Research Output

PCA-ANFIS based prediction for water Injection effectiveness cycle in oil fields.

  By proposing a numerical based method on PCA-ANFIS(Adaptive Neuro-Fuzzy Inference System), this paper is focusing on solving the problem of uncertain cycle of water injection in the oilfield. As the dimension of original data is reduced by PCA, ANFIS can be applied for training and testing the new data proposed by this paper. The correctness of PCA-ANFIS models are verified by the injection statistics data collected from 116 wells inside an oilfield, the average absolute error of testing is 1.80 months. With comparison by non-PCA based models which average error is 4.33 months largely ahead of PCA-ANFIS based models, it shows that the testing accuracy has been greatly enhanced by our approach. With the conclusion of the above testing, the PCA-ANFIS method is robust in predicting the effectiveness cycle of water injection which helps oilfield developers to design the water injection scheme.

  • Type:

    Article

  • Date:

    31 January 2014

  • Publication Status:

    Published

  • Publisher

    Trans Tech Publications

  • DOI:

    10.4028/www.scientific.net/AMM.530-531.422

  • ISSN:

    1660-9336

  • Library of Congress:

    TN Mining engineering. Metallurgy

  • Dewey Decimal Classification:

    622 Mining & related operations

Citation

Tian, Y., Liu, Y., Cheng, G. & Wang, Z. (2014). PCA-ANFIS based prediction for water Injection effectiveness cycle in oil fields. Applied Mechanics and Materials. 530/53, 422-428. doi:10.4028/www.scientific.net/AMM.530-531.422. ISSN 1660-9336

Authors

Keywords

Water injection; oil fields; PCA-ANFIS;

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