Digital Rock Physics Combined with Machine Learning for Rock Mechanical Properties Characterization

2018 ◽  
Author(s):  
Bilal Saad ◽  
Ardi Negara ◽  
Shujath Syed Ali
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Rock Physics ◽  
Digital Rock Physics ◽  
Digital Rock ◽  
Mechanical Properties Characterization ◽  
Rock Mechanical Properties ◽  
Properties Characterization

scholarly journals Construction of pore structure and lithology of digital rock physics based on laboratory experiments

2021 ◽  
Vol 11 (5) ◽  
pp. 2113-2125
Author(s):  
Chenzhi Huang ◽  
Xingde Zhang ◽  
Shuang Liu ◽  
Nianyin Li ◽  
Jia Kang ◽  
...  
Keyword(s):  
Pore Structure ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Laboratory Experiments ◽  
Rock Physics ◽  
Reconstruction Method ◽  
Digital Rock Physics ◽  
Digital Rock ◽  
The Core

AbstractThe development and stimulation of oil and gas fields are inseparable from the experimental analysis of reservoir rocks. Large number of experiments, poor reservoir properties and thin reservoir thickness will lead to insufficient number of cores, which restricts the experimental evaluation effect of cores. Digital rock physics (DRP) can solve these problems well. This paper presents a rapid, simple, and practical method to establish the pore structure and lithology of DRP based on laboratory experiments. First, a core is scanned by computed tomography (CT) scanning technology, and filtering back-projection reconstruction method is used to test the core visualization. Subsequently, three-dimensional median filtering technology is used to eliminate noise signals after scanning, and the maximum interclass variance method is used to segment the rock skeleton and pore. Based on X-ray diffraction technology, the distribution of minerals in the rock core is studied by combining the processed CT scan data. The core pore size distribution is analyzed by the mercury intrusion method, and the core pore size distribution with spatial correlation is constructed by the kriging interpolation method. Based on the analysis of the core particle-size distribution by the screening method, the shape of the rock particle is assumed to be a more practical irregular polyhedron; considering this shape and the mineral distribution, the DRP pore structure and lithology are finally established. The DRP porosity calculated by MATLAB software is 32.4%, and the core porosity measured in a nuclear magnetic resonance experiment is 29.9%; thus, the accuracy of the model is validated. Further, the method of simulating the process of physical and chemical changes by using the digital core is proposed for further study.

Strengthening the Digital Rock Physics, Using Downsampling for Sub-Resolved Pores in Tight Sandstones

2021 ◽  
pp. 103869
Author(s):  
Mohammad Ebadi ◽  
Denis Orlov ◽  
Ivan Makhotin ◽  
Vladislav Krutko ◽  
Boris Belozerov ◽  
...  
Keyword(s):  
Rock Physics ◽  
Digital Rock Physics ◽  
Digital Rock

scholarly journals Validation of Digital Rock Physics Algorithms

Minerals ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 669
Author(s):  
Rongrong Lin ◽  
Leon Thomsen
Keyword(s):  
Rock Physics ◽  
Image Resolution ◽  
Staggered Grid ◽  
Static Properties ◽  
Shear Moduli ◽  
Digital Rock Physics ◽  
Digital Rock ◽  
Saturated Rock ◽  
Theoretical Predictions ◽  
Theoretical Test

With a detailed microscopic image of a rock sample, one can determine the corresponding 3-D grain geometry, forming a basis to calculate the elastic properties numerically. The issues which arise in such a calculation include those associated with image resolution, the registration of the digital numerical grid with the digital image, and grain anisotropy. Further, there is a need to validate the numerical calculation via experiment or theory. Because of the geometrical complexity of the rock, the best theoretical test employs the Hashin–Shtrikman result that, for an aggregate of two isotropic components with equal shear moduli, the bulk modulus is uniquely determined, independent of the micro-geometry. Similarly, for an aggregate of two isotropic components with a certain combination of elastic moduli defined herein, the Hashin–Shtrikman formulae give a unique result for the shear modulus, independent of the micro-geometry. For a porous, saturated rock, the solid incompressibility may be calculated via an “unjacketed” test, independent of the micro-geometry. Any numerical algorithm proposed for digital rock physics computation should be validated by successfully confirming these theoretical predictions. Using these tests, we validate a previously published staggered-grid finite difference damped time-stepping algorithm to calculate the static properties of digital rock models.

Digital Rock Typing DRT Algorithm Formulation with Optimal Supervised Semantic Segmentation

2022 ◽  
Author(s):  
Omar Alfarisi ◽  
Djamel Ouzzane ◽  
Mohamed Sassi ◽  
TieJun Zhang
Keyword(s):  
Carbonate Rock ◽  
Chemical Properties ◽  
Rock Physics ◽  
Semantic Segmentation ◽  
Rock Properties ◽  
Digital Rock Physics ◽  
Digital Rock ◽  
Rock Types ◽  
Fluid Saturation ◽  
Rock Typing

<p><a></a>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.<br></p>

Integration of Chemofacies and Rock Mechanical Properties Using Machine Learning Algorithms: Implications for Geomechanics and Hydraulic Fracture Stimulations in Paleozoic Formations, Saudi Arabia

2021 ◽  
Author(s):  
Maaruf Hussain ◽  
Abduljamiu Amao ◽  
Khalid Al-Ramadan ◽  
Sunday Olatunji ◽  
Ardiansyah Negara
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Saudi Arabia ◽  
Spatial Distribution ◽  
Chemical Composition ◽  
Rock Strength ◽  
Machine Learning Algorithms ◽  
Unconventional Reservoirs ◽  
Geochemical Properties ◽  
Rock Mechanical Properties

Abstract The knowledge of rock mechanical properties is critical to reducing drilling risk and maximizing well and reservoir productivity. Rock chemical composition, their spatial distribution, and porosity significantly influenced these properties. However, low porosity characterized unconventional reservoirs as such, geochemical properties considerably control their mechanical behavior. In this study, we used chemostratigraphy as a correlation tool to separate strata in highly homogenous formations where other traditional stratigraphic methods failed. In addition, we integrated the chemofacies output and reduced Young's modulus to outline predictable associations between facies and mechanical properties. Thus, providing better understanding of lithofacies-controlled changes in rock strength that are useful inputs for geomechanical models and completions stimulations.

Predictive digital rock physics without segmentation

2021 ◽  
pp. 105008
Author(s):  
Eric J. Goldfarb ◽  
Ken Ikeda ◽  
Richard A. Ketcham ◽  
Maša Prodanović ◽  
Nicola Tisato
Keyword(s):  
Rock Physics ◽  
Digital Rock Physics ◽  
Digital Rock
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