A pragmatic technique has been developed to accurately determine the total organic carbon (TOC) content in shale formations by analyzing well logging measurements. More specifically, a generalized equation has been proposed firstly to determine the positive separation (i.e., ΔlogR) which is transformed as an explanatory variable. Then, a multiple linear regression technique is employed to find relationship between such a transformed explanatory variable and the TOC content from the logging measurements. The TOC content is determined once the fisher distribution together with its coefficient has achieved a confidence level of 95% or higher. As for a total of 45 sets of logging measurements, the newly developed technique is found to be able to reproduce the measured TOC values with the correlation coefficient (R2) of 0.828. The presence of uranium is found to be the key for determining the TOC content than the interval transit time and the resistivity.


Not only does the TOC content affect both the shale gas saturation and the quality of shale gas reservoirs, but also is the key to assess the commercial value of the shale gas reservoirs (Jenkins and Boyer II, 2008). Therefore, it is of fundamental and practical importance to accurately quantify the TOC contents of shale formations under various conditions.

Traditionally, TOC content can be directly determined by analyzing the core samples collected from the wells (Spears et al., 2011) and by using either geochemical spectroscopy logging (Charsky and Herron, 2013) or conventional log measurements (Fertl and Chilingar, 1988; Passey et al., 1990). Although analysis of rock sample is time-consuming, expensive, and limited, it is the most accurate method for estimating the TOC content. Geochemical spectroscopy logging shows its potential to directly measure the TOC content and then optimize the coring samples in future wells, but it has seen limited field applications due to the associated high-costs and strict requirements for the well conditions (Joshi et al., 2015). However, it is a time-saving, economical way to determine TOC by conventional log measurements using statistical and artificial intelligence algorithms.

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