Forecast model for gas well productivity based on GA and SVM

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• 作者：Chunbi XuJingcheng LiuJun Li
• 会议时间：2011-11-01
• 关键词：gas well productivity ; GA ; SVM ; forecast model
• 作者单位：Chunbi Xu,Jingcheng Liu(Chongqing University of Science and Technology, Chongqing, China,401331 ;Chongqing Petroleum And Natural gas Society,Chongqing,China,400030)Jun Li(Sinopec Northwest Oilfield Branch Yakela gas plant,Xinjiang, China,842017)
• 母体文献：2011全国特殊气藏开发技术研讨会论文集
• 会议名称：2011全国特殊气藏开发技术研讨会
• 会议地点：重庆
• 主办单位：重庆市科学技术协会
• 语种：chi

摘要

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.