A Fast POD-Based Method for Predicting Oil and Water Flow in Water-Drive Reservoir

SUN Xian-hang; XU Ming-hai; GONG Liang; JIA Xin-xin; ZHOU Hui

doi:10.3879/j.issn.1000-0887.2015.12.002

Volume 36 Issue 12

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SUN Xian-hang, XU Ming-hai, GONG Liang, JIA Xin-xin, ZHOU Hui. A Fast POD-Based Method for Predicting Oil and Water Flow in Water-Drive Reservoir[J]. Applied Mathematics and Mechanics, 2015, 36(12): 1228-1237. doi: 10.3879/j.issn.1000-0887.2015.12.002

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A Fast POD-Based Method for Predicting Oil and Water Flow in Water-Drive Reservoir

doi: 10.3879/j.issn.1000-0887.2015.12.002

¹Department of Thermal Energy and Power Engineering, College of Pipeline and Civil Engineering, China University of Petroleum, Qingdao, Shandong 266580, P.R.China;²Institute of Oceanographic Instrumentation, Shandong Academy of Sciences, Qingdao, Shandong 266001, P.R.China;³Water Supply Company of Liaohe Oilfield, Panjin,Liaoning 124010, P.R.China

Funds: The National Natural Science Foundation of China(51276199); The National Science and Technology Major Project of China(2011ZX05017_004_HZ01)

Received Date: 2015-06-11
Rev Recd Date: 2015-09-30
Publish Date: 2015-12-15

Abstract

Abstract

A fast method based on the proper orthogonal decomposition (POD) technique for predicting oil and water flow in water-drive reservoir was proposed. The reduced order model of oil and water flow in water-drive reservoir was generated with the POD. An ensemble of 100 samples of pressure and water saturation snapshots in the time range of [0 d, 500 d] with an interval step of 5 d for the 2D water-drive reservoir model was obtained through numerical reservoir simulation, and the POD was applied to extract a reduced set of POD basis functions from these snapshots. After the injection and production parameters were changed continuously, the obtained POD basis functions combined with the reduced order model were used to predict the new physical fields. The research results show that fast and accurate predictions can be achieved with the proposed POD-based method, for the given example, the prediction errors of pressure and water saturation are less than 1.2% and 1.5%, respectively. What’s more, this POD-based method is 50 times faster in calculation than the traditional numerical reservoir simulation.
- proper orthogonal decomposition,
- reduced order model,
- water-drive reservoir,
- oil and water flow

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References(15)

References

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