• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of natural gas science and engineering >Support-vector-regression machine technology for total organic carbon content prediction from wireline logs in organic shale: A comparative study
【24h】

Support-vector-regression machine technology for total organic carbon content prediction from wireline logs in organic shale: A comparative study

机译：支持向量回归机技术从有机页岩测井测井中预测总有机碳含量：对比研究

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Organic shale is one of the most important unconventional oil and gas resources. Hydrocarbon potential prediction of organic shale such as total organic carbon (TOC) is an important evaluation tool, which primarily uses empirical equations. A support-vector machine is a set of supervised tools used for classification and regression problems. In this study, a support-vector machine for regression (SVR) is investigated to estimate the TOC content in gas-bearing shale. First, SVR technology is introduced including its basic concepts, associated regression algorithms and kernel functions, and a TOC prediction sketch that uses wireline logs. Then, one example is considered to compare three different regression algorithms and four different kernel functions in a packet dataset validation process and a leave-one-out cross-validation process. Error analysis indicates that the SVR method with the Epsilon-SVR regression algorithm and the Gaussian kernel produces the best results. The method of choosing the optimum Gamma value in the Gaussian kernel function is also introduced. Next, for comparison, the SVR-derived TOC with the optimal model and parameters is compared with the empirical formula and the Delta logR methods. Finally, in a real continuous TOC prediction using wireline logs, TOC prediction tests are performed using SVR to choose the optimal logs as inputs, and the optimal input is finally chosen. Additionally, the radial basis network (RBF) is also applied to perform tests with different inputs; the results of these tests are compared with those of the SVR method. This study shows that SVR technology is a powerful tool for TOC prediction and is more effective and applicable than a single empirical model, Delta logR and some network methods. (C) 2015 Elsevier B.V. All rights reserved.
机译：有机页岩是最重要的非常规油气资源之一。有机页岩的烃潜力预测（例如总有机碳（TOC））是重要的评估工具，主要使用经验方程式。支持向量机是一组用于分类和回归问题的监督工具。在这项研究中，研究了一种用于回归的支持向量机（SVR），以估算含气页岩中的TOC含量。首先，介绍了SVR技术，包括其基本概念，关联的回归算法和内核功能，以及使用有线日志的TOC预测草图。然后，考虑一个示例，在数据包数据集验证过程和留一法交叉验证过程中比较三种不同的回归算法和四种不同的内核函数。误差分析表明，采用Epsilon-SVR回归算法和高斯核的SVR方法可获得最佳效果。还介绍了在高斯核函数中选择最佳伽玛值的方法。接下来，为了进行比较，将具有最佳模型和参数的SVR衍生的TOC与经验公式和Delta logR方法进行比较。最后，在使用有线测井的真实连续TOC预测中，使用SVR进行TOC预测测试以选择最佳测井作为输入，最后选择最佳输入。此外，径向基网络（RBF）也可用于执行具有不同输入的测试。将这些测试的结果与SVR方法的结果进行比较。这项研究表明，SVR技术是用于TOC预测的强大工具，比单个经验模型，Delta logR和某些网络方法更有效，更适用。 （C）2015 Elsevier B.V.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号