High-order streamline simulation and macro-scale visualization experimental studies on waterflooding under given pressure boundaries

Zhang, Nan; Cao, Jie; James, Lesley; Johansen, T. H.

doi:10.1016/j.petrol.2021.108617

Cited by 11 publications

(2 citation statements)

References 25 publications

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“…The streamline simulation is one of the most feasible methods for dominant flow characterization. In recent years, streamline simulation has attracted more and more attention (Mesbah et al, 2019;Wang et al, 2020;Morse and Mahesh, 2021;Namdari et al, 2021;Zhang et al, 2021). However, discrete streamline distribution cannot accurately represent the actual flow field, limiting its effective application to the oil field.…”

Section: Introductionmentioning

confidence: 99%

Flow Field Description and Simplification Based on Principal Component Analysis Downscaling and Clustering Algorithms

et al. 2022

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The flow field obtained from streamline simulation reflects key properties of the reservoir, such as the distribution of the remaining oil and the location of channels. However, in the three-dimensional streamline field, the advantages of streamline simulation are limited. Because numerous streamlines interfere with each other and distribute in a sophisticated way, it is really difficult to infer the connectivity between wells and the flow characteristics of the reservoir. To make a more effective and visualizable description of the flow field, the three-dimensional streamline field has to be simplified. In this paper, principal component analysis (PCA) is applied to parameterize the streamline attributes and reduce the dimensionality of the flow field. After dimension reduction, the principal components of the streamline field can be analyzed by the clustering method. In the clustering procedure, the mainstream lines are selected according to the clustering center, thereby intuitively illustrating the properties of the reservoir. Through experimental verification, the proposed method can characterize the streamlines of the flow field more efficiently and reflect the inter-well connectivity more clearly than the commercial numerical simulator.

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Section: Introductionmentioning

confidence: 99%

Flow Field Description and Simplification Based on Principal Component Analysis Downscaling and Clustering Algorithms

et al. 2022

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“…Generation of streamlines is often time-consuming and even difficult for complex geometries and unstructured grids (Matringe et al, 2008;Haegland, 2009;Rasmussen, 2010;Klausen et al, 2012;da Cunha Teixeira et al, 2021), while the mapping of saturation field between streamlines and grid cells causes extra inaccu-racies. Given these advantages and disadvantages, streamline methods have been applied in many practical problems (Thiele and Batycky, 2003;Batycky et al, 2005;Zhang et al, 2021b).…”

Section: Introductionmentioning

confidence: 99%

A Multi-Stencil Fast Marching Method with Path Correction for Efficient Reservoir Simulation and Automated History Matching

Zhang¹,

Guo²,

Zhang³

et al. 2021

Preprint

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The efficiency of reservoir simulation is important for automated history matching (AHM) and production optimization, etc. The fast marching marching method (FMM) has been used for efficient reservoir simulation. FMM can be regarded as a generalised streamline method but without the need to construct streamlines. In FMM reservoir simulation, the Eikonal equation for the diffusive time-of-flight (DTOF) is solved by FMM and then the governing equations are computed on the 1D DTOF coordinate. Standard FMM solves the Eikonal equation using a 4-stencil algorithm on 2D Cartesian grids, ignoring the diagonal neighbouring cells. In the current study, we build a 8-stencil algorithm considering all neighbouring cells, and use local analytical propagation speeds. In addition, a local path-correction coefficient is introduced to further increase the accuracy of DTOF solution. Next, a discretisation scheme is built on the 1D DTOF coordinate for efficient reservoir simulation. The algorithm is validated on homogeneous and heterogeneous test cases,

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Waterflood Efficiency Assessment Using Injection–Production Relationship Analysis Method

Hao

Zhong²,

Liu³

et al. 2022

Math Geosci

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High-order streamline simulation and macro-scale visualization experimental studies on waterflooding under given pressure boundaries

Cited by 11 publications

References 25 publications

Flow Field Description and Simplification Based on Principal Component Analysis Downscaling and Clustering Algorithms

Flow Field Description and Simplification Based on Principal Component Analysis Downscaling and Clustering Algorithms

A Multi-Stencil Fast Marching Method with Path Correction for Efficient Reservoir Simulation and Automated History Matching

Waterflood Efficiency Assessment Using Injection–Production Relationship Analysis Method

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