantification, biological fluids (Hydrocarbons) and water resources become waste. During the first decades of the 21st century, permeability quantification from nano-micro porous media images emerged, aided by 3D pore network flow simulation, primarily using the Lattice Boltzmann simulator. Earth scientists realized that the simulation process holds millions of flow dynamics calculations with accumulated errors and high computing power consumption. Therefore, accuracy and efficiency challenges obstruct planetary exploration. To efficiently, consistently predict permeability with high quality, we propose the Morphology Decoder. It is a parallel and serial flow reconstruction of machine learning-driven semantically segmented heterogeneous rock texture images of 3D X-Ray Micro Computerized Tomography (μCT) and Nuclear Magnetic Resonance (MRI). For 3D vision, we introduce controllable-measurable-volume as new supervised semantic segmentation, in which a unique set of voxel intensity corresponds to grain and pore throat sizes. The morphology decoder demarks and aggregates the morphologies' boundaries in a novel way to quantify permeability. The morphology decoder method consists of five novel processes, which we describe in this paper, these novel processes are (1) Geometrical: 3D Permeability Governing Equation, (2) Machine Learning: Guided 3D Properties Recognition of Rock Morphology, (3) Analytical: 3D Image Properties Integration Model for Permeability, (4) Experimental: MRI Permeability Imager, and (5) Morphology Decoder (the process that integrates the other four novel processes).
Email Address of Submitting Authoralfarisi@live.ca
ORCID of Submitting Author0000-0002-8669-7376
Submitting Author's InstitutionADNOC
Submitting Author's CountryCanada
machine learning-basedcomputer vision-based frameworkMRIMicro CT image3D image segmentationmorphology types3D printed objectspermeability properties3D Geometryfluid flow characteristicsgeoscience data analyticssemantic segmentationRepresentative elementary volume (REV)carbonate rockHeterogeneous porous mediacomputation efficiencymorphology decoderpore throat networkpore throat sizegrain sizegrain configurationintergranularintragranularBioclastsgrain size segmentationstatic propertiescretaceous carbonateimagingplanetary explorationearth scientistsmoon explorationMars exploration