Paper Title:
Forecast Model for Gas Well Productivity Based on PSO and SVM
  Abstract

It is very important to forecast the gas well productivity of gas reservoir accurately. On the basis of analyzing the parameter performance of support vector machine (SVM) for regression estimation, the paper proposes gas well productivity prediction model based on particle swarm optimization (PSO) and SVM. The parameter of SVM was optimized by PSO. This method took advantage of the minimum structure risk of SVM and the quickly globally optimizing ability of PSO. Compared with BP neural network model, the proposed GA-SVM model for gas well productivity in practical engineering has higher accuracy and speed, and the maximum error is 2.8% . Thus, it provided a new approach to predict the gas well productivity.

  Info
Periodical
Chapter
Chapter 3: Frontiers of Civil Engineering
Edited by
Dongye Sun, Wen-Pei Sung and Ran Chen
Pages
1915-1919
DOI
10.4028/www.scientific.net/AMM.71-78.1915
Citation
M. Yang, J. Li, J. C. Liu, "Forecast Model for Gas Well Productivity Based on PSO and SVM", Applied Mechanics and Materials, Vols. 71-78, pp. 1915-1919, 2011
Online since
July 2011
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Price
$32.00
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