Fast high-resolution prediction of multi-phase flow in fractured formations

Pau, George Shu Heng; Finsterle, Stefan; Zhang, Yingqi

doi:10.1016/j.advwatres.2015.12.008

Title: Fast high-resolution prediction of multi-phase flow in fractured formations

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Abstract

The success of a thermal water flood for enhanced oil recovery (EOR) depends on a detailed representation of the geometrical and hydraulic properties of the fracture network, which induces discrete, channelized flow behavior. The resulting high-resolution model is typically computationally very demanding. Here, we use the Proper Orthogonal Decomposition Mapping Method to reconstruct high-resolution solutions based on efficient low-resolution solutions. The method requires training a reduced order model (ROM) using high- and low-resolution solutions determined for a relatively short simulation time. For a cyclic EOR operation, the oil production rate and the heterogeneous structure of the oil saturation are accurately reproduced even after 105 cycles, reducing the computational cost by at least 85%. Finally, the method described is general and can be potentially utilized with any multiphase flow model.

Authors:

^[1];

^[2]; Zhang, Yingqi ^[2]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Climate and Ecosystem Sciences Division
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Energy Geosciences Division

Publication Date:: Mon Dec 14 00:00:00 EST 2015

Research Org.:: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

OSTI Identifier:: 1471005

Alternate Identifier(s):: OSTI ID: 1358731

Grant/Contract Number:: AC02-05CH11231

Resource Type:: Accepted Manuscript

Journal Name:: Advances in Water Resources

Additional Journal Information:: Journal Volume: 88; Journal Issue: C; Journal ID: ISSN 0309-1708

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 54 ENVIRONMENTAL SCIENCES; 58 GEOSCIENCES; 97 MATHEMATICS AND COMPUTING

Citation Formats


                    Pau, George Shu Heng, Finsterle, Stefan, and Zhang, Yingqi. Fast high-resolution prediction of multi-phase flow in fractured formations.  United States: N. p., 2015. 
Web.  doi:10.1016/j.advwatres.2015.12.008.

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                    Pau, George Shu Heng, Finsterle, Stefan, & Zhang, Yingqi. Fast high-resolution prediction of multi-phase flow in fractured formations.  United States.  https://doi.org/10.1016/j.advwatres.2015.12.008

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                    Pau, George Shu Heng, Finsterle, Stefan, and Zhang, Yingqi. Mon .  
"Fast high-resolution prediction of multi-phase flow in fractured formations".  United States.  https://doi.org/10.1016/j.advwatres.2015.12.008.  https://www.osti.gov/servlets/purl/1471005.

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@article{osti_1471005,

  title        = {Fast high-resolution prediction of multi-phase flow in fractured formations},

  author       = {Pau, George Shu Heng and Finsterle, Stefan and Zhang, Yingqi},

  abstractNote = {The success of a thermal water flood for enhanced oil recovery (EOR) depends on a detailed representation of the geometrical and hydraulic properties of the fracture network, which induces discrete, channelized flow behavior. The resulting high-resolution model is typically computationally very demanding. Here, we use the Proper Orthogonal Decomposition Mapping Method to reconstruct high-resolution solutions based on efficient low-resolution solutions. The method requires training a reduced order model (ROM) using high- and low-resolution solutions determined for a relatively short simulation time. For a cyclic EOR operation, the oil production rate and the heterogeneous structure of the oil saturation are accurately reproduced even after 105 cycles, reducing the computational cost by at least 85%. Finally, the method described is general and can be potentially utilized with any multiphase flow model.},

  doi          = {10.1016/j.advwatres.2015.12.008},

  journal      = {Advances in Water Resources},

  number       = C,

  volume       = 88,

  place        = {United States},

  year         = {Mon Dec 14 00:00:00 EST 2015},

  month        = {Mon Dec 14 00:00:00 EST 2015}

}

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