Predicting total organic carbon from few well logs aided by well-log attributes

Abstract

Derivative/volatility well-log attributes from very few commonly recorded well logs can assist in the prediction of total organic carbon (TOC) in shales and tight formations. This is of value where only limited suites of well logs are recorded, and few laboratory measurements of TOC are conducted on rock samples. Data from two Lower-Barnett-Shale (LBS) wells (USA), including well logs and core analysis is considered. It demonstrates how well-log attributes can be exploited with machine learning (ML) to generate accurate TOC predictions. Six attributes are calculated for gamma-ray (GR), bulk-density (PB) and compressional-sonic (DT) logs. Used in combination with just one of those recorded logs, those attributes deliver more accurate TOC predictions with ML models than using all three recorded logs. When used in combination with two or three of the recorded logs, the attributes generate TOC prediction accuracy comparable with ML models using five recorded well logs. Multi-K-fold-cross-validation analysis reveals that the K-nearest-neighbour algorithm yields the most accurate TOC predictions for the LBS dataset. The extreme-gradient-boosting (XGB) algorithm also performs well. XGB is able to provide information about the relative importance of each well-log attribute used as an input variable. This facilitates feature selection making it possible to reduce the number of attributes required to generate accurate TOC predictions from just two or three recorded well logs.