Forecast Model for Gas Well Productivity Based on GA and SVM

Abstract:

Article Preview

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:

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 et al., "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:

$35.00

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