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  • Open Access


    Prediction of Porous Media Fluid Flow with Spatial Heterogeneity Using Criss-Cross Physics-Informed Convolutional Neural Networks

    Jiangxia Han1,2, Liang Xue1,2,*, Ying Jia3, Mpoki Sam Mwasamwasa1,2, Felix Nanguka4, Charles Sangweni5, Hailong Liu3, Qian Li3

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1323-1340, 2024, DOI:10.32604/cmes.2023.031093

    Abstract Recent advances in deep neural networks have shed new light on physics, engineering, and scientific computing. Reconciling the data-centered viewpoint with physical simulation is one of the research hotspots. The physics-informed neural network (PINN) is currently the most general framework, which is more popular due to the convenience of constructing NNs and excellent generalization ability. The automatic differentiation (AD)-based PINN model is suitable for the homogeneous scientific problem; however, it is unclear how AD can enforce flux continuity across boundaries between cells of different properties where spatial heterogeneity is represented by grid cells with different… More >

  • Open Access


    Prediction of Low-Permeability Reservoirs Performances Using Long and Short-Term Memory Machine Learning

    Guowei Zhu*, Kangliang Guo, Haoran Yang, Xinchen Gao, Shuangshuang Zhang

    FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.5, pp. 1521-1528, 2022, DOI:10.32604/fdmp.2022.020942

    Abstract In order to overcome the typical limitations of numerical simulation methods used to estimate the production of low-permeability reservoirs, in this study, a new data-driven approach is proposed for the case of water-driven hypo-permeable reservoirs. In particular, given the bottlenecks of traditional recurrent neural networks in handling time series data, a neural network with long and short-term memory is used for such a purpose. This method can reduce the time required to solve a large number of partial differential equations. As such, it can therefore significantly improve the efficiency in predicting the needed production performances. More >

  • Open Access


    A POD Coupled Adaptive DEIM (POD-ADEIM) Reduced-Order Model for Incompressible Multiphase Flow in Porous Media

    Jingfa Li1,2, Shuyu Sun2,*, Bo Yu1, Yang Liu2, Tao Zhang2

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.22, No.3, pp. 141-142, 2019, DOI:10.32604/icces.2019.04721

    Abstract The multiphase fluid flow in porous media is one of the most fundamental phenomena in various physical processes, such as oil/gas flow in reservoir, subsurface contamination dispersion, chemical separation, etc. Due to its importance, the efficient and accurate solution and prediction of multiphase flow in porous media is highly required in engineering applications and mechanism studies, which has been a research hot spot with increasing interest in recent years. However, the strong nonlinearity implicated in the multiphase flow model has brought great challenges for the computation and analysis. In addition, the permeability in Darcy-type pressure… More >

  • Open Access


    An Experimental Study of Two-Phase Flow in Porous Media with Measurement of Relative Permeability

    N. Labed1, L. Bennamoun2, J.P. Fohr3

    FDMP-Fluid Dynamics & Materials Processing, Vol.8, No.4, pp. 423-436, 2012, DOI:10.3970/fdmp.2012.008.423

    Abstract Intrinsic and relative permeability are indispensable parameters for performing transfers in porous media. In this paper, the conception and ensuing exploitation of a new testing ground for measuring the relative permeability of water and nitrogen are presented. The experimental work was elaborated in the Laboratory of Thermal Studies in Poitiers, (France) where brick samples were used to verify the performance of the proposed testing strategy. The results prove the existence of several stages during the drainage and the imbibitions. In particular, the three stages observed for the case of gas permeability reduce to only two More >

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