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Research Papers: Petroleum Engineering

An Empirical Model to Estimate Sweep Efficiency of a Surfactant-Alternating-Gas Foam Process in Heterogeneous Reservoirs

[+] Author and Article Information
Jun Yang

Faculty of Engineering and Applied Science,
University of Regina,
Regina, SK S4S0A2
e-mail: yang233j@uregina.ca

Xiangzeng Wang

Shaanxi Yanchang Petroleum (Group),
Shaanxi 710075, China
e-mail: sxycpcwxz@126.com

Yongchao Yang

Shaanxi Yanchang Petroleum (Group),
Shaanxi 710075, China
e-mail: zyytyyc@126.com

Xiaolong Peng

Faculty of Engineering and Applied Science,
University of Regina,
Regina, SK S4S0A2
e-mail: peng200x@uregina.ca

Fanhua Zeng

Faculty of Engineering and Applied Science,
University of Regina,
Regina, SK S4S0A2
e-mail: fanhua.zeng@uregina.ca

1Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the Journal of Energy Resources Technology. Manuscript received March 26, 2019; final manuscript received May 22, 2019; published online June 20, 2019. Assoc. Editor: Hameed Metghalchi.

J. Energy Resour. Technol 141(12), 122902 (Jun 20, 2019) (12 pages) Paper No: JERT-19-1180; doi: 10.1115/1.4043861 History: Received March 26, 2019; Accepted May 23, 2019

A surfactant-alternating-gas (SAG) process is a promising enhanced oil recovery (EOR) method for tight oil reservoirs. In this study, an empirical model is developed to predict the dynamic performance of a SAG process including sweep efficiency of multiple types of well patterns, in which major factors of the SAG process are involved, including gas channeling, reservoir heterogeneity, gravity segregation, and the instability of a foam structure. A novel empirical model is proposed to estimate the recovery factor of a SAG process in typical well patterns, which divides the whole area into three parts based on dominate occupation in situ fluids. Estimating the breakthrough time of each area is the key of this model. A new concept pseudomobility ratio is proposed to convert the negative effect of heterogeneity into unfavorable increment of mobility ratio. Numerical simulation studies are introduced to validate the proposed SAG empirical model. The comparison shows that the SAG performance model is highly consistent with the numerical simulation results calculated by cmg. Sensitivity analysis is introduced to study the effects of variables in the SAG process, including the fluid injection rate, slug size, slug proportion, and reservoir heterogeneity. Oil production estimated by the proposed model is also validated with field production data collected from the Ganguyi SAG project in China, and the growth trend of oil production agrees well with the field data. The proposed model provides a fast approach to predict the dynamic performance of SAG flooding in a field scale, which can be used as a tool to evaluate and optimize current operational parameters.

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Figures

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Fig. 1

A quarter of the well pattern scheme of the SAG process trizone model

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Fig. 2

Equivalent areal sweep efficiency vs. mobility ratio at varying permeability variation [35,36]

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Fig. 3

Relation pattern of vertical sweep efficiency varying with mobility ratio, viscous force, and gravity effect [37]

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Fig. 4

Geological models with different permeability variations: (a) V = 0.1, (b) V = 0.4, and (c) V = 0.8

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Fig. 5

Initial relative permeability curves: (a) water–oil relative permeability curves and (b) gas–oil relative permeability curves

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Fig. 6

Oil recovery factor versus production time at different gas injection rates (m3/day), at 20 °C, 101 kPa

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Fig. 7

Oil recovery factor versus production time at different water injection rates (m3/day), at 20 °C, 101 kPa

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Fig. 8

Oil recovery factor versus production time with different slug sizes, at 20 °C, 101 kPa

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Fig. 9

Oil recovery factor versus production time with different gas–liquid ratios of each slug, at 20 °C, 101 kPa

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Fig. 10

Oil recovery factor versus production time relation with different models: V = 0.1, Qgsc = 200 m3/day, and Qlsc = 8 m3/day, at 20 °C, 101 kPa

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Fig. 11

Recovery factor–time relation with different methods: V = 0.4, Qgsc = 200 m3/day, and Qlsc = 4 m3/day, at 20 °C, 101 kPa

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Fig. 12

Recovery factor–time relation with different methods: V = 0.8, Qgsc = 300 m3/day, and Qlsc = 4 m3/day, at 20 °C, 101 kPa

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Fig. 13

Result comparison between the two-front model and the cmg on recovery factor

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Fig. 14

Result comparison between the one-front model and cmg on recovery factor

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Fig. 15

Comparison between field production data of the one-front model results and the two-front model results

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Fig. 16

Flow chart of the proposed model

Tables

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