Improved Permeability Prediction of Porous Media by Feature Selection and Machine Learning Methods Comparison
Publication: Journal of Computing in Civil Engineering
Volume 36, Issue 2
Abstract
Permeability of subsurface porous media is one of the primary factors that affect fluid transport in porous rock. However, accurate prediction of rock permeability is a challenging task due to its intricate pore network. Development of digital rocks provides an effective approach to reveal and characterize the pore network. In this paper, a combination of digital rock petrophysics and ensemble machine learning (ML) models is proposed to improve the permeability prediction of subsurface porous media. The permeability of the numerically generated porous samples as outputs was determined by the lattice Boltzmann method (LBM). The five most important parameters (porosity, tortuosity, fractal dimension, average pore diameter, and coordination number) were selected as inputs for the permeability prediction. To improve the accuracy, feature selection and ML methods comparisons were conducted. Three feature selection methods based on expert knowledge, correlation coefficient, and importance score were compared. Moreover, a comparison was performed on six ML methods (support vector machine, artificial neural network, decision tree, random forest, gradient-boosting machine, and Bayesian ridge regression) that were optimized by particle swarm optimization (PSO). The results indicated that (1) the feature selection based on the expert knowledge obtained a higher performance than the groups based on the correlation coefficient and importance score, implying the importance of expert knowledge on feature selection, and thus on ML performance; (2) artificial neural network with hyperparameter tuning achieved the best performance in predicting permeability; and (3) the optimized ML method outperformed the empirical equations in predicting permeability. In conclusion, this study provides a fast and reliable approach predicting permeability of subsurface porous media based on numerically generated porous images. Moreover, the proposed framework can be further extended to determine other petrophysical properties, for example, the relative permeability and thermal conductivity.
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Data Availability Statement
All the data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
References
Akande, K. O., T. O. Owolabi, S. O. Olatunji, and A. AbdulRaheem. 2017. “A hybrid particle swarm optimization and support vector regression model for modelling permeability prediction of hydrocarbon reservoir.” J. Petrol. Sci. Eng. 150 (Feb): 43–53. https://doi.org/10.1016/j.petrol.2016.11.033.
Alipour, M., D. K. Harris, and G. R. Miller. 2019. “Robust pixel-level crack detection using deep fully convolutional neural networks.” J. Comput. Civ. Eng. 33 (6): 04019040. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000854.
Aliyu, M. D., and H. P. Chen. 2018. “Enhanced geothermal system modelling with multiple pore media: Thermo-hydraulic coupled processes.” Energy 165 (Dec): 931–948. https://doi.org/10.1016/j.energy.2018.09.129.
Al Khalifah, H., P. W. J. Glover, and P. Lorinczi. 2020. “Permeability prediction and diagenesis in tight carbonates using machine learning techniques.” Mar. Pet. Geol. 112 (Feb): 104096. https://doi.org/10.1016/j.marpetgeo.2019.104096.
An, S., J. Yao, Y. Yang, L. Zhang, J. Zhao, and Y. Gao. 2016. “Influence of pore structure parameters on flow characteristics based on a digital rock and the pore network model.” J. Nat. Gas Sci. Eng. 31 (Apr): 156–163. https://doi.org/10.1016/j.jngse.2016.03.009.
Andersson, L., A. Herring, S. Schlüter, and D. Wildenschild. 2018. “Defining a novel pore-body to pore-throat “morphological aspect ratio” that scales with residual non-wetting phase capillary trapping in porous media.” Adv. Water Resour. 122 (Dec): 251–262. https://doi.org/10.1016/j.advwatres.2018.10.009.
Andrä, H., N. Combaret, J. Dvorkin, E. Glatt, J. Han, M. Kabel, Y. Keehm, F. Krzikalla, M. Lee, and C. Madonna. 2013. “Digital rock physics benchmarks—Part I: Imaging and segmentation.” Comput. Geosci. 50 (9): 25–32. https://doi.org/10.1016/j.cageo.2012.09.005.
Araya-Polo, M., F. O. Alpak, S. Hunter, R. Hofmann, and N. Saxena. 2019. “Deep learning–driven permeability estimation from 2D images.” Comput. Geosci. 2019 (Nov): 1–10. https://doi.org/10.1007/s10596-019-09886-9.
Becker, J., C. Wieser, S. Fell, and K. Steiner. 2011. “A multi-scale approach to material modeling of fuel cell diffusion media.” Int. J. Heat Mass Transfer 54 (7–8): 1360–1368. https://doi.org/10.1016/j.ijheatmasstransfer.2010.12.003.
Berg, C. F. 2014. “Permeability description by characteristic length, tortuosity, constriction and porosity.” Transp. Porous Media 103 (3): 381–400. https://doi.org/10.1007/s11242-014-0307-6.
Bergen, K. J., P. A. Johnson, V. Maarten, and G. C. Beroza. 2019. “Machine learning for data-driven discovery in solid earth geosciences.” Science 363 (6433): 323. https://doi.org/10.1126/science.aau0323.
Blunt, M. J., B. Bijeljic, H. Dong, O. Gharbi, S. Iglauer, P. Mostaghimi, A. Paluszny, and C. Pentland. 2013. “Pore-scale imaging and modelling.” Adv. Water Resour. 51 (3): 197–216. https://doi.org/10.1016/j.advwatres.2012.03.003.
Borujeni, A. T., N. M. Lane, K. Thompson, and M. Tyagi. 2013. “Effects of image resolution and numerical resolution on computed permeability of consolidated packing using LB and FEM pore-scale simulations.” Comput. Fluids 88 (Dec): 753–763. https://doi.org/10.1016/j.compfluid.2013.05.019.
Bultreys, T., W. De Boever, and V. Cnudde. 2016. “Imaging and image-based fluid transport modeling at the pore scale in geological materials: A practical introduction to the current state-of-the-art.” Earth Sci. Rev. 155 (Apr): 93–128. https://doi.org/10.1016/j.earscirev.2016.02.001.
Cai, J., Z. Zhang, W. Wei, D. Guo, S. Li, and P. Zhao. 2019. “The critical factors for permeability-formation factor relation in reservoir rocks: Pore-throat ratio, tortuosity and connectivity.” Energy 188 (Dec): 116051. https://doi.org/10.1016/j.energy.2019.116051.
Cao, G., M. Lin, W. Jiang, H. Li, Z. Yi, and C. Wu. 2017. “A 3D coupled model of organic matter and inorganic matrix for calculating the permeability of shale.” Fuel 204 (Sep): 129–143. https://doi.org/10.1016/j.fuel.2017.05.052.
Cao, P., J. Liu, and Y.-K. Leong. 2016. “General gas permeability model for porous media: Bridging the gaps between conventional and unconventional natural gas reservoirs.” Energy Fuels 30 (7): 5492–5505. https://doi.org/10.1021/acs.energyfuels.6b00683.
Carman, P. C. 1961. L’écoulement des gaz à travers les milieux poreux. Saclay, France: Institut national des sciences et techniques nucléaires.
Carman, P. C. 1997. “Fluid flow through granular beds.” Chem. Eng. Res. Des. 75 (54): 32–48. https://doi.org/10.1016/S0263-8762(97)80003-2.
Chen, L., L. Zhang, Q. Kang, H. S. Viswanathan, J. Yao, and W. Tao. 2015. “Nanoscale simulation of shale transport properties using the lattice Boltzmann method: Permeability and diffusivity.” Sci. Rep. 5 (1): 1–8. https://doi.org/10.1038/srep08089.
Chen, X., and G. Yao. 2017. “An improved model for permeability estimation in low permeable porous media based on fractal geometry and modified Hagen-Poiseuille flow.” Fuel 210 (2): 748–757. https://doi.org/10.1016/j.fuel.2017.08.101.
Civan, F., D. Devegowda, and R. F. Sigal. 2013. “Critical evaluation and improvement of methods for determination of matrix permeability of shale.” In Proc., SPE Annual Technical Conf. and Exhibition. New Orleans: Society of Petroleum Engineers.
Congdon, P. 2007. Bayesian statistical modelling. Hoboken, NJ: Wiley.
Connell, L. D., S. Mazumder, R. Sander, M. Camilleri, Z. Pan, and D. Heryanto. 2016. “Laboratory characterisation of coal matrix shrinkage, cleat compressibility and the geomechanical properties determining reservoir permeability.” Fuel 165 (Apr): 499–512. https://doi.org/10.1016/j.fuel.2015.10.055.
Cortes, C., and V. Vapnik. 1995. “Support-vector networks.” Mach. Learn. 20 (3): 273–297.
Dejam, M., and H. Hassanzadeh. 2018. “Diffusive leakage of brine from aquifers during geological storage.” Adv. Water Resour. 111 (10): 36–57. https://doi.org/10.1016/j.advwatres.2017.10.029.
Deng, S., X. Wang, Y. Zhu, F. Lv, and J. Wang. 2019. “Hybrid grey wolf optimization algorithm–based support vector machine for groutability prediction of fractured rock mass.” J. Comput. Civ. Eng. 33 (2): 04018065. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000814.
Erofeev, A., D. Orlov, A. Ryzhov, and D. Koroteev. 2019. “Prediction of porosity and permeability alteration based on machine learning algorithms.” Transp. Porous Media 128 (2): 677–700. https://doi.org/10.1007/s11242-019-01265-3.
Friedman, J., T. Hastie, and R. Tibshirani. 2001. The elements of statistical learning. Berlin: Springer.
Gholami, R., A. Moradzadeh, S. Maleki, S. Amiri, and J. Hanachi. 2014. “Applications of artificial intelligence methods in prediction of permeability in hydrocarbon reservoirs.” J. Petroleum Sci. Eng. 122 (Sep): 643–656. https://doi.org/10.1016/j.petrol.2014.09.007.
Giordano, R., A. Salleo, S. Salleo, and F. Wanderlingh. 1978. “Flow in xylem vessels and Poiseuille’s law.” Can. J. Bot. 56 (3): 333–338. https://doi.org/10.1139/b78-039.
Hassan, M. R., R. M. Fikry, and S. M. Yakout. 2020. “Artificial neural network approach modeling for sorption of cobalt from aqueous solution using modified maghemite nanoparticles.” J. Environ. Eng. 146 (4): 04020013. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001565.
Hou, X., Y. Zhu, S. Chen, and Y. Wang. 2017. “Gas flow mechanisms under the effects of pore structures and permeability characteristics in source rocks of coal measures in Qinshui Basin, China.” Energy Explor. Exploit. 35 (3): 338–355. https://doi.org/10.1177/0144598717700080.
Isied, M., and M. Souliman. 2019. “Fatigue endurance limit model utilizing artificial neural network for asphalt concrete pavements.” In Airfield and highway pavements, 42–50. Reston, VA: ASCE.
Jiang, Z., K. Wu, G. D. Couples, and J. Ma. 2011. “The impact of pore size and pore connectivity on single-phase fluid flow in porous media.” Adv. Eng. Mater. 13 (3): 208–215.
Ju, Y., Y. Sun, J. Tan, H. Bu, K. Han, X. Li, and L. Fang. 2018. “The composition, pore structure characterization and deformation mechanism of coal-bearing shales from tectonically altered coalfields in eastern China.” Fuel 234 (Sep): 626–642. https://doi.org/10.1016/j.fuel.2018.06.116.
Khosravi, K., B. T. Pham, K. Chapi, A. Shirzadi, H. Shahabi, I. Revhaug, I. Prakash, and D. Tien Bui. 2018. “A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, Northern Iran.” Sci. Total Environ. 627 (Jun): 744–755. https://doi.org/10.1016/j.scitotenv.2018.01.266.
Kozeny, J. 1927. “Uber kapillare leitung der wasser in boden.” Royal Acad. Sci. 136 (2a): 271–306.
Kuhn, M., and K. Johnson. 2013. Applied predictive modeling. Berlin: Springer.
Lai, J., G. Wang, Z. Wang, J. Chen, X. Pang, S. Wang, Z. Zhou, Z. He, Z. Qin, and X. Fan. 2018. “A review on pore structure characterization in tight sandstones.” Earth Sci. Rev. 177 (Feb): 436–457. https://doi.org/10.1016/j.earscirev.2017.12.003.
Li, X., Z. Liu, S. Cui, C. Luo, C. Li, and Z. Zhuang. 2019. “Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning.” Comput. Methods Appl. Mech. Eng. 347 (Apr): 735–753. https://doi.org/10.1016/j.cma.2019.01.005.
Lindley, D. V., and A. F. Smith. 1972. “ Bayes estimates for the linear model.” J. R. Stat. Soc.: Series B (Methodological) 34 (1): 1–18.
Liu, K., M. Ostadhassan, J. Zhou, T. Gentzis, and R. Rezaee. 2017. “Nanoscale pore structure characterization of the Bakken shale in the USA.” Fuel 209 (Dec): 567–578. https://doi.org/10.1016/j.fuel.2017.08.034.
Liu, R., Y. Jiang, B. Li, and L. Yu. 2016. “Estimating permeability of porous media based on modified Hagen–Poiseuille flow in tortuous capillaries with variable lengths.” Microfluid. Nanofluid. 20 (8): 120. https://doi.org/10.1007/s10404-016-1783-5.
Liu, S., A. Zolfaghari, S. Sattarin, A. K. Dahaghi, and S. Negahban. 2019. “Application of neural networks in multiphase flow through porous media: Predicting capillary pressure and relative permeability curves.” J. Petroleum Sci. Eng. 180 (Sep): 445–455. https://doi.org/10.1016/j.petrol.2019.05.041.
Mala, G. M., and D. Li. 1999. “Flow characteristics of water in microtubes.” Int. J. Heat Fluid Flow 20 (2): 142–148. https://doi.org/10.1016/S0142-727X(98)10043-7.
Mohammadmoradi, P., and A. Kantzas. 2016. “Pore-scale permeability calculation using CFD and DSMC techniques.” J. Petroleum Sci. Eng. 146 (Oct): 515–525. https://doi.org/10.1016/j.petrol.2016.07.010.
Montessori, A., P. Prestininzi, M. La Rocca, G. Falcucci, S. Succi, and E. Kaxiras. 2016. “Effects of Knudsen diffusivity on the effective reactivity of nanoporous catalyst media.” J. Comput. Sci. 17 (Nov): 377–383. https://doi.org/10.1016/j.jocs.2016.04.006.
Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, and J. Vanderplas. 2011. “Scikit-learn: Machine learning in Python.” J. Mach. Learn. Res. 12 (Oct): 2825–2830.
Porter, M. L., M. Plampin, R. Pawar, and T. Illangasekare. 2015. “ leakage in shallow aquifers: A benchmark modeling study of gas evolution in heterogeneous porous media.” Int. J. Greenhouse Gas Control 39 (Aug): 51–61. https://doi.org/10.1016/j.ijggc.2015.04.017.
Qi, C., Q. Chen, X. Dong, Q. Zhang, and Z. M. Yaseen. 2020. “Pressure drops of fresh cemented paste backfills through coupled test loop experiments and machine learning techniques.” Powder Technol. 361 (6): 748–758. https://doi.org/10.1016/j.powtec.2019.11.046.
Qi, C., A. Fourie, and Q. Chen. 2018a. “Neural network and particle swarm optimization for predicting the unconfined compressive strength of cemented paste backfill.” Constr. Build. Mater. 159 (Jan): 473–478. https://doi.org/10.1016/j.conbuildmat.2017.11.006.
Qi, C., A. Fourie, Q. Chen, and Q. Zhang. 2018b. “A strength prediction model using artificial intelligence for recycling waste tailings as cemented paste backfill.” J. Cleaner Prod. 183 (May): 566–578. https://doi.org/10.1016/j.jclepro.2018.02.154.
Qi, C., X. Tang, X. Dong, Q. Chen, A. Fourie, and E. Liu. 2019. “Towards intelligent mining for backfill: A genetic programming-based method for strength forecasting of cemented paste backfill.” Miner. Eng. 133 (3): 69–79. https://doi.org/10.1016/j.mineng.2019.01.004.
Raeini, A. Q., M. J. Blunt, and B. Bijeljic. 2014. “Direct simulations of two-phase flow on micro-CT images of porous media and upscaling of pore-scale forces.” Adv. Water Resour. 74 (Dec): 116–126. https://doi.org/10.1016/j.advwatres.2014.08.012.
Roe, B. P., H. J. Yang, J. Zhu, Y. Liu, I. Stancu, and G. McGregor. 2005. “Boosted decision trees as an alternative to artificial neural networks for particle identification.” Nucl. Instrum. Methods Phys. Res. Sect. A 543 (2–3): 577–584. https://doi.org/10.1016/j.nima.2004.12.018.
Rokhforouz, M. R., and H. A. Akhlaghi Amiri. 2019. “Effects of grain size and shape distribution on pore-scale numerical simulation of two-phase flow in a heterogeneous porous medium.” Adv. Water Resour. 124 (6): 84–95. https://doi.org/10.1016/j.advwatres.2018.12.008.
Saha, S., F. Gu, X. Luo, and R. L. Lytton. 2019. “Parametric study of modified subgrade reaction model using artificial neural network approach.” In Proc., Geo-Congress 2019, 308–316. Reston, VA: ASCE.
Shi, Q., M. Abdel-Aty, and J. Lee. 2016. “A Bayesian ridge regression analysis of congestion’s impact on urban expressway safety.” Accid. Anal. Preven. 88 (16): 124–137. https://doi.org/10.1016/j.aap.2015.12.001.
Si, L., Z. Li, and Y. Yang. 2018. “Coal permeability evolution with the interaction between nanopore and fracture: Its application in coal mine gas drainage for Qingdong coal mine in Huaibei coalfield, China.” J. Nat. Gas Sci. Eng. 56 (Aug): 523–535. https://doi.org/10.1016/j.jngse.2018.06.032.
Sinshaw, T. A., C. Q. Surbeck, H. Yasarer, and Y. Najjar. 2019. “Artificial neural network for prediction of total nitrogen and phosphorus in US lakes.” J. Environ. Eng. 145 (6): 04019032. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001528.
Song, R., Y. Wang, J. Liu, M. Cui, and Y. Lei. 2019. “Comparative analysis on pore-scale permeability prediction on micro-CT images of rock using numerical and empirical approaches.” Energy Sci. Eng. 7 (6): 2842–2854. https://doi.org/10.1002/ese3.465.
Srisutthiyakorn, N., and G. Mavko. 2017. “The revised Kozeny-Carman equation: A practical way to improve permeability prediction in the Kozeny-Carman equation through pore-size distribution.” In Proc., SEG International Exposition and Annual Meeting. Austin, TX: OnePetro.
Sun, H., J. Yao, Y. C. Cao, D. Y. Fan, and L. Zhang. 2017. “Characterization of gas transport behaviors in shale gas and tight gas reservoirs by digital rock analysis.” Int. J. Heat Mass Transfer 104 (Jan): 227–239. https://doi.org/10.1016/j.ijheatmasstransfer.2016.07.083.
Tahmasebi, P., M. Sahimi, A. H. Kohanpur, and A. Valocchi. 2017. “Pore-scale simulation of flow of and brine in reconstructed and actual 3D rock cores.” J. Petroleum Sci. Eng. 155 (Jul): 21–33. https://doi.org/10.1016/j.petrol.2016.12.031.
Tang, M., H. Zhan, S. Lu, H. Ma, and H. Tan. 2019. “Pore scale displacement simulation based on three fluid phase lattice Boltzmann method.” Energy Fuels 33 (10): 10039–10055. https://doi.org/10.1021/acs.energyfuels.9b01918.
Tian, J., J. Liu, D. Elsworth, Y.-K. Leong, W. Li, and J. Zeng. 2019. “A dynamic fractal permeability model for heterogeneous coalbed reservoir considering multiphysics and flow regimes.” In Proc., SPE/AAPG/SEG Unconventional Resources Technology Conf., 18. Austin, TX: OnePetro.
Tian, J., C. Qi, Y. Sun, Z. M. Yaseen, and B. T. Pham. 2020. “Permeability prediction of porous media using a combination of computational fluid dynamics and hybrid machine learning methods.” Eng. Comput. 37 (7–8): https://doi.org/10.1007/s00366-020-01012-z.
Tian, S., W. Ren, G. Li, R. Yang, and T. Wang. 2017. “A theoretical analysis of pore size distribution effects on shale apparent permeability.” Geofluids 2017 (Jan): 1. https://doi.org/10.1155/2017/7492328.
Trehan, S., and L. J. Durlofsky. 2018. “Machine-learning-based modeling of coarse-scale error, with application to uncertainty quantification.” Computat. Geosci. 22 (4): 1093–1113. https://doi.org/10.1007/s10596-018-9740-x.
Tsakiroglou, C. D., and A. C. Payatakes. 2000. “Characterization of the pore structure of reservoir rocks with the aid of serial sectioning analysis, mercury porosimetry and network simulation.” Adv. Water Resour. 23 (7): 773–789. https://doi.org/10.1016/S0309-1708(00)00002-6.
Valera, M., Z. Guo, P. Kelly, S. Matz, V. A. Cantu, A. G. Percus, J. D. Hyman, G. Srinivasan, and H. S. Viswanathan. 2018. “Machine learning for graph-based representations of three-dimensional discrete fracture networks.” Computat. Geosci. 22 (3): 695–710. https://doi.org/10.1007/s10596-018-9720-1.
Vapnik, V. 1995. The nature of statistical learning theory. Berlin: Springer.
Vapnik, V. N. 1998. Statistical learning theory. New York: Wiley.
Wang, L., S. Wang, R. Zhang, C. Wang, Y. Xiong, X. Zheng, S. Li, K. Jin, and Z. Rui. 2017. “Review of multi-scale and multi-physical simulation technologies for shale and tight gas reservoirs.” J. Nat. Gas Sci. Eng. 37 (Jan): 560–578. https://doi.org/10.1016/j.jngse.2016.11.051.
Wang, L., L. Zhuang, and Z. Zhang. 2019. “Automatic detection of rail surface cracks with a superpixel-based data-driven framework.” J. Comput. Civ. Eng. 33 (1): 04018053. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000799.
Wang, M., J. Wang, N. Pan, and S. Chen. 2007. “Mesoscopic predictions of the effective thermal conductivity for microscale random porous media.” Phys. Rev. E 75 (3): 036702. https://doi.org/10.1103/PhysRevE.75.036702.
Wei, H., S. Zhao, Q. Rong, and H. Bao. 2018a. “Predicting the effective thermal conductivities of composite materials and porous media by machine learning methods.” Int. J. Heat Mass Transfer 127 (Aug): 908–916. https://doi.org/10.1016/j.ijheatmasstransfer.2018.08.082.
Wei, W., J. Cai, J. Xiao, Q. Meng, B. Xiao, and Q. Han. 2018b. “Kozeny-Carman constant of porous media: Insights from fractal-capillary imbibition theory.” Fuel 234 (May): 1373–1379. https://doi.org/10.1016/j.fuel.2018.08.012.
Weston, J., S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, and V. Vapnik. 2001. “Feature selection for SVMs.” In Advances in neural information processing systems, 668–674. La Jolla, CA: Neural Information Processing Systems Foundation.
Wu, J., X. Yin, and H. Xiao. 2018. “Seeing permeability from images: Fast prediction with convolutional neural networks.” Sci. Bull. 63 (18): 1215–1222. https://doi.org/10.1016/j.scib.2018.08.006.
Wu, Y., P. Tahmasebi, C. Lin, L. Ren, and C. Dong. 2019a. “Multiscale modeling of shale samples based on low- and high-resolution images.” Mar. Pet. Geol. 109 (Nov): 9–21. https://doi.org/10.1016/j.marpetgeo.2019.06.006.
Wu, Y., P. Tahmasebi, C. Lin, M. A. Zahid, C. Dong, A. N. Golab, and L. Ren. 2019b. “A comprehensive study on geometric, topological and fractal characterizations of pore systems in low-permeability reservoirs based on SEM, MICP, NMR, and X-ray CT experiments.” Mar. Pet. Geol. 103 (May): 12–28. https://doi.org/10.1016/j.marpetgeo.2019.02.003.
Xue, X. 2017. “Prediction of slope stability based on hybrid PSO and LSSVM.” J. Comput. Civ. Eng. 31 (1): 04016041. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000607.
Yang, L., L. Ai, K. Xue, Z. Ling, and Y. Li. 2018. “Analyzing the effects of inhomogeneity on the permeability of porous media containing methane hydrates through pore network models combined with CT observation.” Energy 163 (9): 27–37. https://doi.org/10.1016/j.energy.2018.08.100.
Yu, B., and P. Cheng. 2002. “A fractal permeability model for bi-dispersed porous media.” Int. J. Heat Mass Transfer 45 (14): 2983–2993. https://doi.org/10.1016/S0017-9310(02)00014-5.
Zhang, S., and H. H. Liu. 2016. “Porosity–permeability relationships in modeling salt precipitation during sequestration: Review of conceptual models and implementation in numerical simulations.” Int. J. Greenhouse Gas Control 52 (Sep): 24–31. https://doi.org/10.1016/j.ijggc.2016.06.013.
Zhao, Y., S. Liu, D. Elsworth, Y. Jiang, and J. Zhu. 2014. “Pore structure characterization of coal by synchrotron small-angle X-ray scattering and transmission electron microscopy.” Energy Fuels 28 (6): 3704–3711. https://doi.org/10.1021/ef500487d.
Zhao, Y., Y. Sun, L. Yuan, and Q. Xu. 2020. “Impact of nanopore structure on coal strength: A study based on synchrotron radiation nano-CT.” Results Phys. 17 (Jun): 103029. https://doi.org/10.1016/j.rinp.2020.103029.
Zhou, J., X. Li, and H. S. Mitri. 2016. “Classification of rockburst in underground projects: Comparison of ten supervised learning methods.” J. Comput. Civ. Eng. 30 (5): 04016003. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000553.
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Journal of Computing in Civil Engineering
Volume 36 • Issue 2 • March 2022
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Received: Apr 10, 2020
Accepted: Apr 20, 2021
Published online: Dec 21, 2021
Published in print: Mar 1, 2022
Discussion open until: May 21, 2022
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