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

The accurate prediction of gas well productivity is an important task in gas reservoir engineering research. According to the global optimization ability of the genetic algorithm (GA) and the superior regression performance of the support vector machine (SVM), this paper proposed a method based on GA and SVM to improve the prediction accuracy. As the proposed model can reduce the dimensionality of data space and preserve features of gas well productivity, compared with BP neural network model, the proposed GA-SVM model for gas well productivity in practical engineering has higher accuracy and speed, the maximum error is 1.5%. Thus, it provided a new method for the forecast of gas well productivity.

  Info
Periodical
Chapter
Chapter 8: Frontiers of Construction Information
Edited by
Dongye Sun, Wen-Pei Sung and Ran Chen
Pages
4958-4962
DOI
10.4028/www.scientific.net/AMM.71-78.4958
Citation
C. B. Xu, J. C. Liu, J. Li, "Forecast Model for Gas Well Productivity Based on GA and SVM", Applied Mechanics and Materials, Vols. 71-78, pp. 4958-4962, 2011
Online since
July 2011
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Price
$32.00
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