Optimization of the Injection Rate of Well Group Water Injection Using BP Neural Network Method

Yun Lin Jia; Jin Cheng Xu

doi:10.4028/www.scientific.net/AMR.734-737.1219

Paper Titles

Investigation on Polymer Adsorption and Retention in a Polymer Flooded Reservoir
p.1200

Minimum Gas Influx Calculation in Gas Drilling after Water Production Based on Absorption Characteristics of Rock
p.1204

Oil Containment by Floating Boom under Wave-Current Coupling Condition I: Numerical Experimental Platform
p.1209

Optimization of Polymer Injection Program for Second-Class Oil-Bearing Layer
p.1213

Optimization of the Injection Rate of Well Group Water Injection Using BP Neural Network Method
p.1219

Optimization Technique in Double Stepped Thin Horizontal Well
p.1226

Petroleum Exploration Potential of Xisha Trough Basin in SCS
p.1230

Research of Retarded Acid Formula System of Qijiabei Oil Field
p.1235

Research on the CO₂ Corrosion Inhibitor Technology in Oil and Gas Fields
p.1240

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 734-737Optimization of the Injection Rate of Well Group...

Optimization of the Injection Rate of Well Group Water Injection Using BP Neural Network Method

Abstract:

This page considers split methods on well group water injection rate researched by predecessors. So the dividing coefficient is introduced in determining the proper well group water injection. Based on considering all factors that affect the well group water injection, it analyzed the factors that were considered in the course of determining well group split coefficient. It adopted BP neural network method to determine well group split coefficient. Choosing the development data of one year, it established the neural network model about split coefficient and other values to determine well group split coefficient. Then it obtained the single well water injection rate.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 734-737)

Pages:

1219-1225

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.734-737.1219

Citation:

Cite this paper

Online since:

August 2013

Authors:

Yun Lin Jia, Jin Cheng Xu

Keywords:

BP Neural Network (BPNN), Dividing Coefficient, Water Injection

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Li Chunsheng, Tan Minzi. Research on Single Well Production Prediction Based on Improved BP Neural Networks [J]. Science Technology and Engineering, 2011, 11(31): 7766~7769.

Google Scholar

[2] Lou Jingjie, Zhang Qingsong. Research on Forecasting Model of Gas-oil Ratio Based on the Neural Network [J]. Xinjiang Oil & Gas, 2007, 3(3): 31~33.

Google Scholar

[3] Liu Jian. Research on Dynamic Model of Reservoir with the Method of Neural Network [D]. China Petroleum University (Peking), (2010).

Google Scholar

[4] Chen Yanqing. The theory of neural network and its applications in control engineering. Northwest Polytechnic University Press, (1991).

Google Scholar

[5] Zhang Ji-Cheng, Song Kao-Ping. Considerable potential remains after Daqing polymer flood [J]. Oil & Gas Journal, Sept. 15, 2008, 106(35): 61-66.

Google Scholar

[6] ZHANG Ji-Cheng, SONG Kao-Ping, LIU Li and YANG Er-Long. Investigation on Mechanisms of Polymer Enhanced Oil Recovery by Nuclear Magnetic Resonance and Microscopic Theoretical Analysis [J]. Chinese Physics Letters, May, 2008, 25(5): 1750-1752.

DOI: 10.1088/0256-307x/25/5/062

Google Scholar

[7] Li Yanbin. Probing Modification of BP Learning-Rate for Multi-layer Feed-Forward Neural Network [J]. Journal of Shanghai Jiaotong University, 1995, 29(2): 61~67.

Google Scholar

[8] Wang Wencheng. on Adaptive Learning Rate in Neural Network Using Back Propagation [J]. Computer Science, 1995, 22(4): 48~50.

Google Scholar