Tahmasebi Research Group G4 [Geo-Systems, Geo-Materials, Geo-Informatics, Geo-Fluids]

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JOURNAL (Only the peer-reviewed publications)

 

 

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Sahimi*, M., Tahmasebi, P.: Reconstruction, Optimization, and Design of Heterogeneous Materials and Media: Basic Principles, Computational Algorithms, and Applications. Physics Reports (2021) https://doi.org/10.1016/j.physrep.2021.09.003 [PDF]

Bai, T., Tahmasebi*, P.: Attention-based LSTM-FCN for Earthquake Detection and Location. Geophysical Journal International (2021). https://doi.org/10.1093/gji/ggab401

Kamrava, S., Sahimi*, M., Tahmasebi, P.: Simulating fluid flow in complex porous materials by integrating the governing equations with deep-layered machines. Nat. Comput. Mater. 7, 1–9 (2021). https://doi.org/10.1038/s41524-021-00598-2 [PDF]

Karimpouli, S., Tahmasebi, P., Saenger*, E.H.: Ultrasonic prediction of crack density using machine learning: A numerical investigation. Geosci. Front. 101277 (2021). https://doi.org/10.1016/j.gsf.2021.101277 [PDF]

Bai, T., Tahmasebi*, P.: Sequential Gaussian simulation for geosystems modeling: A machine learning approach. Geosci. Front. 13, 101258 (2021). https://doi.org/10.1016/j.gsf.2021.101258 [PDF]

Kamrava, S., Tahmasebi, P., Sahimi*, M.: Physics- and image-based prediction of fluid flow and transport in complex porous membranes and materials by deep learning. J. Memb. Sci. 622, 119050 (2021). https://doi.org/10.1016/j.memsci.2021.119050 [PDF]

Jiang, Z., Tahmasebi*, P., Mao, Z.: Deep residual U-net convolution neural networks with autoregressive strategy for fluid flow predictions in large-scale geosystems. Adv. Water Resour. 150, 103878 (2021). https://doi.org/10.1016/j.advwatres.2021.103878 [PDF]

Saenger*, E.H., Finger, C., Karimpouli, S., Tahmasebi, P.: Single-Station Coda Wave Interferometry: A Feasibility Study Using Machine Learning. Materials (Basel). 14, 3451 (2021). https://doi.org/10.3390/ma14133451 [PDF]

Bai, T., Jiang, Z., Tahmasebi*, P.: Debris flow prediction with machine learning: smart management of urban systems and infrastructures. Neural Comput. Appl. 1–11 (2021). https://doi.org/0.1007/s00521-021-06197-y [PDF]

Tahmasebi, P., Javadpour*, F., Enayati, S.F.: Digital rock techniques to study shale permeability: A mini-review. Energy and Fuels. 34, 15672–15685 (2020). https://doi.org/10.1021/acs.energyfuels.0c03397 [PDF]

Bai, T., Tahmasebi*, P.: Accelerating geostatistical modeling using geostatistics-informed machine Learning. Comput. Geosci. 146, 104663 (2021). https://doi.org/10.1016/j.cageo.2020.104663 [PDF]

Wu, Y., Tahmasebi*, P., Lin, C., Dong, C.: Using Digital Rock Physics to Investigate the Impacts of Diagenesis Events and Pathways on Rock Properties. J. Pet. Sci. Eng. 108025 (2020). https://doi.org/10.1016/j.petrol.2020.108025 [PDF]

Bai, T., Tahmasebi*, P.: Efficient and data-driven prediction of water breakthrough in subsurface systems using deep long short-term memory machine learning. Comput. Geosci. (2020). https://doi.org/10.1007/s10596-020-10005-2 [PDF]

Davydzenka, T., Fagbemi, S., Tahmasebi*, P.: Coupled fine-scale modeling of the wettability effects: Deformation and fracturing. Phys. Fluids. 32, 083308 (2020). https://doi.org/10.1063/5.0018455 [PDF]

Karimpouli, S., Tahmasebi*, P.: Physics informed machine learning: Seismic wave equation. Geosci. Front. (2020). https://doi.org/10.1016/j.gsf.2020.07.007 [PDF]

Fagbemi, S., Tahmasebi*, P., Piri, M.: Elastocapillarity modeling of multiphase flow-induced solid deformation using volume of fluid method. J. Comput. Phys. 421, 109641 (2020). https://doi.org/10.1016/j.jcp.2020.109641 [PDF]

Karimpouli, S., Tahmasebi*, P., Ramandi, H.L.: A review of experimental and numerical modeling of digital coalbed methane: Imaging, segmentation, fracture modeling and permeability prediction, (2020). https://doi.org/10.1016/j.coal.2020.103552 [PDF]

Kamrava, S., Tahmasebi, P., Sahimi*, M., Arbabi, S.: Phase transitions, percolation, fracture of materials, and deep learning. Phys. Rev. E. 102, 011001 (2020). https://doi.org/10.1103/PhysRevE.102.011001 [PDF]

Davydzenka, T., Fagbemi, S., Tahmasebi*, P.: Wettability control on deformation: Coupled multiphase fluid and granular systems. Phys. Rev. E. 102, 013301 (2020). https://doi.org/10.1103/PhysRevE.102.013301 [PDF]

Wu, Y., Tahmasebi*, P., Lin, C., Zahid, M.A.M.A., Dong, C., Golab, A.N.A.N., Ren, L.: 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, 12–28 (2019). https://doi.org/10.1016/j.marpetgeo.2019.02.003 [PDF]

Fagbemi, S., Tahmasebi*, P.: Coupling pore network and finite element methods for rapid modelling of deformation. J. Fluid Mech. 897, A20 (2020). https://doi.org/10.1017/jfm.2020.381 [PDF]

Tahmasebi*, P., Kamrava, S., Bai, T., Sahimi, M.: Machine learning in geo- and environmental sciences: From small to large scale. Adv. Water Resour. 142, 103619 (2020). https://doi.org/10.1016/j.advwatres.2020.103619 [PDF]

Bai, T., Tahmasebi*, P.: Hybrid geological modeling: Combining machine learning and multiple-point statistics. Comput. Geosci. 104519 (2020). https://doi.org/10.1016/j.cageo.2020.104519 [PDF]

Tahmasebi, P., Shokri-Kuehni, S.M.S., Sahimi, M., Shokri*, N.: How do environmental, economic and health factors influence regional vulnerability to COVID-19? MedRxiv. (2020). https://doi.org/10.1101/2020.04.09.20059659 [PDF]

Kamrava, S., Sahimi*, M., Tahmasebi, P.: Quantifying accuracy of stochastic methods of reconstructing complex materials by deep learning. Phys. Rev. E. 101, 043301 (2020). https://doi.org/10.1103/PhysRevE.101.043301 [PDF]

Wu, Y., Tahmasebi*, P., Lin, C., Ren, L., Zhang, Y.: Quantitative characterization of non-wetting phase in water-wet porous media based on multiphase flow experiment and numerical simulation. J. Pet. Sci. Eng. 188, (2020). https://doi.org/10.1016/j.petrol.2020.106914 [PDF]

Wu, Y., Tahmasebi*, P., Lin, C., Dong, C.: Process-based and dynamic 2D modeling of shale samples: Considering the geology and pore-system evolution. Int. J. Coal Geol. 218, (2020). https://doi.org/10.1016/j.coal.2019.103368 [PDF]

Wu, Y., Tahmasebi*, P., Yu, H., Lin, C., Wu, H., Dong, C.: Pore-Scale 3D Dynamic Modeling and Characterization of Shale Samples: Considering the Effects of Thermal Maturation. J. Geophys. Res. Solid Earth. 125, (2020). https://doi.org/10.1029/2019JB018309 [PDF]

Fagbemi, S., Tahmasebi*, P., Piri, M.: Numerical modeling of strongly coupled microscale multiphase flow and solid deformation. Int. J. Numer. Anal. Methods Geomech. 44, (2020). https://doi.org/10.1002/nag.2999 [PDF]

Kamrava, S., Tahmasebi, P., Sahimi*, M.: Linking Morphology of Porous Media to Their Macroscopic Permeability by Deep Learning. Transp. Porous Media. (2019). https://doi.org/10.1007/s11242-019-01352-5 [PDF]

Tahmasebi*, P.: An optimization-based approach for modeling of complex particles. Powder Technol. 356, (2019). https://doi.org/10.1016/j.powtec.2019.08.027 [PDF]

Karimpouli*, S., Tahmasebi, P., Saenger, E.H.: Coal Cleat/Fracture Segmentation Using Convolutional Neural Networks. Nat. Resour. Res. (2019). https://doi.org/10.1007/s11053-019-09536-y [PDF]

Kamrava, S., Tahmasebi, P., Sahimi*, M.: Enhancing images of shale formations by a hybrid stochastic and deep learning algorithm. Neural Networks. 118, 310–320 (2019). https://doi.org/10.1016/J.NEUNET.2019.07.009 [PDF]

Wu, Y., Tahmasebi*, P., Lin, C., Ren, L., Dong, C.: Multiscale modeling of shale samples based on low- and high-resolution images. Mar. Pet. Geol. 109, (2019). https://doi.org/10.1016/j.marpetgeo.2019.06.006 [PDF]

Zhang, X., Tahmasebi*, P.: Effects of Grain Size on Deformation in Porous Media. Transp. Porous Media. 129, (2019). https://doi.org/10.1007/s11242-019-01291-1 [PDF]

Wu, Y., Tahmasebi*, P., Lin, C., Munawar, M.J., Cnudde, V.: Effects of micropores on geometric, topological and transport properties of pore systems for low-permeability porous media. J. Hydrol. 575, (2019). https://doi.org/10.1016/j.jhydrol.2019.05.014 [PDF]

Tahmasebi*, P., Kamrava, S.: A pore-scale mathematical modeling of fluid-particle interactions: Thermo-hydro-mechanical coupling. Int. J. Greenh. Gas Control. 83, (2019). https://doi.org/10.1016/j.ijggc.2018.12.014 [PDF]

Mortazavi, M., Tahmasebi*, P., Hezarkhani, A.: Element Mobility in Alteration Zones. Aust. J. Basic Appl. Sci. 4, 197–207 (2010)

Karimpouli, S., Tahmasebi*, P.: Segmentation of digital rock images using deep convolutional autoencoder networks. Comput. Geosci. 126, 142–150 (2019). https://doi.org/10.1016/J.CAGEO.2019.02.003 [PDF]

Karimpouli, S., Tahmasebi*, P.: Image-based velocity estimation of rock using Convolutional Neural Networks. Neural Networks. 111, 89–97 (2019). https://doi.org/10.1016/J.NEUNET.2018.12.006 [PDF]

Fagbemi, S., Tahmasebi*, P., Piri, M.: Interaction Between Fluid and Porous Media with Complex Geometries: A Direct Pore-Scale Study. Water Resour. Res. 54, (2018). https://doi.org/10.1029/2017WR022242 [PDF]

Tahmasebi*, P., Kamrava, S.: Rapid multiscale modeling of flow in porous media. Phys. Rev. E. 98, 052901 (2018). https://doi.org/10.1103/PhysRevE.98.052901 [PDF]

Fagbemi, S., Tahmasebi*, P., Piri, M.: Pore-scale modeling of multiphase flow through porous media under triaxial stress. Adv. Water Resour. 122, 206–216 (2018). https://doi.org/10.1016/J.ADVWATRES.2018.10.018 [PDF]

Tahmasebi*, P., Sahimi, M.: Editorial to the Special Issue on Reconstruction of Porous Media and Materials and Its Applications. Transp. Porous Media. 125, 1–3 (2018). https://doi.org/10.1007/s11242-018-1131-1 [PDF]

Karimpouli, S., Tahmasebi*, P.: 3D Multifractal Analysis of Porous Media Using 3D Digital Images: Considerations for heterogeneity evaluation. Geophys. Prospect. 67, 1082–1093 (2019). https://doi.org/10.1111/1365-2478.12681 [PDF]

Zhang, X., Tahmasebi*, P.: Micromechanical evaluation of rock and fluid interactions. Int. J. Greenh. Gas Control. 76, 266–277 (2018). https://doi.org/10.1016/J.IJGGC.2018.07.018 [PDF]

Tahmasebi*, P., Sahimi, M.: A Stochastic Multiscale Algorithm for Modeling Complex Granular Materials. Granul. Matter. 20, (2018). https://doi.org/10.1007/s10035-018-0816-z [PDF]

Tahmasebi*, P.: Packing of discrete and irregular particles. Comput. Geotech. 100, 52–61 (2018). https://doi.org/10.1016/J.COMPGEO.2018.03.011 [PDF]

Tahmasebi*, P., Kamrava, S.: A Multiscale Approach for Geologically and Flow Consistent Modeling. Transp. Porous Media. 124, 237–261 (2018). https://doi.org/10.1007/s11242-018-1062-x [PDF]

Karimpouli*, S., Tahmasebi, P., Saenger, E.H.: Estimating 3D elastic moduli of rock from 2D thin-section images using differential effective medium theory. GEOPHYSICS. 83, MR211–MR219 (2018). https://doi.org/10.1190/geo2017-0504.1 [PDF]

Tahmasebi*, P.: Accurate modeling and evaluation of microstructures in complex materials. Phys. Rev. E. 97, 023307 (2018). https://doi.org/10.1103/PhysRevE.97.023307 [PDF]

Tahmasebi*, P., Javadpour, F., Frébourg, G.: Geologic Modeling of Eagle Ford Facies Continuity Based on Outcrop Images and Depositional Processes. SPE J. (2018). https://doi.org/10.2118/189975-PA [PDF]

Tahmasebi*, P., Sahimi, M., Shirangi, M.G.: Rapid Learning-Based and Geologically Consistent History Matching. Transp. Porous Media. (2018). https://doi.org/10.1007/s11242-018-1005-6 [PDF]

Tahmasebi*, P.: Nanoscale and multiresolution models for shale samples. Fuel. 217, 218–225 (2018). https://doi.org/10.1016/j.fuel.2017.12.107 [PDF]

Tahmasebi*, P.: HYPPS: A hybrid geostatistical modeling algorithm for subsurface modeling. Water Resour. Res. 53, 5980–5997 (2017). https://doi.org/10.1002/2017WR021078 [PDF]

Tahmasebi*, P., Javadpour, F., Sahimi, M.: Data mining and machine learning for identifying sweet spots in shale reservoirs. Expert Syst. Appl. 88, 435–447 (2017). https://doi.org/10.1016/j.eswa.2017.07.015 [PDF]

Karimpouli, S., Tahmasebi*, P., Ramandi, H., Mostaghimi, P., Saadatfar, M.: Stochastic modeling of coal fracture network by direct use of micro-computed tomography images. Int. J. Coal Geol. 179, 153–163 (2017). https://doi.org/10.1016/j.coal.2017.06.002 [PDF]

Tahmasebi*, P., Sahimi, M., Andrade, J.E.: Image-based modeling of granular porous media. Geophys. Res. Lett. 44, (2017). https://doi.org/10.1002/2017GL073938 [PDF]

Tahmasebi*, P.: Structural adjustment for accurate conditioning in large-scale subsurface systems. Adv. Water Resour. 101, (2017). https://doi.org/10.1016/j.advwatres.2017.01.009 [PDF]

Karimpouli, S., Tahmasebi*, P.: A Hierarchical Sampling for Capturing Permeability Trend in Rock Physics. Transp. Porous Media. 116, 1057–1072 (2017). https://doi.org/10.1007/s11242-016-0812-x [PDF]

Tahmasebi, P., Sahimi*, M., Kohanpur, A.H., Valocchi, A.: Pore-scale simulation of flow of CO2 and brine in reconstructed and actual 3D rock cores. J. Pet. Sci. Eng. 155, 21–33 (2017). https://doi.org/10.1016/j.petrol.2016.12.031 [PDF]

Tahmasebi*, P., Javadpour, F., Sahimi, M.: Stochastic shale permeability matching: Three-dimensional characterization and modeling. Int. J. Coal Geol. 165, 231–242 (2016). https://doi.org/10.1016/j.coal.2016.08.024 [PDF]

Karimpouli, S., Tahmasebi*, P.: Conditional reconstruction: An alternative strategy in digital rock physics. Geophysics. 81, D465–D477 (2016). https://doi.org/10.1190/geo2015-0260.1 [PDF]

Tahmasebi*, P., Javadpour, F., Sahimi, M., Piri, M.: Multiscale study for stochastic characterization of shale samples. Adv. Water Resour. 89, 91–103 (2016). https://doi.org/10.1016/j.advwatres.2016.01.008 [PDF]

Tahmasebi*, P., Sahimi, M.: Enhancing multiple-point geostatistical modeling: 1. Graph theory and pattern adjustment. Water Resour. Res. 52, 2074–2098 (2016). https://doi.org/10.1002/2015WR017806 [PDF]

Tahmasebi*, P., Sahimi, M.: Enhancing multiple-point geostatistical modeling: 2. Iterative simulation and multiple distance function. Water Resour. Res. 52, 2099–2122 (2016). https://doi.org/10.1002/2015WR017807 [PDF]

Tahmasebi*, P., Javadpour, F., Sahimi, M.: Multiscale and multiresolution modeling of shales and their flow and morphological properties. Sci. Rep. 5, (2015). https://doi.org/10.1038/srep16373 [PDF]

Tahmasebi*, P., Javadpour, F., Sahimi, M.: Three-Dimensional Stochastic Characterization of Shale SEM Images. Transp. Porous Media. 110, 521–531 (2015). https://doi.org/10.1007/s11242-015-0570-1 [PDF]

Scheidt, C., Tahmasebi, P., Pontiggia, M., Da Pra, A., Caers*, J.: Updating joint uncertainty in trend and depositional scenario for reservoir exploration and early appraisal. Comput. Geosci. 19, 805–820 (2015). https://doi.org/10.1007/s10596-015-9491-x [PDF]

Tan, X., Tahmasebi, P., Caers*, J.: Comparing training-image based algorithms using an analysis of distance. Math. Geosci. 46, (2014). https://doi.org/10.1007/s11004-013-9482-1 [PDF]

Tahmasebi*, P., Sahimi, M.: Reconstruction of nonstationary disordered materials and media: Watershed transform and cross-correlation function. Phys. Rev. E - Stat. Physics, Plasmas, Fluids, Relat. Interdiscip. Top. 91, 032401 (2015). https://doi.org/10.1103/PhysRevE.91.032401 [PDF]

Tahmasebi*, P., Sahimi, M.: Geostatistical Simulation and Reconstruction of Porous Media by a Cross-Correlation Function and Integration of Hard and Soft Data. Transp. Porous Media. 107, 871–905 (2015). https://doi.org/10.1007/s11242-015-0471-3 [PDF]

Rezaee, H., Marcotte*, D., Tahmasebi, P., Saucier, A.: Multiple-point geostatistical simulation using enriched pattern databases. Stoch. Environ. Res. Risk Assess. 29, 893–913 (2014). https://doi.org/10.1007/s00477-014-0964-6 [PDF]

Hashemi*, S., Javaherian, A., Ataee-pour, M., Tahmasebi, P., Khoshdel, H.: Channel characterization using multiple-point geostatistics, neural network, and modern analogy: A case study from a carbonate reservoir, southwest Iran. J. Appl. Geophys. 111, 47–58 (2014). https://doi.org/10.1016/j.jappgeo.2014.09.015 [PDF]

Mahmud*, K., Mariethoz, G., Caers, J., Tahmasebi, P., Baker, A.: Simulation of Earth textures by conditional image quilting. Water Resour. Res. 50, 3088–3107 (2014). https://doi.org/10.1002/2013WR015069 [PDF]

Tahmasebi, P., Sahimi, M., Caers*, J.: MS-CCSIM: Accelerating pattern-based geostatistical simulation of categorical variables using a multi-scale search in Fourier space. Comput. Geosci. 67, 75–88 (2014). https://doi.org/10.1016/j.cageo.2014.03.009 [PDF]

Tahmasebi, P., Sahimi*, M.: Cross-correlation function for accurate reconstruction of heterogeneous media. Phys. Rev. Lett. 110, 078002 (2013). https://doi.org/10.1103/PhysRevLett.110.078002 [PDF]

Tahmasebi, P., Sahimi*, M.: Reconstruction of three-dimensional porous media using a single thin section. Phys. Rev. E - Stat. Nonlinear, Soft Matter Phys. 85, 1–13 (2012). https://doi.org/10.1103/PhysRevE.85.066709 [PDF]

Tahmasebi, P., Sahimi*, M., Mariethoz, G., Hezarkhani, A.: Accelerating geostatistical simulations using graphics processing units (GPU). Comput. Geosci. 46, 51–59 (2012). https://doi.org/10.1016/j.cageo.2012.03.028 [PDF]

Tahmasebi*, P., Hezarkhani, A.: A fast and independent architecture of artificial neural network for permeability prediction. J. Pet. Sci. Eng. 86–87, 118–126 (2012). https://doi.org/10.1016/j.petrol.2012.03.019 [PDF]

Mafakheri, E., Tahmasebi, P., Ghanbari*, D.: Application of artificial neural networks for prediction of coercivity of highly ordered cobalt nanowires synthesized by pulse electrodeposition. Meas. J. Int. Meas. Confed. 45, 1387–1395 (2012). https://doi.org/10.1016/j.measurement.2012.03.027 [PDF]

Tahmasebi*, P., Hezarkhani, A., Mortazavi, M.: Application of discriminant analysis for alteration separation; Sungun copper deposit, East Azerbaijan, Iran. Aust. J. Basic Appl. Sci. 4, (2010)

Tahmasebi*, P., Hezarkhani, A.: A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation. Comput. Geosci. 42, 18–27 (2012). https://doi.org/10.1016/j.cageo.2012.02.004 [PDF]

Tahmasebi*, P., Hezarkhani, A.: Application of a Modular Feedforward Neural Network for Grade Estimation. Nat. Resour. Res. 20, 25–32 (2011). https://doi.org/10.1007/s11053-011-9135-3 [PDF]

Tahmasebi*, P., Hezarkhani, A.: Comparison of optimized neural network with fuzzy logic for grade estimation. Aust. J. Basic Appl. Sci. 4, (2010)

Tahmasebi*, P., Hezarkhani, A., Sahimi, M.: Multiple-point geostatistical modeling based on the cross-correlation functions. Comput. Geosci. 16, 779–797 (2012). https://doi.org/10.1007/s10596-012-9287-1 [PDF]

Asadisaghandi, J., Tahmasebi*, P.: Comparative evaluation of back-propagation neural network learning algorithms and empirical correlations for prediction of oil PVT properties in Iran oilfields. J. Pet. Sci. Eng. 78, 464–475 (2011). https://doi.org/10.1016/J.PETROL.2011.06.024 [PDF]

Tahmasebi*, P., Hezarkhani, A.: Application of adaptive neuro-fuzzy inference system for grade estimation. Aust. J. Basic Appl. Sci. 4, 408–420 (2010)



BOOKS AND BOOK CHAPTERS

Tahmasebi, P., 2018, Multiple point statistics, Handbook of Mathematical Geosciences: Fifty Years of IAMG, Edited by B. S. Daya Sagar, Qiuming Cheng and Frits Agterberg, Springer, Netherlands.(Download PDF)

Tahmasebi, P., Sahimi, M., 2015, Geostatistical simulation and reconstruction of porous media, Handbook of porous media, 3rd edition, Edited by Kambiz Vafai, CRC Press, Baton Rouge, pp. 869-890.

Tahmasebi, P., Mariethoz, G., 2013, Multiple point geostatistics, Taylor & Francis Geostatistics Applications in Hydrology. In: Handbook of Engineering Hydrology: Modeling, Climate Change, and Variability, Taylor & Francis, ISBN 9781466552463.

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