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Research Papers: Petroleum Engineering

Numerical Simulation of the Impact of Natural Fracture on Fluid Composition Variation Through a Porous Medium

[+] Author and Article Information
Ali Papi

Department of Chemical Engineering,
Faculty of Engineering,
Shahid Bahonar University of Kerman,
P.O. Box 76169133,
Kerman, Iran

Ali Mohebbi

Department of Chemical Engineering,
Faculty of Engineering,
Shahid Bahonar University of Kerman,
P.O. Box 76169133,
Kerman, Iran
e-mails: amohebbi@uk.ac.ir;
amohebbi2002@yahoo.com

S. Ehsan Eshraghi

Omid Petro Energy Khavaran Co.,
Science and Technology Park,
P.O. Box 113654563,
Mashhad, Iran;
Institute of Petroleum Engineering,
School of Chemical Engineering,
Faculty of Engineering,
University of Tehran,
P.O. Box 113654563,
Tehran, Iran

1Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received May 28, 2017; final manuscript received October 17, 2018; published online November 26, 2018. Assoc. Editor: Ray (Zhenhua) Rui.

J. Energy Resour. Technol 141(4), 042901 (Nov 26, 2018) (16 pages) Paper No: JERT-17-1250; doi: 10.1115/1.4041839 History: Received May 28, 2017; Revised October 17, 2018

In order to lessen the computational time in fractured oil reservoir simulations, all fractures are usually assumed to be as one equivalent fracture at the center or around the model. This, specially, has applications in industrial engineering software, where this assumption applies. In this study, using two general contradictory examples, it is shown that ignoring a fracture network and assuming an equivalent single-fracture has no logical justification and results in a considerable error. The effect of fracture aperture on composition distribution of a binary and a ternary mixture was also investigated. These mixtures were C1 (methane)/n-C4 (normal-butane) and C1 (methane)/C2 (ethane)/n-C4 (normal-butane), which were under diffusion and natural convection. Governing equations were numerically solved using matlab. One of the main relevant applications of this study is where permeability and temperature gradient are the key difference between reservoirs. Compositional distribution from this study could be used to estimate initial oil in place. Using this study, one can find the optimum permeability, namely the permeability at which the maximum species separation happens, and the threshold permeability (or fracture aperture), after which the convection imposes its effect on composition distribution. It is found that the threshold permeability is not constant from reservoir to reservoir. Also, one can find that full mixing happens in the model, namely heavy and light densities of top and bottom mix up together in the model. Furthermore, after maximum separation point, convection causes unification of components.

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Figures

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Fig. 1

Model geometry (a) (equivalent) single-fracture model and (b) (original) fracture network model

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Fig. 2

Density variation versus temperature(left) and weight fraction(right), obtained by the Peng–Robinson EOS for the binary mixture of C1/nC4 at P = 1.1 × 107 Pa

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Fig. 3

Density variation versus temperature (upper-left), methane mole fraction (upper-right), and ethane mole fraction (bottom) for different temperatures and mole fractions, obtained by the Peng–Robinson EOS for the ternary mixture of C1/C2/nC4 at P = 8.5 × 106 Pa

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Fig. 4

Boundary conditions and discovering well location for binary system

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Fig. 5

Boundary conditions and discovering well location for ternary system

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Fig. 6

A portion of a unit block cell of the fractured model grid system, which is repeated throughout all the system [36]

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Fig. 7

Experimental data of nC24 by El Maataoui [56] and predicted values by the model, used in this work, at top and bottom of a TGC in a binary mixture of nC12/nC24 with initial nC24 mass fraction of 15%, a temperature difference of 25 K, column height of 120 cm, and permeability of 6.1 × 10−11 m2

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Fig. 8

Effect of fracture aperture on methane molar distribution in a binary mixture of C1/n-C4 for the single-fracture model with matrix permeability of 0.05 md and variable fracture aperture of 0.01–0.5 mm

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Fig. 9

Effect of fracture aperture on methane molar distribution in a binary mixture of C1/n-C4 for the single-fracture model with matrix permeability of 1 md and variable fracture aperture of 0.01–0.5 mm

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Fig. 10

Density contours of a binary mixture of C1/n-C4 for the single-fracture model with fracture aperture of 0.1 mm and variable matrix permeability of 0.01–4 md

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Fig. 11

Effect of fracture aperture on methane and ethane molar distributions in a ternary mixture of C1/C2/n-C4 for the single-fracture model with matrix permeability of 1 md and fracture aperture of 0.01 mm, 0.25 mm, 0.5 mm, and 0.75 mm

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Fig. 12

Effect of fracture aperture on methane and ethane molar distribution in a ternary mixture of C1/C2/n-C4 for the fracture network model with matrix permeability of 1 md and fracture aperture of 0.01 mm, 0.25 mm, 0.5 mm, and 0.75 mm

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Fig. 13

Methane and ethane molar distributions in a ternary mixture of C1/C2/n-C4 with matrix permeability of 1md for: (a) the original fracture network model with fracture aperture of 0.6 mm and (b) the equivalent single-fracture model with faeq,x = 1.8 mm and faeq,y = 3.3 mm

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Fig. 14

Methane and ethane molar distributions in a ternary mixture of C1/C2/n-C4 with matrix permeability of 1md for: (a) the original fracture network model with fracture aperture of 0.2 mm and (b) the equivalent single-fracture model with faeq,x = 0.6 mm and faeq,y = 1.1 mm

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