Forecast Model for Gas Well Productivity Based on PSO and SVM

Abstract:

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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:

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 et al., "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:

$35.00

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