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Research Papers: Petroleum Engineering

Semi-Analytical Proxy for Vapex Process Modeling in Heterogeneous Reservoirs

[+] Author and Article Information
Jindong Shi

School of Mining and Petroleum Engineering,
Markin/CNRL Natural Resources
Engineering Facility,
University of Alberta,
Edmonton, AB T6G 2W2, Canada

Juliana Y. Leung

School of Mining and Petroleum Engineering,
Markin/CNRL Natural Resources
Engineering Facility,
University of Alberta,
Edmonton, AB T6G 2W2, Canada
e-mail: juliana2@ualberta.ca

1Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received November 10, 2013; final manuscript received April 9, 2014; published online May 15, 2014. Assoc. Editor: W. David Constant.

J. Energy Resour. Technol 136(3), 032904 (May 15, 2014) (13 pages) Paper No: JERT-13-1317; doi: 10.1115/1.4027571 History: Received November 10, 2013; Revised April 09, 2014

Vapex (vapor extraction) is a nonthermal process that has significant potential in providing a more environmentally friendly and energy-efficient alternative to steam injection. Vaporized solvent injected in-situ dissolves into the oil and reduces oil viscosity, allowing the oil to flow to a horizontal production well via gravitational forces. While compositional simulators are available for assessing the Vapex performance, the simulation process may become difficult when taking into account the uncertainty due to reservoir heterogeneity. A semi-analytical proxy is proposed to model the process, in a way analogous to the steam-assisted gravity drainage (SAGD) model described by Butler, who demonstrated the similarity between two processes with a series of Hele-Shaw experiments and derived an analytical steady-state flow rate relationship that is comparable with the SAGD case. Solvent concentration and intrinsic diffusivity are introduced in this model instead of temperature and thermal diffusivity in SAGD. In this paper, analytical solutions and implementation details for the Vapex proxy are presented. The proposed approach is then applied to various reservoirs discretized with spatially varying rock porosity and permeability values; bitumen drainage rate and solvent penetration are calculated sequentially at grid blocks along the solvent–bitumen interface over incremental time steps. Results from this model are compared against experimental data available in the literature as well as detailed compositional simulation studies. Computational requirement of the proxy in comparison with numerical simulations is also emphasized. An important contribution from this work is that process physics are built directly into this proxy, giving it an advantage over other data-driven modeling approaches (e.g., regression). It can be used as an efficient alternative to expensive detailed flow simulations. It presents an important potential for assessing the uncertainty due to multiscale heterogeneity on effective mass transfer and the resulting recovery performance, as well as assisting decisions-making for future pilot and field development.

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References

Figures

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Fig. 1

Schematic of the solvent–bitumen interface in Vapex (adapted from Ref. [6])

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Fig. 2

Illustration of the drainage area observed in a Hele-Shaw cell Vapex experiment (adapted from Ref. [6])

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Fig. 3

Oil saturation and solvent (CO2) mole fraction profile at a distance away from the injector–producer well pair. The oil saturation gradually changes from 1 − Swi to Sor. The shaded area indicates the diffusion zone in a porous medium.

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Fig. 5

Comparison of solvent–bitumen interface position between Hele-Shaw experimental results [6] and proxy model predictions for the Athabasca bitumen sample in case study 1

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Fig. 6

Comparison of solvent–bitumen interface position between detailed flow simulations and proxy model predictions for the homogeneous reservoir case study 2

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Fig. 7

Comparison of oil production rate as a function with time between flow simulation results and proxy predictions (flow rate at the bottom interface node divided by the tuning parameter) for case study 2: (a) entire production period and (b) production after 600 days

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Fig. 8

Comparison of cumulative oil production as a function with time between flow simulation results and proxy predictions (flow rate at the bottom interface node divided by the tuning parameter) for case study 2: (a) entire production period and (b) production after chamber is fully developed

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Fig. 9

Absolute permeability and porosity field in x–z cross-sectional view for heterogeneous case study 3

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Fig. 10

Comparison of solvent–bitumen interface position between detailed flow simulations and proxy model predictions for the heterogeneous case study 3

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Fig. 11

Comparison of oil production rate as a function with time between flow simulation results and proxy predictions (flow rate at the bottom interface node divided by the tuning parameter) for case study 3: (a) entire production period and (b) production after 600 days

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Fig. 12

Comparison of cumulative oil production as a function with time between flow simulation results and proxy predictions (flow rate at the bottom interface node divided by the tuning parameter) for case study 3: (a) entire production period and (b) production after chamber is fully developed

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