Jiangxia Han^1,2, Liang Xue^1,2,*, Ying Jia³, Mpoki Sam Mwasamwasa^1,2, 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 physical properties. In this work,… More >

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. Practical examples about water-driven hypotonic… More >

Jingfa Li^1,2, Shuyu Sun^2,*, 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 equation is always represented as… More >

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 steps for liquid permeability. Comparison… More >