RESEARCH ARTICLE


Predicting the Oil Well Production Based on Multi Expression Programming



Xin Ma*, Zhi-bin Liu
School of Science, Southwest Petroleum University, Chengdu 610500, China


Article Metrics

CrossRef Citations:
0
Total Statistics:

Full-Text HTML Views: 3045
Abstract HTML Views: 1589
PDF Downloads: 400
ePub Downloads: 256
Total Views/Downloads: 5290
Unique Statistics:

Full-Text HTML Views: 1376
Abstract HTML Views: 826
PDF Downloads: 271
ePub Downloads: 196
Total Views/Downloads: 2669



© Ma and Liu; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 International Public License (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/legalcode), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the School of Science, Southwest Petroleum University, Chengdu 610500, China; E-mail: cauchy7203@gmail.com


Abstract

Predicting the oil well production is very important and also quite a complex mission for the petroleum engineering. Due to its complexity, the previous empirical methods could not perform well for different kind of wells, and intelligent methods are applied to solve this problem. In this paper the multi expression programming (MEP) method has been employed to build the prediction model for oil well production, combined with the phase space reconstruction technique. The MEP has shown a better performance than the back propagation networks, gene expression programming method and the Arps decline model in the experiments, and it has also been shown that the optimal state of the MEP could be easily obtained, which could overcome the over-fitting.

Keywords: ANN, gene expression programming, genetic algorithm, MEP, oil well production.