TY - JOUR
AU - Li,, Yafen
AB - Abstract X-ray CT scanning is one of the main methods to study the pore structure of the rocks. However, low resolution is currently one of the major challenges of this method, which does not allow for the identification of microscopic pores. Even though adopting images of higher resolutions could reflect the microscopic pores of the rocks, due to the small size of the scanned rock sample in the x-ray CT scanning method, the rock parameters cannot be effectively obtained, especially for the rocks with strong heterogeneity. This would cause the loss of important pore information, such as fractures’ information. Focusing on this problem, the current research tries to propose a method for constructing high-precision digital rocks of complexly porous rocks. This method is based on the simultaneous application of x-ray CT scanning images, nuclear magnetic resonance measurements, as well as mercury injection experimental data and fractal discrete fracture network. These methods effectively compensate for the shortcomings of the scanning method where it cannot capture pores smaller than the scanning resolution and for the lack of fracture information due to rock sampling. The research results showed that compared with the digital rock models constructed by single-resolution scanning, the high-precision complex pore digital rock constructed by this method can effectively improve the accuracy of the results of porosity and permeability calculations. These results were closer to the results of rock physics experiments. This approach provides a solid foundation for the numerical simulation of rock parameters in unconventional reservoirs with complex pore structures, such as carbonate, tight sandstone and shale reservoirs. digital rock, rock physics experiment, nuclear magnetic resonance (NMR), mercury injection, multi-scale integration, fractal discrete fracture network 1. Introduction Rock physics experiments have many difficulties in studying reservoirs, such as carbonate, tight sandstone and shale, which have complex pore structures. For instance, in the case of carbonate reservoirs, it is difficult to carry out low porosity and low permeability rock displacement experiments and obtain representative fractured rock samples. Rock physics experiments cannot quantitatively study the effects of the reservoir’s microscopic parameters on its macroscopic physical properties. With the wide application of x-ray CT scanning technology in rock physics experiments and the development of computer technology, digital rocks constructed by the CT scanning technique can well characterize the microscopic pore structure, which sets up the bridge between the microscopic pore structure and macroscopic physical properties (Liang et al1998, Okabe and Blunt 2005, Wu et al2006, Liu et al2017). As a research platform for microscopic seepage and acoustic characteristics, digital rock technology is of great significance in the research and application of rock physical properties as porosity and permeability (Valvatne and Blunt 2004, Jiang et al2011, Jiang et al2016, Dong et al2018). Due to the limitations of scanning resolution, it is difficult to accurately identify the microscopic pores in unconventional reservoirs. Accordingly, there is always some deviation between the numerical simulation results and experimental results. Besides, owing to the problem of obtaining rock samples, it may also result in the loss of some pore information, especially fracture information. In this paper, the high-precision complex pore digital rock was constructed based on CT scanning images combined with nuclear magnetic resonance (NMR), mercury injection experimental data and a fractal discrete fracture network. Compared to the single-resolution scanning digital rock, the high-precision complex pore digital rock constructed by this method effectively solved the problems associated with the lack of microscopic pore, as well as low porosity and poor connectivity of pores, which happen due to the limitations of scanning resolution and the lack of fracture information due to the sampling process. This method could effectively reflect the real characteristics of the fracture information, for which a great improvement was observed in determining porosity and permeability where the results were much closer to the results of rock physics experiments. This method provides an effective solution in numerical simulation of physical properties of the reservoir rocks’ having complex pore structures. 2. Constructing digital rock of microscopic pores NMR and mercury injection experimental data can effectively reflect the distribution of rock pore sizes and throat sizes. In order to construct digital rocks of microscopic pores smaller than the scanning resolution, it is necessary to cut out the portion where the radius of pores and throats are smaller than the scanning resolution and use that portion as the input to generate random networks. 2.1. Acquisition of rock pore radius The attenuation of spin echo trains in NMR phenomena is a function of the number and distribution of hydrogen nuclei in fluids, where the echo amplitude decreases with the increase of time. Therefore, the decay rate of nuclear magnetic echo trains in saturated water rocks can be used to reflect the pore structural information. The initial amplitude of the echo train can be used to obtain porosity by choosing an appropriate scale. When the hydrophilic rock is completely saturated with water, the T2 value of a single pore is proportional to the surface/volume ratio of the pore, that is, the size of the pore. Accordingly, the observed T2 distribution of all pores gathered together represents the pore radius distribution of the rock. Through determining the appropriate conversion factor (factor related to surface relaxation), the pore radius distribution will basically coincide with the T2 curve only by a reasonable lateral shift. The dependence of the T2 relaxation time on the pore size could be represented by equation (1) as (Xiao et al2016): 1T2=ρ2SV, 1 where T2 is transverse nuclear magnetic resonance relaxation, ρ2 is surface relaxivity and (S/V) is the specific surface area of pore. 2.2. Acquisition of rock throat radius A mercury injection experiment mainly reflects the distribution of rock throat radius. A high-pressure mercury injection could push mercury into pores, including those with throat sizes of 4 nm, with a pressure of 180 MPa. However, if the pressure increases to 450 MPa, the injected mercury can even inter pore throats of 1 nm. This can effectively surpass the limitations of the CT scanning resolution. Due to the almost constant wetting angle of mercury and rock solids, the relationship between capillary pressure and throat radius can be determined as follows (Golsanami et al2015, Li et al2017): pc=2σcosθr. 2 Where pc is capillary pressure, θ is the wetting angle of mercury and rock solid surface, σ is the surface tension of mercury-air system and r is the throat radius. In practical applications, the surface tension of mercury and the wetting angle are usually considered to be about 480 mN m-1 and 140°, respectively. So the aforementioned formula would be written as follows: pc≈0.735r. 3 2.3. Generation of microscopic pore digital rock In the present research, the porosity of digital rock obtained by CT scanning was compared with the porosity measured by rock physics experiments and then the loss of porosity was calculated. Then, based on the intercepted information of the pore and throat radius, the pore network topology was constructed by using a stochastic network method. In the next step, the network model was generated and discretized into microscopic pore digital rock. It should be mentioned that the porosity of microscopic pore digital rock after the discretization was consistent with the loss of porosity. Herein, the steps that we took to generate this digital rock were as follows: At first, the size of the random network to be constructed was determined. Then, a certain number of pores were randomly placed in the network and each pore was given an index value in the range of [1, N], where N is the total number of pores given we have geometry and topology information of each pore information including coordination number, pore radius and pore volume. Finally, we used the following equation to calculate the weight of each pore: Pi=Di-DminDmax-Dminn. 4 In this formula, Pi is the weight of the ith pore, Di is the diameter of the ith pore, and Dmin and Dmax are the diameters of the smallest and largest pores, respectively. Meanwhile, n is the correlation coefficient where if n = 0, no correlation between pores and throats exists; nevertheless, n < 0 represents that the probability of small pores being connected by means of large throats is high, and when n > 0, larger pores have a greater probability of being connected through larger throats. According to the coordination number, the first pore is connected to the nearest few pores, and then the second pore is connected to the nearest few pores and so on. Meanwhile, at the same time, the number of connected pores is reduced by one. This process is repeated until the connections of the last pore are completed. After all the pores in the network are connected, the weight of each throat would be calculated following the formula of equation (5): Ti=P1+P22. 5 Where Ti is the weight of ith throat, and P1 and P2 are the weights of the pores connected to the throat, respectively. For each throat, the radius value of throat is assigned according to the magnitude of the weight, where the minimum weight is assigned to the smallest throat radius. 3. Construction of fractured digital rock Fractures are common in carbonate, tight sandstone and shale reservoirs. For these reservoirs, the rock physical properties are closely related to the scale. Numerous studies have shown that fractal theory can be used to describe fracture information. Fractals have self-similarity and scale-independent features. Therefore, the fractal characteristics of fractures are independent of the measurement scale. In our research, a fractal discrete model of a fracture network was constructed using the fractal theory and was applied to the digital rock through which the fractured digital rock representing fractured reservoirs was obtained. Currently, there are two major methods for the generation of a fractal discrete fracture network which is generating either the length distribution of fractures, or the center point distribution of them. Since the fracture density value used to generate fracture length distribution is related to the scale, the same fracture density value cannot be used for the generation of fracture networks having different scales. However, the fracture density value used to generate the distribution of fracture center points is independent of the scale (Davy et al1990, Piggott 1997, Bour et al2002). Therefore, the distribution of fracture center points could be used to obtain fractal discrete fracture networks. 3.1. Fracture shape model With regards to a fracture’s shape, there are two important factors that need to be considered. On one hand, there is the fracture’s shape that should be as close as possible to the shape of the real fracture. On the other hand, there are the parameters that are used to characterize the shape of fractures in the current reservoir characterization technology. The more accurate the description of the fracture’s shape is, the higher the required reservoir characterization and prediction techniques are. The most widely used crack shapes are the Baecher disk model and the Veneziano polygon model (Baecher et al1977). The disk model, which was used in our study, does not need to consider the roughness of the fracture surface, and its attribute parameters mainly include position (central coordinate), size (length, opening degree) and attitude (dip angle, inclination) of the fractures. 3.2. Random simulation for the fracture center point The center points of the fracture can be generated by using the Poisson point process or the doubling cascade method. The points generated by applying the Poisson point process (Marmarelis and Berger 2005) are evenly distributed throughout the space and far from the distribution of real fracture. The fractures produced by the doubling cascade method exhibit aggregation behavior and are closer to the distribution of real fracture (Darcel et al2003). Therefore, the doubling cascade method could be used to generate the distribution of fracture center point in the current research. The doubling cascade method is an iterative method, and the steps for the generation of fracture center points are as shown in equation (6). The fracture producing area is divided into a series of sub-areas, and each sub-area is given a probability Pi, which can be calculated as: ∑i=1nPiq(1/sr)(q-1)Dq=1. 6 Herein, Pi is the probability, sr is the side ratio of parent area to child area, q indicates the number of fractal dimensions and is equal to 2 because of the adoption of totally two fractal dimensions Dc and Dl, and Dq represents the multifractal dimension. In the next step, the sub-region is further divided into smaller sub-regions and the current sub-regions become the parent region. A probability is assigned to each sub-region according to equation (6) and is multiplied by the probability of the parent region as the probability of the sub-region. These steps are repeated until the specified number of iterations is reached. At the end, each sub-region is given a value P which falls in the range of 0 to 1. Comparing the probability value of each sub-region with P, if this probability is greater than P, a fracture center point will be generated in the sub-region; otherwise, no center points will be generated. 3.3. Random simulation of fracture size The size of the fracture refers to its length and aperture opening. The length of the fracture refers to the diameter of the circle having the same area as the fracture surface. Because the shape of the fracture is being represented by a circular surface, the diameter of the circular surface is the length of the fracture. Therefore, the length of the fracture can be easily calculated. At present, there are two main approaches to deal with the fracture opening. The first one is to randomly generate the fracture opening using a certain random distribution function. The other one is to establish the correlation between the fracture opening and the length and calculate the opening of the crack according to its length (Song and Xu 2004). Both of these methods assume that the fractures are parallel, so the opening of each fracture is a fixed value and the resulting fracture porosity does not reflect the fracture porosity in the real situation. Because the fracture opening is random, the fracture porosity generated by the former method is not fixed. Considering that the porosity of the fractures corresponding to the larger lengths is obviously larger, this situation is applicable to tension joints but not to shear joints. Therefore, the porosity obtained by the latter method would be larger. Considering the problems of the two aforementioned methods in generating fracture opening, the fractal theory was used for this purpose. Many algorithms can be used to generate random numbers that fit the fractional Brownian motion model (Cox and Wang 1993, McGaughey and Aitken 2000). Some examples of these algorithms we would mention are fast Fourier transform filtering algorithm (FFT), random mid-point displacement method (RMD), continuous random increase algorithm (SRA) and improved continuous random increment algorithm (MSRA). Due to its relatively simple and high computational efficiency, in current research, the MSRA algorithm was used to generate fracture openings. The specific steps that were taken started by selecting a square area with side length l to indicate the surface of the fracture. Herein, the four vertices of the area were represented by 1 and each vertex was given a random initial value that satisfied a standard normal distribution with a mean of zero and a variance of σ02, i.e. N(0, σ02 ⁠). Then, based on the values of the four corner points, a linear interpolation method was used to insert the point 2 in the center of the square area and, following a similar trend, all points were added with a random number that satisfied N(0, σ12 ⁠). Next, linear interpolation was used to insert a midpoint, denoted by 3, for each edge of a square region based on the values of adjacent corners and a random number that satisfied N(0, σ22 ⁠) for all points. Finally, these steps were repeated and after N-step calculations, we could get a (2N + 1) × (2N + 1) matrix. A random number that satisfied N(0, σj2 ⁠) was added to each point in the matrix until σj2 got close to σ02. The resulting matrix was distribution of fracture openings. 3.4. Random simulation of fracture occurrences Fracture occurrences follow the standard definition in the field of geology (figure 1). Assume that OP is the unit normal vector of the fracture surface and ON is its projection on the horizontal plane, while the positive direction of the Y axis is defined as the true north direction. The dip angle β of the fracture is defined as the angle between the fracture surface and the horizontal plane. The fracture tendency α is defined as the positive clockwise angle between the north direction and ON. In the stochastic modeling of fracture occurrences, for the sake of calculation, the occurrence of fracture is not represented by β and α, but is presented by the unit normal vector OP of the fracture surface. OP can be represented by the spherical coordinate θ and φ, where θ is defined as the angle between OP and the Z axis, and φ is tendency of the fracture. By knowing the fracture angle β and the inclination α, the following equations can be used to obtain θ and φ: φ=α. 7 θ=β. 8 Since the fracture angle β and the inclination α are equal to the fracture surface normal vectors θ and φ, respectively, the fracture surface normal vector θ is also called the angle of the fracture; likewise, φ is called the tendency of the fracture. Figure 1. View largeDownload slide Standard definition of fractures in the field of geology. Figure 1. View largeDownload slide Standard definition of fractures in the field of geology. In a set of fractures in a 3D space, let the angle and tendency of the ith fracture be represented by iθ and iφ, respectively. While M is defined as the average unit normal vector of the group of fractures, the angle and tendency are θ0 and φ0, respectively. The angular and azimuthal distances between the ith fracture and M are also iψ and iζ, respectively. The angular distance between the ith fracture and M is randomly generated using the Fisher distribution, and the azimuth distance is randomly generated by using the standard normal distribution. Fracture occurrence simulation can be realized by rotations of two coordinates. 4. Reconstruction of high-precision complex pore digital rock From the comparison of many digital rock modeling methods, it can be seen that the CT scanning technology is the best way to reflect the real microscopic pore structure of the rock. However, there are always some differences in the CT-obtained rock porosities when different resolutions are used to scan the same rock. Under this situation, no matter how high the scanning resolution is, the porosity of digital rock is still smaller than the results of the rock physical measurement of porosity, which indicates the existence of some microscopic pores within the rock structure that are smaller than the scanning resolution of the CT device. Due to the existence of these microscopic pores, the porosity of digital rock is relatively low compared with the laboratory pores which affects the connectivity of the digital rock’s pore network. Besides, the lack of information about partial fractures that may result from coring has great influence on the subsequent numerical simulation of rock parameters. The construction method of high-precision complex pore digital rock was divided into the following steps as shown in figure 2. At first, the rock samples were scanned by the x-ray CT scanning device to build the fundamental digital rock model of the studied sample. Then, the distribution of throat radius and pore radius were obtained from mercury injection and NMR measurements. Then, the part of the digital rock for which the throat radius was smaller than the scanning resolution was intercepted and was used as the input to generate a random fracture network. Comparing the porosity of digital rock with that obtained from rock physics experiments, the loss of porosity was calculated and then the random network method was used to construct the topology of pore network. The pore network model was transformed into the microscopic pore digital rock by applying the grid method, and the multiscale fusion method was used to overlay the digital rock of the Mercury injection and nuclear magnetic resonance method into the CT scanning digital rock (Yao et al2007, Cui et al2017, Dong et al2017). The fracture generation and application technology was also applied to the x-ray CT digital rock, and finally the high-precision complex pore digital rock was constructed successfully. Figure 2. View largeDownload slide Flowchart of the reconstruction approach of high-precision complex pore digital rock. Figure 2. View largeDownload slide Flowchart of the reconstruction approach of high-precision complex pore digital rock. 5. Results and discussions We employed rock sample YP-1 as an example to verify the feasibility and accuracy of our method. We constructed high-precision complex pore digital rock for this sample, as illustrated in figure 3. The porosity results of rock physical measurement and the numerical simulation of the x-ray CT digital rock model were as shown in table 1. The loss of porosity could be calculated through comparing the results mentioned previously. The experimental data of mercury injection and NMR for sample YP-1 were as shown in figure 4 and could be used to obtain the information of pore radius and throat radius. Figure 3. View largeDownload slide Digital rock model constructed by x-ray CT scanning technology. The image size was 400 × 400 × 400 pixels and the resolution was 3.60 um/pixel. The blue and red components represent the rock skeleton and rock pores, respectively. Figure 3. View largeDownload slide Digital rock model constructed by x-ray CT scanning technology. The image size was 400 × 400 × 400 pixels and the resolution was 3.60 um/pixel. The blue and red components represent the rock skeleton and rock pores, respectively. Table 1. Comparison between the results of rock physical numerical simulation and rock physics experiments. X-ray CT digital rock High-precision digital rock Rock physics experiment Porosity/% 9.14 9.76 10.03 Relative error/% 8.87 2.69 — Permeability/(×10-3 um2) 0.076 0.083 0.087 Relative error/% 12.64 4.60 — X-ray CT digital rock High-precision digital rock Rock physics experiment Porosity/% 9.14 9.76 10.03 Relative error/% 8.87 2.69 — Permeability/(×10-3 um2) 0.076 0.083 0.087 Relative error/% 12.64 4.60 — View Large Table 1. Comparison between the results of rock physical numerical simulation and rock physics experiments. X-ray CT digital rock High-precision digital rock Rock physics experiment Porosity/% 9.14 9.76 10.03 Relative error/% 8.87 2.69 — Permeability/(×10-3 um2) 0.076 0.083 0.087 Relative error/% 12.64 4.60 — X-ray CT digital rock High-precision digital rock Rock physics experiment Porosity/% 9.14 9.76 10.03 Relative error/% 8.87 2.69 — Permeability/(×10-3 um2) 0.076 0.083 0.087 Relative error/% 12.64 4.60 — View Large Figure 4. View largeDownload slide (a) Lab-measured NMR T2 curve for sample YP-1, (b) the experimental results of mercury injection, (c) the cumulative probability distribution of pore radius and (d) the cumulative probability distribution of throat radius for the sample. Figure 4. View largeDownload slide (a) Lab-measured NMR T2 curve for sample YP-1, (b) the experimental results of mercury injection, (c) the cumulative probability distribution of pore radius and (d) the cumulative probability distribution of throat radius for the sample. Figure 5. View largeDownload slide (a) 3D microscopic digital rock model constructed using NMR and mercury injection data; (b) corresponding high-precision digital rock model. Figure 5. View largeDownload slide (a) 3D microscopic digital rock model constructed using NMR and mercury injection data; (b) corresponding high-precision digital rock model. Considering the real operational conditions and data, a very high resolution would lead to very high data size and analysis difficulties. The original resolution of YP-1 digital rock was 3.60 um and was increased by four times only reaching up to 0.90 um. This increase would not result in further data handling difficulties. Then, the pore and throat radius in the range of 0.90–3.60 um were intercepted by mercury injection and NMR technique and were used as the input parameters to generate random networks. We cut out a sub-sample of 100 × 100 × 100 pixels digital rock called ZYP-1 at a random position on the YP-1sample’s digital model. The porosity and permeability of this sub-sample were 9.14% and 0.076 × 10-3 um2, respectively. By increasing the resolution to 0.90 um, the digital rock size changed to 400 × 400 × 400 pixels. Then, we used a random network to construct a network model (figure 5(a)) and discretized it into a 400 × 400 × 400 pixels digital rock. Finally, a high-precision digital rock model, as shown in figure 5(b), could be obtained by superimposing the sub-sample digital rock ZYP-1 and the result of the random network model. Herein, the porosity and permeability of a digital rock model obtained through superimposing were 9.76% and 0.083 × 10-3 um2, respectively, which were closer to the rock physics experiments’ results. It can be seen from the table 1 that compared to the digital rock constructed by single-resolution scanning, porosity and permeability of the high-precision digital rock obtained by using single-resolution scanning combined with mercury injection and the NMR technique were both greatly improved and were closer to those results obtained by rock physics experiments. The method described earlier was applied to generate a 3D fractal discrete fracture network model figure 6(a). The fractal dimension of the center point of the fracture generated by the simulation was 1.58 and the fracture length fractal calculation result of the simulated fracture was 1.08. Table 2 shows the fractal dimension of the fractures produced by the simulation and the fractal parameters obtained by the NMR measurement. Through comparison, it was revealed that the fractal dimension of the fracture center point, azimuth and fractal dimension of its length, the fracture fractal dimension and development azimuth of the fractal fracture generated by the simulation were consistent with the information acquired by NMR, which verifies the accuracy of the method for generating the fractal discrete fracture network. The geometrical transformation technique was used to mesh the fractal discrete fracture network model. The resolution was determined by the digital resolution of the rock matrix, which was 3.60 um. Finally, the discretized 400 × 400 × 400 pixels fractal discrete fracture network model was superimposed with a high-precision digital rock to obtain the high-precision complex pore digital rock (figure 7). The imposed pore network could reflect the actual fracture distribution and morphology, which provided an effective solution for numerical simulation of rock physical properties of a complex reservoir. Figure 6. View largeDownload slide (a) 3D fractal discrete fracture network model and (b) digital rock model with fractal discrete fractures. Figure 6. View largeDownload slide (a) 3D fractal discrete fracture network model and (b) digital rock model with fractal discrete fractures. Table 2. Comparison of fractal parameters of fractures generated by simulation and obtained by NMR measurement. The first set of fractures The second set of fractures Method of measurement Fracture center point fractal dimension Fracture length fractal dimension Trend Fisher constant Trend Fisher constant Numerical simulation method 1.58 1.08 77.6 45.7 112 11.7 NMR measurement 1.61 1.12 76.8 46.9 108 10.3 The first set of fractures The second set of fractures Method of measurement Fracture center point fractal dimension Fracture length fractal dimension Trend Fisher constant Trend Fisher constant Numerical simulation method 1.58 1.08 77.6 45.7 112 11.7 NMR measurement 1.61 1.12 76.8 46.9 108 10.3 View Large Table 2. Comparison of fractal parameters of fractures generated by simulation and obtained by NMR measurement. The first set of fractures The second set of fractures Method of measurement Fracture center point fractal dimension Fracture length fractal dimension Trend Fisher constant Trend Fisher constant Numerical simulation method 1.58 1.08 77.6 45.7 112 11.7 NMR measurement 1.61 1.12 76.8 46.9 108 10.3 The first set of fractures The second set of fractures Method of measurement Fracture center point fractal dimension Fracture length fractal dimension Trend Fisher constant Trend Fisher constant Numerical simulation method 1.58 1.08 77.6 45.7 112 11.7 NMR measurement 1.61 1.12 76.8 46.9 108 10.3 View Large Figure 7. View largeDownload slide A 3D high-precision complex pore digital rock model. Figure 7. View largeDownload slide A 3D high-precision complex pore digital rock model. 6. Conclusions In this paper, a method for constructing high-precision complex pore digital rock is proposed, which effectively compensates for the lack of microscopic pore structural information for pores smaller than the scanning resolution and also for the lack of fracture information caused by rock sampling. In particular, due to the small size of the scanned rock samples, the rock parameters cannot be effectively obtained for highly homogeneous rocks, which leads to the lack of rock porosity and fracture information. However, our proposed method provides a solution to this challenge as well. According to the results, the following conclusions could be drawn: High-precision digital rock constructed based on the CT scanning images of rock, combined with the experimental mercury injection and NMR data can reflect both groups of the pores being larger than or smaller than the scanning resolution. Moreover, the petrophysical properties of the high-precision digital rock constructed by this method, i.e. porosity and permeability, were close enough to the porosity and permeability obtained through experimental measurements. Based on the fractal theory, a random simulation method was used to generate fractal discrete fracture network possessing features of fracture and then digitize the network. The high-precision complex pore digital rock was constructed by overlaying this digitized network with a high-precision digital rock and could reflect the microscopic structure of the rock. Herein, the true distribution characteristics of fractures effectively compensate for the lack of fracture information caused during coring. The high-precision complex pore digital rock modeling method can solve the problem of the lack of micro-pore information, low porosity and weak pore connectivity, which are due to the scanning resolution, as well as make up for the lack of cracks’ information caused by the rock coring. This method breaks the limitation of the CT scanning instrument’s resolution and provides an effective solution for numerical simulation of complex reservoirs’ rock physical properties. Acknowledgments The financial support for this work was received from the National Natural Science Foundation of China (grant no. 41574122 and no. 41874138), the National Science and Technology Major Project (grant no. 2016ZX05006002-004), the Fundamental Research Funds for the Central Universities (grant no. 18CX06027A) and Innovation Research Funds for the China University of Petroleum (grant no. YCX2018003). 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TI - A method to construct high-precision complex pore digital rock
JF - Journal of Geophysics and Engineering
DO - 10.1088/1742-2140/aae04e
DA - 2018-12-01
UR - https://www.deepdyve.com/lp/oxford-university-press/a-method-to-construct-high-precision-complex-pore-digital-rock-nrDDFGl3dg
SP - 2695
VL - 15
IS - 6
DP - DeepDyve
ER -