Oil production predicting with modified BP neural network method

DOI

10.2991/csss-14.2014.33How to use a DOI?

Keywords

Oil field;oil production; neural network; predicting accuracy

Abstract

Feasibility of oil production predicting results influence the annual planning and long-term field development plan of oil field, so the selection of predicting models plays a core role. In this paper, a common and useful model is introduced, it is,the neural network model. By using this model to predict the oil production in DAQ oilfield in China, advantages and disadvantages of the model has been discussed. The predicting results show: the fitting accuracy by the neural network model is high, and the prediction error is smaller than 10%, so neural network model can be used to short-term forecast of oil production, after changing the weighting value in training, we can also improve the predicting accuracy, however, this process takes much time. Next, our team will try to develop new theory to shorten the training time.

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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TY  - CONF
AU  - Liu Haohan
AU  - Li Wei
AU  - Zhang Songlin
PY  - 2014/06
DA  - 2014/06
TI  - Oil production predicting with modified BP neural network method
BT  - Proceedings of the 3rd International Conference on Computer Science and Service System
PB  - Atlantis Press
SP  - 146
EP  - 148
SN  - 1951-6851
UR  - https://doi.org/10.2991/csss-14.2014.33
DO  - 10.2991/csss-14.2014.33
ID  - Haohan2014/06
ER  -