TOC determination of Zhangjiatan shale of Yanchang formation, Ordos Basin, China, using support vector regression and well logs

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.