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

Tian, Yajuan; Liu, Ye; Cheng, Guojian; Wang, Zhe

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.

Citation

Tian, Y., Liu, Y., Cheng, G., & Wang, Z. (2014). PCA-ANFIS based prediction for water Injection effectiveness cycle in oil fields.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 2014 2nd International Conference on Energy Engineering and Environment Engineering
• Start Date: Jan 10, 2014
• End Date: Jan 11, 2014
• Publication Date: 2014-02
• Deposit Date: Jun 1, 2016
• Peer Reviewed: Peer Reviewed
• Keywords: Water injection; oil fields; PCA-ANFIS
• Public URL: http://researchrepository.napier.ac.uk/id/eprint/10327