Abstract Pore structure is a crucial factor affecting the physical properties of porous materials, and understanding the mechanisms and laws of these effects is of great significance in the fields of geosciences and petroleum engineering. However, it remains a challenge to accurately understand and quantify the relationship between pore structures and effective properties. This paper improves a workflow to focus on investigating the effect of pore structure on physical properties. First, a hybrid modeling approach combining process-based and morphology-based methods is proposed to reconstruct 3D models with diverse pore structure types. Then, the characteristics and differences in pore structure in these models are compared. Finally, the variation laws and pore-scale mechanisms of the influence of pore structure on physical properties (permeability and elasticity) are discussed based on the reconstructed models. The relationship models between pore structure parameters and permeability/elastic parameters in the grain packing model are established. The effect of pore structure evolution on permeability/elasticity and the microscopic mechanism in three types of morphology-based reconstruction models are explored. The influence degree of pore structure on elastic parameters (bulk modulus, shear modulus, P-wave velocity, and S-wave velocity) is quantified, reaching 29.54%, 51.40%, 18.94%, and 23.18%, respectively. This work forms a workflow for exploring the relationship between pore structures and petrophysical properties at the microscopic scale, providing more ideas and references for understanding the complex physical properties in porous media. 

Modeling and petrophysical properties of digital rock models with various pore structure types: An improved workflow

To accurately model and analyze various phenomena in porous media, it is necessary to have three-dimensional (3D) microstructure samples that genuinely represent the key characteristics of the material being studied. Although modern imaging methods such as X-ray computer tomography have enabled the extraction of 3D images of pore space, obtaining 3D microstructure images remains a costly and time-consuming process. Therefore, it is becoming more crucial to use image reconstruction techniques to create new realizations of microstructures. Machine learning (ML) techniques have been increasingly used to generate porous microstructures in recent years. This article reviews some of the most promising studies that have introduced such ML-based reconstruction techniques. We categorize the existing approaches into different types while highlighting their key characteristics, advantages, and disadvantages. Furthermore, this paper provides information on various methods for evaluating the performance of the reconstruction algorithms. We also discuss the current research status of ML-assisted porous media reconstruction in energy-related applications, namely, energy storage systems and digital rock reconstruction. Despite their success and rapid progress over the past decade, ML-based approaches, particularly generative deep neural networks, still suffer from issues that impede their further applications. The paper elaborates on some of the most important limitations and current challenges of these approaches while indicating potential research areas for future studies. 

Minireview on Porous Media and Microstructure Reconstruction Using Machine Learning Techniques: Recent Advances and Outlook

Understanding the Mechanism of Hydraulic Fracturing in Naturally Fractured Carbonate Reservoirs: Microseismic Monitoring and Well Testing

The scientific and engineering challenges of research on porous media have gained substantial attention in recent decades. These intricate issues span different disciplines and fields, manifesting in natural and industrial systems like soils, oil and gas reservoirs, tissues, plants, etc. Meanwhile, digital core analysis technology has rapidly developed, proving invaluable not just in oil and gas reservoirs development, but also in geothermal energy, carbon and hydrogen storage. The China InterPore Chapter and the Research Center of Multiphase Flow in Porous Media at China University of Petroleum (East China) have established a conference platform for global scholars to exchange ideas and research in porous media utilizing digital core analysis technology. The 6th International Conference on Digital Core Analysis &amp; the 2023 China Interpore Conference on Porous Media was successfully held in Qingdao from July 5 to 7, 2023. The conference facilitated discussions among 150 participants, including over 20 invited experts from academia and industry, and the recent advances in research of fluid flow in porous media using digital core analysis technology were thoroughly presented. Document Type: Editorial Cited as: Yang, Y., Horne, R. N., Cai, J., Yao, J. Recent advances on fluid flow in porous media using digital core analysis technology. Advances in Geo-Energy Research, 2023, 9(2): 71-75. https://doi.org/10.46690/ager.2022.08.01 

Recent advances on fluid flow in porous media using digital core analysis technology

The advancements in hydraulic fracturing and horizontal drilling techniques have substantially facilitated the large-scale extraction of natural gas from shale gas reservoirs. However, the use of fracking water poses several potential drawbacks including the contamination of groundwater, surface water, and soil, as well as risks to air quality. Due to the unique physical properties of supercritical CO2, shale gas exploitation using this method has been considered a promising technology that can not only improve gas recovery but can also enable CO2 geological storage. This paper clarifies the gas adsorption mechanism in shale formations, including the factors influencing the adsorption of CH4, the differences between CH4 and CO2 adsorption, and various adsorption models. We show that shale inherently exhibits a preference for CO2 adsorption over CH4. Then, the supercritical CO2 fracturing mechanism, including the shale fracking pressure and the factors influencing CO2 fracturing, is analyzed. The mechanisms of CO2 extraction in shale gas and the key factors influencing CO2 geological storage are discussed. The main challenges and future prospects regarding the use of supercritical CO2 for shale gas recovery and geological sequestration in gas shale reservoirs are finally summarized. A more detailed understanding is required to evaluate the efficiency of shale gas recovery and CO2 geological sequestration in shale formations using supercritical CO2. This work provides a basis and serves as a reference for future research investigating the mechanisms of shale gas exploitation using supercritical CO2, as well as the limitations and advantages of CO2 geological sequestration in unconventional shale gas reservoirs. 

Advances and Prospects of Supercritical CO2 for Shale Gas Extraction and Geological Sequestration in Gas Shale Reservoirs

The complex and multiscale nature of shale gas transport imposes new challenges to the already well-developed techniques for conventional reservoirs, especially digital core analysis. Multiscale complicated pore systems and distinctive properties limit most reconstruction methods not applicable. High-precision imaging experiments play a key role in the characterization of pore structures and mineral components. While the exhilarating breakthroughs in physical experimental methods and hybrid superposition methods have made significant achievements in shale digital rock reconstruction, rapidly evolving deep learning methods also present a promising option. Benefiting from the digital rock techniques, the pore-scale flow of shale gas can be directly simulated based on digital rock or indirectly modeled using the pore network model. It is precise and realistic to investigate the shale gas flow at the pore scale considering the desorption, surface diffusion, and slippage in nanopores. In this paper, we reviewed the recent advances in off-mentioned methods and processes and presented a hand for the research in this field. 

Recent Advances in Multiscale Digital Rock Reconstruction, Flow Simulation, and Experiments during Shale Gas Production

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Recent Advances in Multiscale Digital Rock Reconstruction, Flow Simulation, and Experiments during Shale Gas Production