{"title":"Darcy-scale digital core models for rock properties upscaling and computational domain reduction","authors":"Denis Orlov, Batyrkhan Gainitdinov, Dmitry Koroteev","doi":"10.1016/j.jocs.2025.102715","DOIUrl":null,"url":null,"abstract":"<div><div>The rapid development of Digital Rock Physics (DRP) requires the elaboration of robust techniques for closing the gaps between different scales of rock studies (upscaling). The upscaling workflows are especially needed to support the applicability of DRP for heterogeneous rocks. Basically, DRP involves two primary stages: model construction and simulation of physical processes on the models created. For heterogeneous rocks, there is an inherent trade-off between the spatial resolution of the data and the representativeness of the model size. The primary objective of this study was to implement and test a technique for upscaling digital core models from microscale to macroscale, enabling the computation of rock properties while accounting for heterogeneity of various scales. The upscaling is based on establishing correlations between tomography data of different resolutions and transforming low-resolution tomography into a multi-class model according to the defined correlation. The convolutional neural network for high-resolution tomography data was considered as the optimal algorithm for transforming low-resolution tomography into a multi-class model. The output of the neural network was an upscaled model of lower resolution than the original tomography image. Each cell in the upscaled model belonged to one of several types of formation, whose generalized characteristics were determined on the basis of the analysis of high-resolution tomography data. To validate the upscaling technique, we constructed a digital model of a complex carbonate reservoir based on data from multi-scale microtomography (<span><math><mi>μ</mi></math></span>CT). A Darcy-scale model has been used and validated as a multi-class model, enabling the computation of flows in pore samples of various scales. By incorporating diverse pore space structures as different classes in the Darcy-scale model, it is possible to preserve the substantial physical size of the model while enhancing its level of complexity.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102715"},"PeriodicalIF":3.7000,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Science","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1877750325001929","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
The rapid development of Digital Rock Physics (DRP) requires the elaboration of robust techniques for closing the gaps between different scales of rock studies (upscaling). The upscaling workflows are especially needed to support the applicability of DRP for heterogeneous rocks. Basically, DRP involves two primary stages: model construction and simulation of physical processes on the models created. For heterogeneous rocks, there is an inherent trade-off between the spatial resolution of the data and the representativeness of the model size. The primary objective of this study was to implement and test a technique for upscaling digital core models from microscale to macroscale, enabling the computation of rock properties while accounting for heterogeneity of various scales. The upscaling is based on establishing correlations between tomography data of different resolutions and transforming low-resolution tomography into a multi-class model according to the defined correlation. The convolutional neural network for high-resolution tomography data was considered as the optimal algorithm for transforming low-resolution tomography into a multi-class model. The output of the neural network was an upscaled model of lower resolution than the original tomography image. Each cell in the upscaled model belonged to one of several types of formation, whose generalized characteristics were determined on the basis of the analysis of high-resolution tomography data. To validate the upscaling technique, we constructed a digital model of a complex carbonate reservoir based on data from multi-scale microtomography (CT). A Darcy-scale model has been used and validated as a multi-class model, enabling the computation of flows in pore samples of various scales. By incorporating diverse pore space structures as different classes in the Darcy-scale model, it is possible to preserve the substantial physical size of the model while enhancing its level of complexity.
期刊介绍:
Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory.
The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation.
This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods.
Computational science typically unifies three distinct elements:
• Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous);
• Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems;
• Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).