Lithofacies identification is a crucial work in reservoir characterization and modeling. The vast inter-well area can be supplemented by facies identification of seismic data. However, the relationship between lithofacies and seismic information that is affected by many factors is complicated. Machine learning has received extensive attention in recent years, among which support vector machine (SVM) is a potential method for lithofacies classification. Lithofacies classification involves identifying various types of lithofacies and is generally a nonlinear problem, which needs to be solved by means of the kernel function. Multi-kernel learning SVM is one of the main tools for solving the nonlinear problem about multi-classification. However, it is very difficult to determine the kernel function and the parameters, which is restricted by human factors. Besides, its computational efficiency is low. A lithofacies classification method based on local deep multi-kernel learning support vector machine (LDMKL-SVM) that can consider low-dimensional global features and high-dimensional local features is developed. The method can automatically learn parameters of kernel function and SVM to build a relationship between lithofacies and seismic elastic information. The calculation speed will be expedited at no cost with respect to discriminant accuracy for multi-class lithofacies identification. Both the model data test results and the field data application results certify advantages of the method. This contribution offers an effective method for lithofacies recognition and reservoir prediction by using SVM.

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基于局部深度多核学习的支持向量机岩相识别

岩相识别是储层表征和建模的关键工作。广阔的井间区域可以通过地震数据的相识别来补充。但是，受许多因素影响的岩相和地震信息之间的关系很复杂。近年来，机器学习受到广泛关注，其中支持向量机（SVM）是岩相分类的一种潜在方法。岩相分类涉及识别各种类型的岩相，通常是一个非线性问题，需要借助核函数来解决。多核学习支持向量机是解决非线性多分类问题的主要工具之一。但是，确定内核功能和参数非常困难，受人为因素的限制。此外，其计算效率低。提出了一种基于局部深度多核学习支持向量机（LDMKL-SVM）的岩相分类方法，该方法可以考虑低维全局特征和高维局部特征。该方法可以自动学习核函数和支持向量机的参数，以建立岩相与地震弹性信息之间的关系。关于多类别岩相识别的判别准确性，将免费加快计算速度。模型数据测试结果和现场数据应用结果均证明了该方法的优势。这一贡献为使用SVM进行岩相识别和储层预测提供了一种有效的方法。提出了一种基于局部深度多核学习支持向量机（LDMKL-SVM）的岩相分类方法，该方法可以考虑低维全局特征和高维局部特征。该方法可以自动学习核函数和支持向量机的参数，以建立岩相与地震弹性信息之间的关系。关于多类别岩相识别的判别准确性，将免费加快计算速度。模型数据测试结果和现场数据应用结果均证明了该方法的优势。这一贡献为使用SVM进行岩相识别和储层预测提供了一种有效的方法。提出了一种基于局部深层多核学习支持向量机（LDMKL-SVM）的岩相分类方法，该方法可以考虑低维全局特征和高维局部特征。该方法可以自动学习核函数和支持向量机的参数，以建立岩相与地震弹性信息之间的关系。关于多类别岩相识别的判别准确性，将免费加快计算速度。模型数据测试结果和现场数据应用结果均证明了该方法的优势。这一贡献为使用SVM进行岩相识别和储层预测提供了一种有效的方法。