大庆石油学院学报2011，Vol.35Issue(6)：35-40,6.
逐类组合支持向量机在致密储层判识和产能预测中的应用
摘要
Abstract
It is difficult to identify reservoir and predict deliverability accurately in the tight resecpyoir. This paper proposes a new modeling method -termwise-combination support vector machine, (TCS-VM). Firstly, we apply support vector classification (SVC) to achieve reservoir identification, and then use the support vector regression (SVR) to establish deliverability prediction model by category. Finally, reservoir identification and deliverability prediction are implemented in the TCSVM model. After the noise reduction, dimension reduction in the earlier days, this model can reduce interference from the cat-egories of samples, thus improve greatly the accuracy of reservoir identification and deliverability prediction. The model is applied to identify reservoir and predict deliverability in M51 reservoir, which belongs to the central gas-field of Qrdos Basin. Ninety two samples were acquired by zonal testing from the selected nineteen wells to identify the gas horizon, gas-bearing horizon, dry zone, water layer and deliverability prediction. The paper chooses seventy eight samples randomly for training, and the remaining 14 to be the testing. To establish the model of M51 reservoir in the central gas-field, we select ten parameters which related closely with the reservoir characteristics as input variables. Then we use the model to achieve reservoir identification and deliverability prediction. The results show that the model prediction error is lower than the traditional modeling method and polynomial self-organizing neural networks (MOSN). Especially, the principal component analysis and termwise-combination support vector machine model (PCA-TCSVM) prediction error is the lowest (average absolute error 0. 359, average relative error 0. 036). Therefore, PCA-TCSVM model can generally reduce interference from the categories of samples and improve the accuracy. This will have a positive significance for oil and gas exploration.关键词
逐类组合支持向量机/气层判识/气层产能预测/陕甘宁盆地马五1气藏Key words
termwise-combination of support vector machines/ gas horizon identification/ deliverability prediction/ M51 reservoir in the Ordos basin分类
能源科技引用本文复制引用
庞河清,匡建超,蔡左花,王众,黄耀综..逐类组合支持向量机在致密储层判识和产能预测中的应用[J].大庆石油学院学报,2011,35(6):35-40,6.基金项目
教育部规划基金项目(11YJAZH043) （11YJAZH043）
四川石油天然气研究中心重点资助项目(川油气科SKA09—01) （川油气科SKA09—01）
