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A Comparative Study on Oil Performance Prediction Methods: The Case of Shuanghe Oilfield, China

Authors

  • Theoneste Nzayisenga China university of Geosciences, 388 Lumo Road, 430074,Wuhan,Hubei,China; Energy Water and Sanitation Authority, Po Box: 537 Kigali Rwanda
  • Xie Congjiao2 Congjiao Energy Water and Sanitation Authority, Po Box: 537 Kigali Rwanda
  • Jean de la Paix Mupenzi Independent Institute of Lay Adventists of Kigali, P.O. Box 6293 Kigali, Rwanda; State Key laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Science, 818 Beijing Road South, Urumqi, Xinjiang 830011,

DOI:

https://doi.org/10.62103/unilak.eajst.1.1.29

Keywords:

Shuanghe oilfield, Oil production prediction, Decline curve

Abstract

While being the dominant source of energy, oil has also brought affluence and power to different societies. Energy produced from oil is fundamental to all parts of society. In the foreseeable future, the majority of energy will still come from oil production. Consequently, reliable methods for forecasting that production are crucial. Petroleum engineers have searched for simple but reliable way to predict oil production for a long time. Many methods have been developed in the latest decades and one common practice is decline curve analysis. Prediction of future production of petroleum wells is important for cost-effective operations of the petroleum industry. This work presents a comparative analysis of methods used to predict the performance of Shuanghe oilfield, China.  Using decline curve analysis including three different methods: Arps empirical methods, LL-model and simplified model and the new simplified model, LL-Model, to crosscheck Arps exponential decline model prediction results. The results showed by the comparative analysis of predictions calculated proved LL-model to be the best predictor for Shuanghe oilfield since it takes into account more parameters than the old models used in this work. However, the subsurface information or parameters of the reservoir used in LL-model may not be available every time, therefore Arps models may apply as defined. In Shuanghe oilfield calculated average geological reserves N was estimated at 9449.41 ×104 tons, the average recoverable reserves NR were estimated to 4274.61×104 tons while the water cut was 97% and the water cut predicted by LL-model was 96.7%; not far from water flooding curves value. The exponential decline model showed recoverable reserves NR estimated around 4685.88×104 tons of oil while the decline phase of total development was estimated around 34 years which means that if the actual production conditions remain unchanged, Shuanghe oilfield would continue producing for another 25 years from 2008.

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Published

2011-05-02

How to Cite

Nzayisenga, T., Congjiao, X. C., & Mupenzi, J. de la P. (2011). A Comparative Study on Oil Performance Prediction Methods: The Case of Shuanghe Oilfield, China. East African Journal of Science and Technology, 1(1), 59–73. https://doi.org/10.62103/unilak.eajst.1.1.29

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