Proceedings of the 3rd Annual International Conference on Mechanics and Mechanical Engineering (MME 2016)

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An Artificial Intelligence Prediction Method of Bottomhole Flowing Pressure for Gas Wells Based on Support Vector Machine

Authors
Qin-Feng Di, Wei Chen, Jing-Nan Zhang, Wen-Chang Wang, Hui-Juan Chen
Corresponding Author
Qin-Feng Di
Available Online December 2016.
DOI
10.2991/mme-16.2017.28How to use a DOI?
Keywords
Flowing bottomhole pressure, Support vector machine, Random samples selection, Gas wells.
Abstract

The flowing bottomhole pressure (FBHP) of gas wells was affected by many factors. Although a lot of research works have been done to predict the FBHP and at least more than ten models were proposed, but no one can effectively provide an accurate results for all ranges of production data and conditions due to the existence of many uncertain relations between the changeable influence factors. In this paper, an artificial intelligence prediction method for FBHP based on the support vector machine (SVM), named the FBHP-SVM method, was studied, and a support vector regression (SVR) model with -insensitive loss function ( -SVR) based on radial basis function (RBF) was used to predict the FBHP of gas wells. Compared with the true values, the average absolute and relative errors of the new method were 0.27MPa and 2.29%, respectively. The FBHP-SVM method was also compared to the vertical pipe flowing method. The results showed this new method was a new practical tool to predict FBHP in gas wells and it had a satisfying prediction accuracy.

Copyright
© 2017, 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 Annual International Conference on Mechanics and Mechanical Engineering (MME 2016)
Series
Advances in Engineering Research
Publication Date
December 2016
ISBN
10.2991/mme-16.2017.28
ISSN
2352-5401
DOI
10.2991/mme-16.2017.28How to use a DOI?
Copyright
© 2017, 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

risenwbib
TY  - CONF
AU  - Qin-Feng Di
AU  - Wei Chen
AU  - Jing-Nan Zhang
AU  - Wen-Chang Wang
AU  - Hui-Juan Chen
PY  - 2016/12
DA  - 2016/12
TI  - An Artificial Intelligence Prediction Method of Bottomhole Flowing Pressure for Gas Wells Based on Support Vector Machine
BT  - Proceedings of the 3rd Annual International Conference on Mechanics and Mechanical Engineering (MME 2016)
PB  - Atlantis Press
SP  - 206
EP  - 214
SN  - 2352-5401
UR  - https://doi.org/10.2991/mme-16.2017.28
DO  - 10.2991/mme-16.2017.28
ID  - Di2016/12
ER  -