Proceedings of the 3rd International Conference on Computer Science and Service System

Oil production prediction with neural network method

Authors
Liu Haohan, Zhang Songlin, Li Wei
Corresponding Author
Liu Haohan
Available Online June 2014.
DOI
https://doi.org/10.2991/csss-14.2014.32How to use a DOI?
Keywords
correlation degree analysis; neural network; prediction
Abstract

Many kinds of method can be used to predict oil production, and the neural network method is one of the most basic methods to predict oil production. In this study a modified neural network method is proposed to predict oil production in oil field. A fuzzy cluster analysis is introduced to determine the major influencing factors and obtain non-dimensional data; a proper kernel function of the neural network structure is chosen to establish the relational expression of site variables and fit the relational expression of weight. A new predicting method based on the cluster analysis is proposed to predict the oil production. Good predicting results are obtained by introducing this new method to the Cong-D block of certain block faulted oilfield of China.

Copyright
© 2014, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 3rd International Conference on Computer Science and Service System
Series
Advances in Intelligent Systems Research
Publication Date
June 2014
ISBN
10.2991/csss-14.2014.32
ISSN
1951-6851
DOI
https://doi.org/10.2991/csss-14.2014.32How to use a DOI?
Copyright
© 2014, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Liu Haohan
AU  - Zhang Songlin
AU  - Li Wei
PY  - 2014/06
DA  - 2014/06
TI  - Oil production prediction with neural network method
BT  - Proceedings of the 3rd International Conference on Computer Science and Service System
PB  - Atlantis Press
SP  - 143
EP  - 145
SN  - 1951-6851
UR  - https://doi.org/10.2991/csss-14.2014.32
DO  - https://doi.org/10.2991/csss-14.2014.32
ID  - Haohan2014/06
ER  -