Prediction of organic carbon content in oil shale based on logging: a case study in the Songliao Basin, Northeast China

The total organic carbon (TOC) content is an important parameter used to evaluate oil shale resources. TOC prediction methods based on resistivity (RT), density (DEN), acoustic (AC) and gamma ray (GR) logging curves have been widely used in the evaluation of hydrocarbon source rocks via linear regression (LR), the logging curve superposition method (Delta logR) and artificial neural networks (ANNs). These methods also have the potential to predict the quality of oil shale. Based on the measured TOC content of 462 core samples and corresponding logging (RT, DEN, AC and GR) data, Delta logR, LR and ANN models are established to predict the TOC contents of the oil shale in the nonmarine Songliao Basin. The results show that when the sample size is sufficient, the ANN model has the strongest prediction ability, followed by the LR model. When the sample size is scarce, the LR model can still maintain a relatively high prediction ability. The prediction ability of the Delta logR model is the lowest for different sample sizes, indicating that the Delta logR model commonly used for source rock evaluation is not suitable for TOC content prediction of oil shale. The different porosity characteristics caused by the organic matter content are the primary factors leading to the difference effect of TOC content logging inversion between oil shale and source rock.

Automated summary

The total organic carbon (TOC) content is an important parameter for evaluating oil shale resources. Prediction methods based on resistivity, density, acoustic, and gamma ray logging curves have been used to predict TOC content. The study found that the artificial neural networks (ANN) model had the strongest prediction ability, followed by the linear regression (LR) model, while the Delta logR model had the lowest prediction ability. The difference in porosity characteristics caused by organic matter content is the primary factor affecting the inversion of TOC content logging between oil shale and source rock.