Application of support vector regression analysis to estimate total organic carbon content of Cambay shale in Cambay basin, India – a case study

Abstract

The objective of the present study is to estimate total organic carbon (TOC) content over the entire thickness of Cambay Shale, in the boreholes of Jambusar–Broach block of Cambay Basin, India. To achieve this objective, support vector regression (SVR), a supervised data mining technique, has been utilized using five basic wireline logs as input variables. Suitable SVR model has been developed by selecting epsilon-SVR algorithm and varying three different kernel functions and parameters like gamma and cost on a sample dataset. The best result is obtained when the radial-basis kernel function with gamma = 1 and cost = 1, are used. Finally, the performance of developed SVR model is compared with the ΔlogR method. The TOC computed by SVR method is found to be more precise than the ΔlogR method, as it has better agreement with the core-TOC. Thus, in the present study area, the SVR method is found to be a powerful tool for estimating TOC of Cambay Shale in a continuous and rapid manner.