Review of Data Science Trends and Issues in Porous Media Research With a Focus on Image‐Based Techniques

Rabbani, Arash; Fernando, W. Ashane M.; Shams, Reza; Singh, Ankita; Mostaghimi, Peyman; Babaei, Masoud

doi:10.1029/2020wr029472

Cited by 22 publications

(10 citation statements)

References 278 publications

(503 reference statements)

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“…Evaluating their efficacy on 3D images of rocks can critically improve digital rock physics workflows (e.g., to achieve large fields of view with high resolution or enhance fast 4D low quality scans). Although pioneering efforts have been made to apply these methods to digital rocks, they mainly focused on sedimentary rocks and employed LR images synthetically downsampled from HR images 20 , 25 . Janssens et al 18 recently showed that synthetically downsampled LR images are often able to retain microstructure complexities, largely compromised in real LR scans by imaging artefacts.…”

Section: Resultsmentioning

confidence: 99%

“…Testing the effectiveness of these methods on digital rocks with different features can be thus critical to improve digital rock physics workflows, such as to achieve large sample volume with high resolution or enhance fast low quality scans (e.g., for samples to be preserved and 4D imaging with large experimental apparatus and/or high temporal resolution). Some pioneering efforts have been done in this direction, leading to very promising results, although in most cases LR images were synthetically downsampled from HR scans and only conventional sedimentary digital rocks were employed 18 , 20 – 25 (for details see 20 , 25 ).…”

Section: Introductionmentioning

confidence: 99%

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Exploring microstructure and petrophysical properties of microporous volcanic rocks through 3D multiscale and super-resolution imaging

Buono

Caliro

Macedonio

et al. 2023

Sci Rep

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Digital rock physics offers powerful perspectives to investigate Earth materials in 3D and non-destructively. However, it has been poorly applied to microporous volcanic rocks due to their challenging microstructures, although they are studied for numerous volcanological, geothermal and engineering applications. Their rapid origin, in fact, leads to complex textures, where pores are dispersed in fine, heterogeneous and lithified matrices. We propose a framework to optimize their investigation and face innovative 3D/4D imaging challenges. A 3D multiscale study of a tuff was performed through X-ray microtomography and image-based simulations, finding that accurate characterizations of microstructure and petrophysical properties require high-resolution scans (≤ 4 μm/px). However, high-resolution imaging of large samples may need long times and hard X-rays, covering small rock volumes. To deal with these limitations, we implemented 2D/3D convolutional neural network and generative adversarial network-based super-resolution approaches. They can improve the quality of low-resolution scans, learning mapping functions from low-resolution to high-resolution images. This is one of the first efforts to apply deep learning-based super-resolution to unconventional non-sedimentary digital rocks and real scans. Our findings suggest that these approaches, and mainly 2D U-Net and pix2pix networks trained on paired data, can strongly facilitate high-resolution imaging of large microporous (volcanic) rocks.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring microstructure and petrophysical properties of microporous volcanic rocks through 3D multiscale and super-resolution imaging

Buono

Caliro

Macedonio

et al. 2023

Sci Rep

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“…We investigated to compare and analyze the differences and correlations between 2D images and 3D volumes when calculating the same parameter. The specific details of 2D digital image analysis for pore/grain parameters are available in our previous publication (Rabbani et al., 2021). Our findings indicate a strong positive correlation between the average pore/throat size in 3D and the average pore/throat size in 2D (Figure 10).…”

Section: Resultsmentioning

confidence: 99%

“…Combining well logging or key parameter data with 1D CNN models, the physical properties of rocks, such as permeability and mechanical properties, can be accurately estimated, thereby enhancing our understanding of subsurface formations (Hu & Zhang, 2023; Li et al., 2022; Li & Misra, 2018; Prifling et al., 2021; L. Wu et al., 2023). For predicting image‐based 3D properties, CNNs directly finds many reliable microstructure‐property correlations to rapidly estimate physical properties of porous rocks, such as permeability, porosity, SSA and tortuosity (Table 1) (Alqahtani et al., 2020; Da Wang et al., 2021; Kamrava et al., 2020; M. Liu et al., 2023; Rabbani et al., 2020, 2021; Tahmasebi et al., 2020). Compared with the numerical simulation on 3D volume, CNNs require relatively low computational cost (Cang et al., 2018; Karimpouli & Tahmasebi, 2019; H. Wei et al., 2018).…”

Section: Introductionmentioning

confidence: 99%

Predicting 3D Physical Properties From a Single 2D Slice Based on Convolutional Neural Networks: 2D‐Slice‐To‐3D‐Properties for Porous Rocks

Chen,

Yang,

et al. 2023

Water Resources Research

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The 3D physical properties of porous rocks directly determine the subsurface flow and modelling. However, predicting a wide range of 3D physical rocks remains a formidable challenge and requires a large amount of input. Reliable microstructure‐property correlations can accurately predict 3D physical properties, avoiding time‐consuming experimental testing. Here, we propose a new dimensionless CNN‐based method to find inherent correlations for accurately predicting 3D properties of porous rocks using only a single 2D slice or a series of 2D slices. Training and testing were conducted on 9 properties of 16,409 semi‐realistic 3D porous rocks. Eight properties, except for the formation factor, were predicted with an acceptable correlation coefficient of 0.92. Furthermore, the modeling results indicate that the network architecture of the model has a significant impact on its performance. The classical CNN Model 4 achieved an average validating loss of 1.0×10‐3. Interval 2D slice sampling can significantly reduce the computational cost in training model. The developed model was verified and tested by five independent porous samples. Additionally, the proposed method accurately predicted permeability in different directions using only 2D slices trained in one direction, for porous rocks where anisotropy is not significant. This work demonstrates the ability to predict various 3D properties rapidly and accurately using only a single 2D slice from semi‐realistic 3D volumes. It provides insights into the conversion of 2D slices to 3D properties for porous media in a wide range of applications, including property estimation from 2D images without structural construction and simulation in 3D.This article is protected by copyright. All rights reserved.

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“…Barring the inefficiency, subjectivity, and lack of scalability of manual approaches, integrating qualitative/semi-quantitative descriptions into reservoir characterization schemes remains challenging, primarily due to the quantitative nature of the other input data modalities (e.g., well-logs, seismic lines, core plug petrophysical measurements, etc.) (Rabbani et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

An Object‐Based Approach to Differentiate Pores and Microfractures in Petrographic Analysis Using Explainable, Supervised Machine Learning

Jayachandran,

Gibbs,

Laya

et al. 2024

Earth and Space Science

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Petrographic observations are vital for carbonate pore‐typing, linking geological frameworks to petrophysical behavior. However, current petrographic pore typing is manual, with the qualitative to semi‐quantitative results not easily fitted into quantitative subsurface characterization. Some recent studies have automated this process using supervised machine learning (ML) and deep learning (DL), focusing on simple pore morphological features, and have reported high classification accuracies for several complex pore types. However, there are concerns about the validity of these studies due to conceptual and technical flaws in their collective approach. This study was aimed at a more fundamental problem, classifying between open microfractures and open pores in petrographic thin sections using an object‐based approach and explainable supervised ML. We analyzed 18 carbonate thin sections from the USA, numerically representing them using five shape features: compactness, aspect ratio, extent, solidity, and formfactor. Using a labeled data set of 400 microfractures and 400 pores, we evaluated nine of the most widely used supervised models. All models showed high testing accuracies (89.58%–90.42%). Interestingly, complex non‐linear models did not significantly outperform simpler linear ones. Compactness and aspect ratio were the most informative features. However, the labeled data sets did not reflect the overall data set's complexity, which suggested that high accuracies in similar studies might be due to curated data sets rather than accounting for the true complexity of carbonate pore systems. The study concludes that simple shape features are ineffective for classifying carbonate pore types. It is hoped that this study will provide a foundation for more robust artificial intelligence‐assisted pore typing.

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Review of Data Science Trends and Issues in Porous Media Research With a Focus on Image‐Based Techniques

Cited by 22 publications

References 278 publications

Exploring microstructure and petrophysical properties of microporous volcanic rocks through 3D multiscale and super-resolution imaging

Exploring microstructure and petrophysical properties of microporous volcanic rocks through 3D multiscale and super-resolution imaging

Predicting 3D Physical Properties From a Single 2D Slice Based on Convolutional Neural Networks: 2D‐Slice‐To‐3D‐Properties for Porous Rocks

An Object‐Based Approach to Differentiate Pores and Microfractures in Petrographic Analysis Using Explainable, Supervised Machine Learning

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