3D Digital Core Research Articles

Computational microfluidics of reactive transport processes with solid dissolution and self-induced multiphase flow

There are still many unclear mechanisms in the multiphase reactive flow with solid dissolution processes. In this study, the reactive transport processes coupled with solid dissolution and self-induced multiphase flow in three-dimensional (3D) structures with increasing complexity is studied by developing a 3D computational microfluidic method, which considers multiphase flow, interfacial mass transport, heterogeneous chemical reactions, and solid structure evolution. Solid dissolution diagram in a simple channel in the framework of multiphase flow is proposed, with six coupled multiphase flow and solid dissolution patterns identified and the transition between different patterns discussed. Then, multiphase reactive flow in a porous chip is further studied, and the interesting 3D phenomena are discovered, including enhanced solid dissolution in the middle and enriched bubble generation at the corner along the thickness direction. Considering the importance of reactive surface area, correlations of reactive surface area-porosity-saturation with different dissolution patterns are proposed based on the pore-scale results. Finally, the computational microfluidic model is extended to investigate the multiphase reactive flow in a 3D digital core. Different dissolution patterns are recognized using the local porosity evolution character, and the corresponding pore size distribution and bubble characteristics are deciphered. These findings advance understanding of multiphase reactive transport processes and contribute to improve continuum-scale reactive transport modeling.

Multi-factor numerical simulation and response characteristics of two-dimensional nuclear magnetic resonance in digital cores

Two-dimensional nuclear magnetic resonance (2D NMR) logging is an important technology for reservoir evaluation in oil and gas exploration and development, and it has unique advantages in fluid identification. However, in oil shale reservoirs, the mechanisms of 2D NMR logging are complex, and the response characteristics of various factors are unclear. Numerical simulation based on digital core is a significant method to study the influences of multiple factors on 2D NMR. This manuscript introduces the construction method of high-resolution three-dimensional (3D) digital core models, and expounds the principle and workflow of 2D NMR numerical simulation. High-resolution 3D digital cores were constructed based on X-CT, MAPS, and QEMSCAN data of Berea sandstone and Daqing Gulong shale, respectively. Based on 3D digital cores, 2D NMR numerical simulations were carried out on temperature/viscosity, echo spacing, and frequency. Then, 2D NMR mechanisms were analyzed and the response characteristics of multiple factors were summarized. The results show that the T2 and T1 of the water peak have a linear negative correlation with temperature/viscosity, and the T2 and T1 of the oil peak have a linear positive correlation. For Daqing Gulong shale, the T2 of the water peak has a linear positive correlation with echo spacing, while for Berea sandstone, echo spacing has little effect. For Berea sandstone, the T1 of the water peak is linearly positively correlated with frequency. For Daqing Gulong shale, the T1 of the water peak and oil peak are both linearly positively correlated with frequency. The response characteristics of multiple factors could provide a reference and basis for the interpretation and evaluation of 2D NMR logging under complex conditions.

Pore structure characterization and permeability prediction of uranium-bearing sandstone based on digital core

The permeability of the ore-bearing layer is an important indicator affecting the in-situ leaching (ISL) of uranium-bearing sandstone, which is related to various factors such as pore shape, distribution, and size. In order to study the effect of pore structure on seepage in low-permeability uranium-bearing sandstone, CT scanning tests were conducted to create a 3D digital core based on scanning images and to calculate the fractal dimension using the box counting dimension method, which integrated fractal theory to define the core samples' pore structure. The permeability prediction was realized based on the porosity-permeability model and the fractal theory model. Results indicated that this type of sandstone is obviously characterized by pore connectivity, large differences in distribution, and strong microscopic inhomogeneity. The pores are dominated by micro- and nano-pores, as well as small pores, accounting for 90 %; macropores are few in number, but the diameters of their single pores are large. The distribution of pore structure in this type of sandstone exhibits a good fractal characteristic; the three-dimensional fractal dimensionality is 2.044–2.310. The porosity-permeability model was established, and permeability prediction was realized by combining the fractal theory to provide theoretical support for determining the values of well field parameters in ISL.

Super-resolution reconstruction of 3D digital rocks by deep neural networks

Digital rock technology provides valuable insights into the pore structure and fluid flow properties of geoenergy resources. Artificial intelligence technology in vision and image processing, especially the image super-resolution, has great potential for digital rock reconstruction and resolution enhancement. However, the analyzed core samples are typically sandstones/carbonates in micro-scale resolutions and in two-dimensional (2D) space, whereas the shale rocks in nano-scale resolutions for unconventional resources or three-dimensional (3D) digital cores are rarely investigated. Additionally, previous studies primarily emphasized image quality from a computer vision perspective, with little consideration given to estimating physical properties of digital rocks using super-resolution techniques.This study presents a very deep super-resolution (VDSR) algorithm, specifically designed to generate high-resolution 3D digital rock images, for nano-scale shale matrix and micro-scale hydraulic fractures. We compare both image quality and permeability accuracy between the original high-resolution images and the super-resolution images reconstructed by the proposed method. The results reveal that the reconstructed images using the proposed method closely resemble the actual images, and effectively reduce errors in permeability computations. This study highlights the applicability of the proposed VDSR algorithm in establishing the detailed structures of 3D nano-scale shale matrix and hydraulic fractured rocks, thus advancing super-resolution techniques in digital core analysis for geoenergy resources development.

Acoustical-electrical models of tight rocks based on digital rock physics and double-porosity theory

The acoustical-electrical (AE) properties of reservoir rocks are affected by their microstructure (pores, microfractures, cracks and their geometry) and saturating fluids. To aid the interpretation, digital rock physics (DRP) is a useful technique to characterize this microstructure, as well as equivalent petrophysical models (EPM) to obtain joint AE properties. We perform thin-section analysis on rocks from tight-oil reservoirs, along with X-ray diffraction (XRD) and computed tomography (CT) to study rock lithology and minerals. We also perform porosity, permeability, ultrasound, and electrical conductivity experiments as a function of confining pressure to analyze pore structure. First, the 3D digital multicomponent cores are created based on a geology-driven multiphase segmentation workflow and images of the samples, and verified based on the porosity and minerals. Then, the AE properties and permeability are calculated by using numerical simulations (finite difference and finite volume methods). Next, the rigid and microcracked (soft) porosities are determined by using the Shapiro model to create the rock skeleton. We develop a joint EPM based on the effective medium AE and the Biot-Rayleigh equations with double porosity to describe the rock properties. The ultrasonic, sonic and seismic multiple data are used to compare and analyze the two approaches. The results show that the DRP techniques based on real cores are effective in characterizing the microstructure and the proposed EPM can describe the AE properties of real rocks, indicating a potential for quantitative characterization of reservoirs.

Mesoscale migration of oil in tight sandstone reservoirs by multi-field coupled two-phase flow

The oil migration process plays a pivotal role in determining the distribution and scale of oil reservoirs in tight sandstone formations, while also simultaneously influencing their development outcomes. However, the mechanisms governing oil migration in these tight formations remain poorly understood. This study conducted numerical simulations to investigate the oil migration characteristics in tight sandstone at the mesoscale. Multivariate regression analysis was used to characterize the micromechanical parameters of a 3D digital core. In addition, the main equations describing the two-phase flow that couples the stress and seepage fields were proposed, thereby elucidating the oil migration process. Finally, the effects of the reservoir space type, pressure gradient, and stress on the oil migration efficiency were assessed. The results showed that: (1) Compared with the porous-type model, the fractured-type model had a higher level and more concentrated distribution of damage, better connectivity, and higher oil migration efficiency due to the seepage-stress coupling effect; (2) High fluid pressure gradients improved the efficiency of oil migration and enhanced the control of the three-dimensional distribution of the pore domain over oil distribution in the matrix domain. (3) When σ1 >σ3, the oil phase migration efficiency improves with the increasing axial stress. When σ1 <σ3, the oil phase migration efficiency decreases with the increasing confining stress. When σ1 = σ3, high stress leads to low oil phase migration efficiency. This study reveals the mesoscopic-scale mechanism of oil migration in tight sandstone reservoirs and may prove useful in the exploration and development in such reservoirs.

3D-FGAN: A 3D stochastic reconstruction method of digital cores

Digital core reconstruction can quantitatively characterize the interior microstructure of porous media and revealing variations in the petrophysical properties of rocks, which can be classified into physical experimental methods and numerical reconstruction methods. Numerical reconstruction methods utilize the information contained in 2D or 3D images to reconstruct 3D digital cores, which is relatively inexpensive and more efficient, but traditional numerical methods usually cannot reuse extracted statistical information from previous reconstruction process and may generate unsatisfying results. Nowadays, the development of deep learning has opened a door to the high-quality reconstruction of digital cores. Generative adversarial networks (GANs), viewed as an important branch of deep learning, have been used for the 3D digital core reconstruction. GANs have a strong feature extraction capability and can generate fake images that closely resemble the real ones. Nevertheless, due to the complex structure of 3D core images, especially those with high anisotropy, GANs and their variants still have difficulties in generating satisfying pore structure. To address the above issues, we propose a variant of GANs called 3D fast generative adversarial network (3D-FGAN) to rapidly generate high-quality 3D digital core images. Several channel attention modules called skip-layer excitation blocks are added to the generator of 3D-FGAN, enhancing the utilization of important features. Auto-encoders are employed to optimize the discriminator of 3D-FGAN, which makes the discriminator more comprehensively extract the features of input images and thus better guides the generator. Through the evaluation metrics of pore space distribution (e.g., isolated pore space and connected pore space, pore diameters and throat diameters), multiple-point connectivity, absolute permeability and diversity test, the quality of 3D-FGAN can be proved. Meanwhile, in the reconstruction of anisotropic shales with evident fractures, 3D-FGAN can quickly produce higher-quality images compared with some other numerical reconstruction methods.

Digital core reconstruction based on discrete element and Markov chain-Monte Carlo methods

The digital core is an essential platform for pore-scale flow simulation in geoenergy exploitation, CO2 storage, and hydrogen storage. However, the existing reconstruction methods, such as the Markov chain-Monte Carlo (MCMC) method, can not obtain the digital core reflecting the influence of complex stress. Moreover, the discrete element method (DEM)-based reconstruction, which can consider the effect of stress, has yet to be reported to consider the actual particle shape. To this end, a hybrid reconstruction method based on DEM and MCMC methods is proposed in this study. Firstly, watershed and level set algorithms are used to segment and extract the contours of rock particles, and clump in PFC2D is used to fill the contours to establish a clump template library. Then, the initial condition (ground condition) digital core slice is established with clump packing based on porosity and particle size distribution. Subsequently, the stress loading simulation is carried out to obtain 2D digital core slices with different stress combinations. Finally, the MCMC method obtains 3D digital cores. This proposed method is used to reconstruct the digital core of Belgian fieldstone with different stresses and effective moduli, and the evolution of pore structure characteristics is analyzed. Results show that the stress compresses the range of the pore radius distribution curve, resulting in the shortening of the throat length distribution; the conductivity of the model is poor. The maximum reduction of porosity and permeability is 6.56% and 24.81%, respectively. The model with a large effective modulus has a stronger resistance to deformation and can protect the pore structure. These results prove that the method proposed can obtain the digital core reflecting the stress effects and provide a reference for accurately describing the in-situ rocks' pore structure and the pore-scale flow simulation.

3D reconstruction of digital cores based on multi-discriminator GAN and improved residual blocks

Digital cores have gradually become an effective way for core analysis in recent years, which is of great significance for the exploitation of oil and gas resources, seepage mechanisms and geological science. Traditional numerical reconstruction methods for digital cores often have the disadvantages of long training time and unsatisfying reconstruction quality. As a popular model in the field of image generation, generative adversarial network (GAN) has powerful feature extraction capability and is widely applied for three-dimensional (3D) reconstruction of digital cores, but it also requires a sufficient number of training samples and a long reconstruction process. Based on GAN, we propose a model named multi-discriminator GAN (MDGAN), which uses a single image as the training image (TI) and combines residual blocks as well as diversity regularization. In comparison with several representative numerical reconstruction methods and single-image GAN (SinGAN), it is demonstrated that MDGAN can accomplish 3D digital core reconstruction faster with high quality.

Research on a New Reconstruction Technology and Evaluation Method for 3D Digital Core Pore Structure

This study analyses the main techniques for constructing a three-dimensional (3D) digital core, and summarizes the advantages and disadvantages of each reconstruction technique. The direction for the development of reconstruction technology for 3D digital cores is also proposed. The integration of multiscale, multicomponent, and nanoscale technology under different resolutions will play a significant role in future research efforts surrounding the physical properties of 3D digital cores. This study also introduces the main evaluation methods of digital cores, while analyzing and comparing the applicable scenarios of each method. The results of the pore statistics method, based on the seed filling algorithm, can be used as a new and effective evaluation method for the 3D reconstruction of digital cores.

Deep learning method of stochastic reconstruction of three-dimensional digital cores from a two-dimensional image.

Digital cores can characterize the true internal structure of rocks at the pore scale. This method has become one of the most effective ways to quantitatively analyze the pore structure and other properties of digital cores in rock physics and petroleum science. Deep learning can precisely extract features from training images for a rapid reconstruction of digital cores. Usually, the reconstruction of three-dimensional (3D) digital cores is performed by optimization using generative adversarial networks. The training data required for the 3D reconstruction are 3D training images. In practice, two-dimensional (2D) imaging devices are widely used because they can achieve faster imaging, higher resolution, and easier identification of different rock phases, so replacing 3D images with 2D ones avoids the difficulty of acquiring 3D images. In this paper, we propose a method, named EWGAN-GP, for the reconstruction of 3D structures from a 2D image. Our proposed method includes an encoder, a generator, and three discriminators. The main purpose of the encoder is to extract statistical features of a 2D image. The generator extends the extracted features into 3D data structures. Meanwhile, the three discriminators have been designed to gauge the similarity of morphological characteristics between cross sectionsof the reconstructed 3D structure and the real image. The porosity loss function is used to control the distribution of each phase in general. In the entire optimization process, a strategy using Wasserstein distance with gradient penalty makes the convergence of the training process faster and the reconstruction result more stable; it also avoids the problems of gradient disappearance and mode collapse. Finally, the reconstructed 3D structure and the target 3D structure are visualized to ascertain their similar morphologies. The morphological parameter indicators of the reconstructed 3D structure were consistent with those of the target 3D structure. The microstructure parameters of the 3D structure were also compared and analyzed. The proposed method can achieve accurate and stable 3D reconstruction compared with classical stochastic methods of image reconstruction.

Digital Core Permeability Computation by Image Processing Techniques

Calculation of REV (representative elementary volume) properties of geological porous media refers to the process of creating a 3D digital representation of a rock sample, typically obtained from imaging techniques such as X-ray microtomography. This technique allows for a detailed analysis of the internal structure and the properties of rocks, as well as precise calculation of various flow parameters. However, one major challenge with calculation of REV properties of geological porous media is the high computational cost required to generate accurate results, especially for large and complex samples. In this study, we constructed 3D digital cores of dune sand and fractured shale using CT scanning technology, and then used two image processing techniques, namely digital core image resampling and cutting, to reduce the computational cost of calculating digital core permeability. Next, a fast permeability calculation method is employed to reduce the complexity of permeability calculation. Finally, we summarized the applicability of different image processing methods to different rock samples, and provided prerequisites for high computational cost digital core permeability calculation.

Quantitative Characterization of Pore Structure of Carbonate Rocks Based on Micron-CT Technique and Its Seepage Simulation

Micron-CT technology can make up for many shortcomings of traditional rock physics experiments and provide a new platform for rock physics research. In this paper, a 3D digital core model with realistic pore structure characteristics was established using micron-CT scanning combined with advanced image processing techniques with carbonate rocks as the research object. Various morphological algorithms included in the digital core software (Avizo) are applied to quantify and characterize the pore structure of digital cores, and obtain the characteristics of total porosity, effective porosity, pore and throat equivalent radius distribution, and establish an equivalent pore network model. The absolute permeability experiment module was applied in the digital core software (Avizo) to realize the simulation of seepage on the micron scale and calculate the absolute permeability. This paper enriches the existing digital petrophysical research tools by applying digital core software (Avizo), which is of great significance to deepen the understanding of hydrocarbon storage and transport mechanisms inside the reservoir.

Impact of tectonic deformation on shale pore structure using adsorption experiments and 3D digital core observation: A case study of the Niutitang Formation in Northern Guizhou

Tectonic deformation has an evident impact on the pore structure of organic-rich marine shale. To study the impact of tectonic deformation on the pore structure of shale, cores were taken from different tectonic regions in the Niutitang Formation. The paleotectonic stress, pore structures, mineral composition, and geochemical parameters of the shale were determined by conducting the following tests: paleotectonic stress test, core thin-section observation, focus ion beam-scanning electron microscopy (FIB-SEM) observation, vitrinite reflectivity test, low-temperature nitrogen adsorption test, total organic carbon (TOC) test, X-ray diffraction (XRD) test, and nano-CT. The results showed that the shale in the deformed region mainly developed meso-macropores, and the pores were mainly slit-type and conical. The shale in the transition and stability regions primarily developed mesopores and micropores, which were primarily elliptical, slit-type, and columnar. The pore volumes of the shale in the deformed region were 9.54 cm3/kg and 13.9 cm3/kg higher than those in the transition and stability regions, respectively. The shale in the stability and transition regions had average microporous volumes 8.67% and 7.93% higher than that in the deformed region, respectively. The shale reservoir in the stability region developed more micropores and had a higher specific surface area and microporous volume. Furthermore, the organic matter had a greater impact on the micropores than on the meso-macropores. Thermal maturity had a greater impact on the microporous volume than on the macroporous volume. The shale pore connectivity of Well TX 1 was 86.48% and that of Well FC 1 was 22.33%. The pore connectivity of the shale in the stability region was higher than that of the shale in the deformed region. The shale samples from the deformed region had larger pore volumes and primarily developed macropores. The difference in the meso-macropores is significantly greater than the difference in the microporous volume. Hence, the geological tectonism had a greater impact on the meso-macropores than on the micropores in the shale cores.

Correlation Evaluation and Schematic Analysis of Influencing Factors Affecting Pore and Fracture Connectivity on the Microscale and Their Application Discussion in Coal Reservoir Based on X-ray CT Data.

The connectivity of the pore/fracture system is the key to CO2 injection and CH4 production, which is of great significance in analyzing the correlation and weight of the influencing factors affecting the connectivity on the microscale. First, the 3D reconstruction of the coal reservoir is realized. Second, the characteristic parameters of pore/fracture structures are analyzed. Next, the characteristics of absolute permeability are analyzed, and then the correlation and weight analysis of the influencing factors are realized. Finally, the schematic analysis and application discussion of the influencing factors are carried out. The results show that porosity is the key factor restricting fluid migration. The heterogeneity of the reservoir can be characterized by the volume changes of the pore/fracture, organic matter, and mineral. The interconnected pores/fractures are mainly distributed in sheets and bands. The coordination number ranges from 1 to 15. The Ferret diameter is 0-10 μm. The tortuosity is 2.27111, 1.9034, 3.98522, and 3.51516, respectively, and the Euler characteristic number is 0.931868, 0.974719, 0.921144, and 0.897697, respectively. The permeability of the SH and YW samples is higher than that of the RL and PY samples. The single weight of the influencing factor is as follows: coordination number > Ferret diameter > Euler characteristic number > porosity > tortuosity. The analysis area of the comprehensive evaluation score of the influencing factors and the permeability value can be divided into three grades. There is a positive correlation among the coordination number, the quantity equilibrium of pores and throats, and the connectivity. The shape factor gradually increases with the increase of the Ferret diameter. The reservoir permeability is indirectly characterized by the coordination number, Ferret diameter, tortuosity, Euler characteristic number, and shape factor. This study can provide new ideas for clarifying the correlation degree and weight value of the characteristic parameters and can enrich the development of 3D digital core and CO2-ECBM technology.

Analysis of Influencing Factors of Poisson’s Ratio in Deep Shale Gas Reservoir Based on Digital Core Simulation

Conventional petrophysical experiments in deep shale gas reservoirs are characterized by difficult coring, high cost, and insufficient representative samples, so it is difficult to comprehensively investigate the key factors of Poisson’s ratio through petrophysical experiments. In this study, a multiscale and multicomponent three-dimensional (3D) digital core was constructed for the shale gas reservoir of Wufeng Formation-Longmaxi Formation in the Dazu area, Western Chongqing, China, to quantitatively simulate the influences of the changes of reservoir gas saturation, mineral composition, stratification, and fractures on Poisson’s ratio. The absolute errors between Poisson’s ratio measured by core experiments, Poisson’s ratio simulated by the multiscale and multicomponent digital core, and Poisson’s ratio calculated with the time differences of longitudinal and transverse waves were analyzed. The analysis results showed that Poisson’s ratio was sensitive to stratification dip angle and fracture dip angle. When the stratification dip angle or fracture dip angle was close to 45°, Poisson’s ratio reached its minimum value. Poisson’s ratio was more sensitive to the content of calcite than the contents of quartz, dolomite, and pyrite. The influence of gas saturation on Poisson’s ratio was the least. The average error between Poisson’s ratio measured by core experiments and Poisson’s ratio simulated by the multiscale and multicomponent digital core was 4.920%. The average error between Poisson’s ratio measured by core experiments and Poisson’s ratio calculated with the time differences of longitudinal and transverse waves was 10.968%.

Evaluating the Pore Structure of Low Permeability Glutenite Reservoir by 3D Digital Core Technology

In order to analyze the micro pore structure characteristics of low permeability glutenite reservoirs more intuitively and accurately, CT scanning imaging technology is used to evaluate the development characteristics of pores, fractures and high-density interstitial materials in the three-dimensional core space of samples, and the rock pore structure is analyzed from the perspective of image gray, homogeneity and pore throat connectivity.

Shale Oil Occurrence Mechanisms: A Comprehensive Review of the Occurrence State, Occurrence Space, and Movability of Shale Oil

Shale oil resources are important supplements for the gradually decreasing oil production from conventional reservoirs. Although the exploitation and development of shale oil have achieved considerable progress in the last decade, the commercial extraction of hydrocarbons from shales is still difficult, especially in the lacustrine sedimentary basins of China. One of the key points controlling the successful extraction of hydrocarbons from shale systems is the understanding of the occurrence mechanism of shale oil. This study comprehensively summarizes the theories and techniques to characterize oil occurrence state, occurrence space, oil content, and oil movability in shale systems. Sophisticated instruments, such as high-resolution scanning electron microscopy and high-energy ray imaging, were utilized to qualitatively analyze the pore networks of shales. Advanced physical experiments and numerical simulation techniques, including step-by-step rock pyrolysis, solvent extraction, and NMR, were introduced to characterize shale oil adsorption and movability. By the comparative analysis of the occurrence space, it is found that the image observation technique especially focuses on concentrated pores, such as organic matter-hosted pores. The fluid injection technology yields particular pore size information, which should be calibrated using other information. The 3D digital core, demonstrating the spatial distribution of minerals and pores, is an effective input for shale oil flow simulation. Geological controls analysis about oil retention in organic-rich shales has found that the inorganic matter pores and fractures are probably the “sweet spot” of shale oil, due to the low oil adsorption and high light hydrocarbons content. Many physical experiments measure the total free oil content but neglect the hydrocarbon–rock interaction and the sequential migration of hydrocarbon compounds. Thus, micro-scaled experiments measuring the hydrocarbon adhesion forces are needed to uncover the occurrence mechanism of shale oil in the future.

Evaluating the pore structure of low permeability glutenite reservoir by 3D digital core technology

In order to analyze the micro pore structure characteristics of low-permeability glutenite reservoirs more intuitively and accurately, µ-CT scanning imaging technology is used to evaluate the development characteristics of pores, fractures and high-density interstitial materials in the three-dimensional core space of samples, and the rock pore structure is analyzed from the perspective of image gray, homogeneity and pore throat connectivity. According to the maximum sphere algorithm, a digital core pore network model is extracted and established, and the probability distribution characteristics of pore radius, throat radius, pore throat ratio and core pore throat coordination number of different rock samples are studied. The results show that the reservoir space of the samples in the study area is dominated by dissolution pores and micro fractures, with pore radius ranging from 0.1 to 10 μm, throat radius from 0.1 to 7 μm, pore throat ratio from 0 to 4, and coordination number from 0 to 7. The overall pore throat ratio, throat radius and poor throat connectivity are the main reasons for the low core permeability in the study area. It is found that the 3D digital core technology can more accurately evaluate the internal heterogeneity of rocks, and the calculated pore structure parameters include both connected pore information and isolated pore information. Compared with conventional mercury injection and nuclear magnetic resonance, the 3D digital core technology has obvious advantages in the evaluation of rock micro pore structure.

Digital Core Modeling Based on Pretrained Generative Adversarial Neural Networks

Accurately establishing a 3D digital core model is of great significance in oil and gas production. The physical experiment method and numerical modeling method are common modeling methods. With the development of deep learning technology, a variety of deep learning algorithms have been applied to digital core modeling. The digital core modeling method based on generative adversarial neural networks (GANs) has attracted wide attention due to its good quality and simple generation process. The disadvantage of this method is that the network needs thousands of trainings to achieve acceptable results. For this reason, this paper proposes to use the pretrained GANs for digital core modeling training, which can greatly reduce the number of network training while ensuring the core modeling effect. We can use the presented method to quickly complete the training and use the trained generator model to obtain multiple digital cores. By analyzing the quality of the generated cores from multiple aspects, it is revealed that the properties of the generated cores are in good agreement with the ones of the real core samples. The results indicate the reliability of the pretrained GAN method.