On scaling immiscible displacements in permeable media

Peters, Ekwere J.; Afzal, Nadeem; Gharbi, Ridha

doi:10.1016/0920-4105(93)90014-6

Cited by 25 publications

(10 citation statements)

References 31 publications

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“…Applying Buckingham's π theorem on a set of selected variables results in generating the number and the forms of the dimensionless groups. Many studies have focused on applications of the scaling theory and similarity laws for flow in porous systems [61,[65][66][67][68][69][70][71][72][73][74][75][76]. Initially, Leverett et al [77] used the dimensionless groups in a research work on immiscible displacement of oil by water, which was later extended by Engleberts and Klinkenberg [78].…”

Section: Dimensional Analysismentioning

confidence: 99%

Application of Gene Expression Programming (GEP) in Modeling Hydrocarbon Recovery in WAG Injection Process

2021

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Water alternating gas (WAG) injection has been successfully applied as a tertiary recovery technique. Forecasting WAG flooding performance using fast and robust models is of great importance to attain a better understanding of the process, optimize the operational conditions, and avoid high-cost blind tests in laboratory or pilot scales. In this study, we introduce a novel correlation to determine the performance of the near-miscible WAG flooding in strongly water-wet sandstones. We conduct dimensional analysis with Buckingham’s π theorem technique to generate dimensionless numbers using eight key parameters. Seven dimensionless numbers are employed as the input variables of the desired correlation for predicting the recovery factor of a near-miscible WAG injection. A verified mathematical model is used to generate the required training and testing data for the development of the correlation using a gene expression programming (GEP) algorithm. The provided data points are then separated into two subsets: training (67%) to develop the model and testing (33%) to assess the models’ capability. Conducting error analysis, statistical measures and graphical illustrations are provided to assess the effectiveness of the introduced model. The statistical analysis shows that the developed GEP-based correlation can generate target data with high precision such that the training phase leads to R2 = 92.85% and MSE = 1.38 × 10−3 and R2 = 91.93% and MSE = 4.30 × 10−3 are attained for the testing phase. The relative importance of the input dimensionless groups is also determined. According to the sensitivity analysis, decreasing the oil–water capillary number results in a significant reduction in RF in all cycles. Increasing the magnitudes of oil to gas viscosity ratio and oil to water viscosity ratio lowers the RF of each cycle. It is found that oil to gas viscosity ratio has a higher impact on RF value compared to oil to water viscosity ratio due to a higher viscosity gap between the gas and oil phases. It is expected that the GEP, as a fast and reliable tool, will be useful to find vital variables including relative permeability in complex transport phenomena such as three-phase flow in porous media.

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Section: Dimensional Analysismentioning

confidence: 99%

Application of Gene Expression Programming (GEP) in Modeling Hydrocarbon Recovery in WAG Injection Process

2021

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“…7. [6][7][8][9][10][11][12][13][14][15][16][17][18][19] The eight dimensionless scaling groups describing the displacement are:…”

Section: Dimensionless Scaling Groupmentioning

confidence: 99%

“…[6][7][8][9][10][11][12][13][14][15][16][17][18][19] Despite all these studies (only few cited here), none of them have investigated scaling of miscible displacements in porous media with a wide range of heterogeneity variations. Reservoir heterogeneity has long been recognized as an important parameter in determining reservoir performance.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Prediction Model of Miscible Displacements in Heterogeneous Reservoirs

Gharbi

2003

Proceedings of Middle East Oil Show

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Understanding and predicting the performance of solvent drives and remediation of contaminated aquifers in heterogeneous reservoirs is of great importance to the petroleum and environmental industries. In this paper, a general method to scale flow through heterogeneous reservoirs is presented for a miscible displacement of oil by a solvent. Results show that scaling miscible displacements in a two-dimensional, heterogeneous, anisotropic vertical cross section requires the matching of thirteen dimensionless scaling groups. These groups were derived using a general procedure of inspectional analysis. A detailed numerical sensitivity study was performed to reveal the relationship between the scaling groups and the fractional oil recovery of miscible displacements in heterogeneous reservoirs. This relationship was then mapped using an artificial neural network, which can be used as a quick prediction tool for the fractional oil recovery for any combinations of the scaling groups, thus eliminating the need for the expensive fine-mesh simulations. These results have potential applications in modeling miscible displacements and in the scaling of laboratory displacements to field conditions.

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“…The subject of scaling and similarity laws applicable to multiphase flow in porous medium has been extensively studied in the past (Rapoport and Leas, 1953;Nielsen and Tek, 1963;Wygal, 1963;Greenkorn, 1964;Loomis and Crowell, 1964;Bear, 1972;Collins, 1976;Shook et al, 1992;Waggoner et al 1992;Peters et al, 1993;Sorbie et al, 1994;Li and Lake, 1996;Gharbi et al 1998;Gharbi et al 1998). In 1942 dimensionless scaling groups to investigate immiscible displacement of oil by water.…”

Section: Introductionmentioning

confidence: 96%

Scaling Immiscible Displacements in Porous Media with Horizontal Wells

2006

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Despite the increase in horizontal well applications, scaling fluid displacement in porous medium with horizontal wells is yet to be fully investigated. Determining the conditions under which horizontal wells may lead to better oil recovery is of great importance to the petroleum industry. In this paper, a numerical sensitivity study was performed for several well configurations. The study is performed in order to reveal the functional relationships between the scaling groups governing the displacement and the performance of immiscible displacements in homogeneous reservoirs produced by horizontal wells. These relationships can be used as a quick prediction tool for the fractional oil recovery for any combinations of the scaling groups, thus eliminating the need for the expensive fine-mesh simulations. In addition, they provide the condition under which a horizontal well configuration may yield better recovery performance. These results have potential applications in modeling immiscible displacements and in the scaling of laboratory displacements to field conditions.

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On scaling immiscible displacements in permeable media

Cited by 25 publications

References 31 publications

Application of Gene Expression Programming (GEP) in Modeling Hydrocarbon Recovery in WAG Injection Process

Application of Gene Expression Programming (GEP) in Modeling Hydrocarbon Recovery in WAG Injection Process

Neural Network Prediction Model of Miscible Displacements in Heterogeneous Reservoirs

Scaling Immiscible Displacements in Porous Media with Horizontal Wells

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