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Source rock evaluation plays a key role in studies of hydrocarbon accumulation and resource potential. Total organic carbon (TOC) is the basis of source rock evaluation and it is a key parameter that influences petroleum resource assessment. The Majiagou formation in the eastern Ordos Basin has complicated lithology and low abundance of organic matters. There are different opinions over the existence of scale source rocks. Due to inadequate laboratory data of TOC in the Ordos Basin, it is difficult to accurately describe source rocks in the region; thus, log interpretation of TOC is needed. In this study, the neural network model in the artificial intelligence (AI) field was introduced into the TOC logging interpretation. Compared with traditional ΔlogR methods, sample optimization, logging correlation analysis and comparative optimization of computational methods were carried out successively by using measured TOC data and logging data. Results show that the neural network model has good prediction effect in complicated lithologic regions and it can identify variations of TOC in continuous strata accurately regardless of the quick lithologic changes.

Quantitative Interpretation of TOC in Complicated Lithology Based on Well Log Data: A Case of Majiagou Formation in the Eastern Ordos Basin, China