改进的PSO-LSSVM火山岩气藏压裂水平井产能预测模型

 在火山岩气藏压裂水平井产能预测模型中,影响因素多、实际样本少、各项参数获取不完整,因而利用常规方法预测的误差较大。为了充分地利用现有数据资料,从而快速有效地确定火山岩气藏压裂水平井产能,本文采用灰色关联方法确定了影响火山岩气藏压裂水平井产能的因素,利用粒子群算法对最小二乘支持向量机参数进行了优化,同时考虑到不同参数的敏感性,引入因素权重,形成了改进的PSO-LSSVM火山岩气藏压裂水平井产能预测模型。模型既充分利用了最小二乘支持向量机的小样本学习能力强和计算简单的特点,又发挥了粒子群算法计算速度快和具有较强的全局搜索能力的优点,还兼顾了各因素之间相互作用的影响。使用改进的PSO-LSSVM模型与传统的PSO-LSSVM模型和BP-LM模型进行计算对比的结果表明,改进的PSO-LSSVM模型所需的计算迭代次数更少,计算精度更高,进行模型预测的结果也更精确。

Improved PSO-LSSVM Productivity Prediction Model for the Fractured Horizontal Well in Volcanic Gas Reservoir

The existing productivity prediction model of fractured horizontal well in volcanic gas reservoir has more influence factors, less real samples, and incomplete parameters, therefore, it is difficult to accurately predict the productivity by using conventional methods. In order to quickly and effectively make certain of the productivity of fractured horizontal well in volcanic gas reservoir with existing data, the influence factors are determined by using Grey Relational Analysis(GRA), and the sensitivity of factor weights is considered to amend the algorithm. The improved PSO-LSSVM productivity model is established based on the parameters of Least Squares Support Vector Machines (LSSVM) which are optimized by Particle Swarm Optimization (PSO) algorithm. This model not only makes full use of the characteristics of the LSSVM small samples, which possess the strong learning ability and simple calculation, but also takes the advantages of fast calculation and better global searching ability of PSO. Comparing the PSO-LSSVM model with the BP-LM model, the improved PSO-LSSVM model has less iteration times, higher calculation precision, and more accurate predict results.