Total organic carbon content (TOC) is an important parameter for shale gas resource evaluation. Among the existing TOC prediction methods, ΔlgR method is the most widely used. There are two main problems in this method: 1) assuming a linear relationship between ΔlgR and TOC; 2) manually determining the baseline and introducing human error. Due to the frequent alternating changes in the sedimentary environments of continental shale, the heterogeneity of it is strong. In this case, the relationship between well logs and TOC is complex and nonlinear, which leads to incorrect results for predicted TOC using ΔlgR method. The objective of this paper is to develop a new empirical method to determine the TOC of continental shale using support vector regression (SVR) based on grid search cross-validation. The optimized SVR method can fit the nonlinear relationships between log data and TOC well, and it is suitable for TOC estimation in strong heterogeneity case such as continental shale. Three hundred sixty-four measured TOC samples of Zhangjiatan shale and the corresponding GR, DEN, AC, U logs data were used to construct the SVR model. Compared with the ΔlgR method, the accuracy of the SVR model to calculate TOC is obviously higher.

总有机碳含量（TOC）是评估页岩气资源的重要参数。在现有的TOC预测方法中，ΔlgR方法是使用最广泛的方法。该方法存在两个主要问题：1）假设ΔlgR与TOC之间存在线性关系；2）手动确定基线并引入人为错误。由于大陆页岩沉积环境频繁交替变化，其非均质性很强。在这种情况下，测井与TOC之间的关系是复杂且非线性的，这会导致使用ΔlgR方法预测TOC的结果不正确。本文的目的是开发一种新的经验方法，以基于网格搜索交叉验证的支持向量回归（SVR）确定大陆页岩的总有机碳。优化的SVR方法可以很好地拟合测井数据与TOC之间的非线性关系，适用于大陆页岩等强非均质情况下的TOC估算。利用张家滩页岩的364份测得的TOC样品和相应的GR，DEN，AC，U测井数据构建了SVR模型。与ΔlgR方法相比，SVR模型计算TOC的准确性明显更高。