2019
DOI: 10.1108/hff-02-2019-0129
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Numerical investigation of the POD reduced-order model for fast predictions of two-phase flows in porous media

Abstract: Purpose This paper aims to present an efficient IMPES algorithm based on a global model order reduction method, proper orthogonal decomposition (POD), to achieve the fast solution and prediction of two-phase flows in porous media. Design/methodology/approach The key point of the proposed algorithm is to establish an accurate POD reduced-order model (ROM) for two-phase porous flows. To this end, two projection methods including projecting the original governing equations (Method I) and projecting the discrete… Show more

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Cited by 14 publications
(8 citation statements)
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“…Numerical simulation is an effective approach to study mechanisms and phenomena especially when experimental studies are hard to perform [94][95][96][97][98][99]. Modeling the mechanisms behind low salinity waterflooding is the first step to mathematically understand and describe the observed phenomenon, as well as the key point in developing advanced enhanced oil recovery techniques.…”
Section: Numerical Simulation On Pipeline Scaling With Different Injementioning
confidence: 99%
“…Numerical simulation is an effective approach to study mechanisms and phenomena especially when experimental studies are hard to perform [94][95][96][97][98][99]. Modeling the mechanisms behind low salinity waterflooding is the first step to mathematically understand and describe the observed phenomenon, as well as the key point in developing advanced enhanced oil recovery techniques.…”
Section: Numerical Simulation On Pipeline Scaling With Different Injementioning
confidence: 99%
“…According to the specific form of the original governing equations or discrete matrix equations, there are several ways to construct the corresponding POD reduced‐order model (POD‐ROM) . All of these procedures must establish accurate mathematical expressions of the POD‐ROM to obtain the spectral coefficients or POD time coefficients, which are needed for the reconstructions of approximation solutions.…”
Section: Galerkin‐free Pod Reduced‐order Modelmentioning
confidence: 99%
“…The proper orthogonal decomposition reduced‐order model (POD‐ROM) can reduce the number of degrees of freedom for original high‐dimensional physical problems by combining the POD and the Galerkin projection method . It has been applied in many fields, such as turbulence flow, heat conduction and convective heat transfer, two‐phase flow, and gas flow . Recently, the POD method has been extended to simulate flow in porous media .…”
Section: Introductionmentioning
confidence: 99%
“…In order to avoid the instability issues, the model is solved implicitly in time, i.e. the transient term is discretized using the backward Euler scheme [46][47][48]. For spatial term, the central difference and upwind schemes are used to discretize the diffusion and convection terms, respectively.…”
Section: Dimensionless Modelmentioning
confidence: 99%