Digital rock physics and laboratory considerations on a high-porosity volcanic rock

Schepp, Laura L.; Ahrens, Benedikt; Balcewicz, Martin; Duda, Mandy; Nehler, Mathias; Osorno, Maria; Uribe, David; Steeb, Holger; Nigon, Benoit; Stöckhert, Ferdinand; Swanson, Donald A.; Siegert, Mirko; Gurris, Marcel; Saenger, Erik H.

doi:10.1038/s41598-020-62741-1

Cited by 26 publications

(13 citation statements)

References 35 publications

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“…Like other recent studies of sandstones and other lithologies (e.g., [36][37][38]), we used a voxel-based Digital Rock Physics approach for calculation of permeability of sample F3, applying this technique to sub-volumes of the segmented X-CT scan (Figure 4). The X-CT image of this sample was clipped into sub-volume cubes with 1.43 mm (200 voxels) to a side (Figure 5B), 84 sub-volumes in total being modelled.…”

Section: Numerical Flow Experimentsmentioning

confidence: 99%

Permeability and Mineralogy of the Újfalu Formation, Hungary, from Production Tests and Experimental Rock Characterization: Implications for Geothermal Heat Projects

et al. 2021

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Hundreds of geothermal wells have been drilled in Hungary to exploit Pannonian Basin sandstones for district heating, agriculture, and industrial heating projects. Most of these sites suffer from reinjection issues, limiting efficient use of this vast geothermal resource and imposing significant extra costs for the required frequent workovers and maintenance. To better understand the cause of this issue requires details of reservoir rock porosity, permeability, and mineralogy. However, publicly available data for the properties of reservoir rocks at geothermal project sites in Hungary is typically very limited, because these projects often omit or limit data acquisition. Many hydrocarbon wells in the same rocks are more extensively documented, but their core, log, or production data are typically decades old and unavailable in the public domain. Furthermore, because many Pannonian sandstone formations are poorly consolidated, coring was always limited and the collected core often unsuitable for conventional analysis, only small remnant fragments typically being available from legacy hydrocarbon wells. This study aims to reduce this data gap and to showcase methods to derive reservoir properties without using core for flow experiments. The methods are thin-section analysis, XRD analysis and mercury intrusion porosimetry, and X-CT scanning followed by numerical flow simulation. We validate our results using permeability data from conventional production testing, demonstrating the effectiveness of our method for detailed reservoir characterization and to better constrain the lateral variation in reservoir properties across the Pannonian Basin. By eliminating the need for expensive bespoke coring to obtain reservoir properties, such analysis will contribute to reducing the capital cost of developing geothermal energy projects, thus facilitating decarbonization of global energy supply.

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Section: Numerical Flow Experimentsmentioning

confidence: 99%

Permeability and Mineralogy of the Újfalu Formation, Hungary, from Production Tests and Experimental Rock Characterization: Implications for Geothermal Heat Projects

et al. 2021

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“…He used the image grey value for rock image processing research, which inspired the use of rock grey image for rock pore analysis in this study. Schepp et al [ 33 ] studied digital rock physics and laboratory considerations for a high-porosity volcanic rock. The results of their study provide a new idea for improving the TCRMs used in carbonate porosity research.…”

Section: Introductionmentioning

confidence: 99%

Porosity of the porous carbonate rocks in the Jingfengqiao–Baidiao area based on finite automata

2022

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This study is based on the processing of computed microtomography images of rock samples. In this study, a finite automation is constructed using the grey value, red-green-blue (RGB) value and Euler number of polarized images of carbonate rocks from the Jingfengqiao–Baidiao area. The finite automaton is used to perform black and white binary processing of the polarized images of the carbonate rocks. The porosity of the carbonate rock is calculated based on the black and white binarization processing results of the polarized images of the carbonate rocks. The obtained porosity is compared with the carbonate porosity obtained by use of the traditional carbonate research method. When the two porosities are close, the image processing threshold of the finite automata is considered to be credible. Based on the finite automata established using the image processing threshold, the black and white binary images of the polarized images of the carbonate rocks are used to establish a rock pore image using I mage J2X. The polarized images of the carbonate rocks are classified according to their RGB values using the finite automata for the porosity classification, and the obtained images are used as textures to paste onto a cube to construct a three-dimensional data model of the carbonate rocks. This study also uses 16S rDNA analysis to verify the formation mechanism of the carbonate pores in the Jingfengqiao–Baidiao area. The results of the 16S rDNA analysis show that the pores in the carbonate rocks in the Jingfengqiao–Baidiao area are closely related to microorganisms, represented by denitrifying bacteria.

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“…Anselmetti et al (1998) and Anselmetti and Eberli (1999) introduced an empirical method for calculating pore geometry based on thin section studies and velocity-deviation log, respectively. More recently, digital rock physics (DRP) was introduced, where Xray-computed tomography (CT scan) was utilized to quantify pore geometry (Verri et al, 2017;Arns et al, 2019;Prananda et al, 2019Schepp et al, 2020. Weger et al (2009) and Bashah and Pierson (2012) used thin section data in the framework of digital image analysis (DIA) to quantify pore space parameters with a focus on the permeability of carbonate reservoirs.…”

Section: Introductionmentioning

confidence: 99%

Intelligent pore type characterization: Improved theory for rock physics modelling

Sharifi

2022

Geophysical Prospecting

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Thanks to the recent developments in both hardware and software capabilities of computers, intelligent rock physics modelling has emerged as an alternative to the conventional approach to the rock physics. Respecting the crucial contribution of the pore geometry into the rock physics modelling, I propose an accurate yet cost‐ and time‐efficient intelligent framework to measure pore space based on digital rock physics. In this method, total pore space was calculated after estimating the pore geometry through pattern recognition on thin section images captured through polarized‐light microscopy. Next, applying three different multi‐class classifiers (radial basis function, support vector machine and k‐nearest neighbours) for estimating pore type and aspect ratio, the best results were obtained using the fuzzy Sugeno integral, and the pore types were classified according to the most widely used pore type classification scheme. Next, an artificial neural network was applied to interpolate discrete data points (thin sections) into continuous profiles of pore type and aspect ratio. Subsequently, as a case study, the proposed approaches were applied to a real‐world carbonate reservoir for modelling the P‐ and S‐wave velocities through a rock physics model. Verifying the modelling results against ultrasonic and measured well‐logging data, the methodology showed promising performance at acceptable levels of uncertainty. The most significant advantage of the intelligent pore type quantification over the conventional methods was found to be its ability to estimate elastic properties with good accuracy. The key findings of this research include automatic detection of the pore types and aspect ratio, provision of a database of pore geometry, attenuation of uncertainty in pore type characterization and improvement of rock physics modelling in the absence of reliable S‐wave velocity data.

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Digital rock physics and laboratory considerations on a high-porosity volcanic rock

Cited by 26 publications

References 35 publications

Permeability and Mineralogy of the Újfalu Formation, Hungary, from Production Tests and Experimental Rock Characterization: Implications for Geothermal Heat Projects

Permeability and Mineralogy of the Újfalu Formation, Hungary, from Production Tests and Experimental Rock Characterization: Implications for Geothermal Heat Projects

Porosity of the porous carbonate rocks in the Jingfengqiao–Baidiao area based on finite automata

Intelligent pore type characterization: Improved theory for rock physics modelling

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