PCA-ANFIS Based Prediction for Water Injection Effectiveness Cycle in Oil Fields

Abstract:

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.

• Periodical: Applied Mechanics and Materials (Volumes 530-531)
• Pages: 422-428
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.530-531.422
• Online since: February 2014
• Authors: Ya Juan Tian, Ye Liu, Guo Jian Cheng, Zhe Wang
• Keywords: ANFIS, Effectiveness Cycle, Prediction, Principal Component Analysis (PCA), Water Injection

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