Pore scale image analysis for petrophysical modelling

Pal, Arnab Kumar; Garia, Siddharth; Ravi, K.; Nair, Archana M.

doi:10.1016/j.micron.2021.103195

Cited by 14 publications

(3 citation statements)

References 58 publications

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“…Arnab Kumar Pal et al [33] have revealed the internal micro-structure and their connectivity across multiple scales with digital pore-scale images of sandstone. Joyce Schmatz et al [34] imaged pore-scale fluid-fluid-solid contacts in sandstone with nanometer resolution using cryogenic broad ion-beam polishing in combination with scanning electron microscopy and phase identification by energy-dispersive X-ray analysis.…”

Section: Digital Reconstruction Of Pore Structure 231 Methodsmentioning

confidence: 99%

Analytical Prediction of Coal Spontaneous Combustion Tendency: Pore Structure and Air Permeability

Liang

Tian

et al. 2023

Sustainability

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In previous research, many scientists and researchers have carried out related studies about the spontaneous combustion of coal at both the micro and the macro scales. However, the macroscale study of coal clusters and piles cannot reveal the nature of oxidation and combustion, and the mesoscale study of coal molecule and functional groups cannot be directly applied to engineering practice. According to our literature survey, coal is a porous medium and its spontaneous combustion is a multi-scale process. Thus, the mesoscale study of coal’s spontaneous combustion is essential. In this manuscript, the mesoscale of the coal body (such as pore size, pore volume, and specific surface area), and the meso-scale structural morphological characteristics of the coal surface are finely analyzed and characterized. On this basis, the meso-scale structure of pores and fractures are digitally reconstructed. Furthermore, velocity and pressure distributions of the flow field in the pores of the scan plane are outlined and described by numerical simulation. The results indicate that, because of the pore structure characteristics and fluid viscosity, not all fluids in the pores demonstrate flow. This conclusion well explains the source of CO gas in methane extraction pipes, which is one of the main index/indicator gases of the spontaneous combustion of coal.

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Section: Digital Reconstruction Of Pore Structure 231 Methodsmentioning

confidence: 99%

Analytical Prediction of Coal Spontaneous Combustion Tendency: Pore Structure and Air Permeability

Liang

Tian

et al. 2023

Sustainability

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“…Digital image analysis (DIA) based on thin-section images has increasingly been applied to calculate pore throat structure, mineral particles, particle/grain size distribution, and even wettability and dynamic saturation of multiphase flow, owing to the low cost and wide applicability of casting thin slices [15][16][17]. Currently, there exist well-developed semi-automatic or fully automatic digital imaging methods for capturing grain and pore information [18][19][20]. Image thresholding and binary images are commonly utilized for pore-throat and particle size analysis.…”

Section: Introductionmentioning

confidence: 99%

Estimation of 3D Permeability from Pore Network Models Constructed Using 2D Thin-Section Images in Sandstone Reservoirs

Luo,

Wan,

Chen

et al. 2023

Energies

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Using thin-section images to estimate core permeability is an economical and less time-consuming method for reservoir evaluation, which is a goal that many petroleum developers aspire to achieve. Although three-dimensional (3D) pore volumes have been successfully applied to train permeability models, it is very expensive to carry out. In this regard, deriving permeability from two-dimensional (2D) images presents a novel approach in which data are fitted directly on the basis of pore-throat characteristics extracted from more cost-effective thin sections. This work proposes a Fluid–MLP workflow for estimating 3D permeability models. We employed DIA technology combined with artificial lithology and pore classification to calculate up to 110 characteristic parameters of the pore-throat structure on the basis of 2D rock cast thin sections. The MLP network was adopted to train the permeability prediction model, utilizing these 110 parameters as input. However, the accuracy of the conventional MLP network only reached 90%. We propose data preprocessing using fluid flow simulations to improve the training accuracy of the MLP network. The fluid flow simulations involve generating a pore network model based on the 2D pore size distribution, followed by employing the lattice Boltzmann method to estimate permeability. Subsequently, six key structural parameters, including permeability calculated by LBM, pore type, lithology, two-dimensional porosity, average pore–throat ratio, and average throat diameter, were fed into the MLP network for training to form a new Fluid–MLP workflow. Comparing the results predicted using this new Fluid–MLP workflow with those of the original MLP network, we found that the Fluid–MLP network exhibited superior predictive performance.

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“…Currently, with the continuous advancement of micro/nano CT technology, the digital core simulation technique based on X-ray attenuation image reconstruction enables quantitative analysis of 3D pore characteristics and development of 3D digital core models for seepage simulation, thereby facilitating the elucidation of underlying physical mechanisms governing seepage. Research has been conducted to reconstruct the 3D pore models of carbonate rock, coals, sandstone, or shale to investigate the corresponding seepage characteristics of water or hydrocarbons within [9][10][11][12][13][14], as well as to investigate the particle movement or settlement in the pore network [15,16], and, lastly, the phase permeability characteristics can also be investigated by pore scale flow simulation [17,18]. Some researchers have even simulated three-phase flow characteristics under different wettability conditions within the digital cores [19,20].…”

Section: Introductionmentioning

confidence: 99%

Seepage Simulation of Conglomerate Reservoir Based on Digital Core: A Case Study of the Baikouquan Formation in Mahu Sag, Junggar Basin

Zhang,

Hu,

Dong

et al. 2023

Processes

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Pore structure and flow characteristics are key factors affecting oil recovery rates in heterogeneous tight conglomerate reservoirs. Using micron computed tomography (CT) and modular automated processing system (MAPS) techniques, the pore structure of downhole core samples taken from Mahu’s tight conglomerate reservoirs was analyzed in detail, and a two-scale digital core pore network model with both a wide field of view and high resolution was constructed based on these pore structure data; the digital pore model was corrected according to the mercury intrusion pore size distribution date. Finally, we simulated flow characteristics within the digital model and compared the calculated permeability with the indoor permeability test date to verify the dependability of the pore network. The results indicated that the pore–throat of the conglomerate reservoir in Mahu was widely distributed and exhibited significant bimodal characteristics, with main throat channels ranging from 0.5 to 4 μm. The pore structure showed pronounced microscopic heterogeneity and intricate modalities, mainly consisting of dissolved pores, intergranular pores, and microfractures. These pores were primarily strip-like, isolated, and played a more crucial role in enhancing pore connectivity rather than contributing to the overall porosity. The matrix pores depicted by the MAPS were relatively smaller in size and more abundant in number, with no individual pore type forming a functional seepage channel. The permeability parameters obtained from the two-scale coarse-fine coupled pore network aligned with the laboratory experimental results, displaying an average coordination number of two. Flow simulation results indicated that the core’s microscopic pore structure affected the shape of the displacement leading edge, resulting in a tongue-in phenomenon during oil–water flow. The dominant flow channel was mainly dominated by water, while tongue-in and by-pass flow were the primary microscopic seepage mechanisms hindering oil recovery. These findings lay a foundation for characterizing and analyzing pore structure as well as investigating flow mechanisms in conglomerate reservoirs.

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Pore scale image analysis for petrophysical modelling

Cited by 14 publications

References 58 publications

Analytical Prediction of Coal Spontaneous Combustion Tendency: Pore Structure and Air Permeability

Analytical Prediction of Coal Spontaneous Combustion Tendency: Pore Structure and Air Permeability

Estimation of 3D Permeability from Pore Network Models Constructed Using 2D Thin-Section Images in Sandstone Reservoirs

Seepage Simulation of Conglomerate Reservoir Based on Digital Core: A Case Study of the Baikouquan Formation in Mahu Sag, Junggar Basin

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