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Prediction of Hydrocarbon Reservoirs Permeability Using Support Vector Machine

机译：基于支持向量机的油气藏渗透率预测

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摘要

Permeability is a key parameter associated with the characterization of any hydrocarbon reservoir. In fact, it is not possible to have accurate solutions to many petroleum engineering problems with-out having accurate permeability value. The conventional methods for permeability determination are core analysis and well test techniques. These methods are very expensive and time consuming. Therefore, attempts have usually been carried out to use artificial neural network for identification of the relationship between the well log data and core permeability. In this way, recent works on artificial intelligence techniques have led to introduce a robust machine learning methodology called support vector machine. This paper aims to utilize the SVM for predicting the permeability of three gas wells in the Southern Pars field. Obtained results of SVM showed that the correlation coefficient between core and predicted permeability is 0.97 for testing dataset. Comparing the result of SVM with that of a general regression neural network (GRNN) revealed that the SVM approach is faster and more accurate than the GRNN in prediction of hydrocarbon reservoirs permeability.
机译：渗透率是与任何烃储层特征相关的关键参数。实际上，如果没有准确的渗透率值，就不可能对许多石油工程问题有准确的解决方案。确定渗透率的常规方法是岩心分析和试井技术。这些方法非常昂贵且耗时。因此，通常尝试使用人工神经网络来识别测井数据与岩心渗透率之间的关系。这样，有关人工智能技术的最新工作导致引入了一种强大的机器学习方法，即支持向量机。本文旨在利用支持向量机预测南帕尔斯地区三口气井的渗透率。支持向量机的结果表明，测试数据集的岩心与渗透率的相关系数为0.97。将SVM的结果与通用回归神经网络（GRNN）的结果进行比较后发现，在预测油气藏渗透率方面，SVM方法比GRNN更快，更准确。

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  • 来源
    《Mathematical Problems in Engineering》 |2012年第2期|p.247-264|共18页
  • 作者

    R. Gholami; A. R. Shahraki; M. Jamali Paghaleh;

  • 作者单位

    Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology,P.O. Box 316, Shahrood, Iran;

    Department of Industrial Engineering, University of Sistan and Baluchestan, Zahedan, Iran;

    Young Researchers Club, Islamic Azad University, Zahedan Branch, Zahedan 98168, Iran;

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