Source rocks are fundamental elements for petroleum systems, and Total Organic Carbon (TOC) is one of the most important geochemical parameters in source rock property evaluation. The TOC determination methods using laboratory tests are expensive and limited, therefore prediction of TOC using geophysical well logs are vital for source rock characterization. Though there are various proposed TOC quantitation method, however, there still remains large uncertainty in delineation and quantitation of TOC using well log data due to the complex non-linear relationships between TOC and well log information, as well as the inherent limitations of various methods for TOC prediction. To fill the gaps between TOC and well logs, and eliminate uncertainties existing in empirical methods such as ΔlgR method, the geological, geophysical and geochemical data are integrated. History of source rock evaluation using well logs is reviewed, and sensitive well log parameters for source rocks are selected. The TOC content is correlated with well log series to unravel the well log responses of source rock intervals, and the organic matter rich intervals have high Uranium (U) concentrations and gamma ray (GR) readings, high sonic transit time (AC) and compensated neutron log (CNL), high resistivity, but low density readings. Then the various methods used for TOC quantitation are summarized in terms of their principles, interpretation process, and advantage and limitations. The Schmoker method is not applicable in shales, and borehole regularity will affect the linear regression relationship between TOC and bulk density. The Passey's ΔlgR method is widely used, however, the baseline selection will reduce the accuracy, and ΔlgR method is not applicable in highly mature or deep burial source rocks. The multiple regression analysis is hard to extend in other source rocks. The spectral GR method can hardly be used for lacustrine source rock analysis. The high acquisition costs of Nuclear Magnetic Resonance (NMR) and spectral mineral composition log (Schlumberger's Litho-Scanner logs) limit their extension in source rock evaluation. Artificial intelligence methods such as Back propagation (BP) neural network, Extreme Gradient Boosting (XGBOOST) can be used to predict TOC content via conventional logs, and the results are compared with the geochemical-measured TOC and ΔlgR method. The optimization of various methods for TOC prediction should fully consider their advantage and limitations. Additionally, comprehensive assessment of source rock should determine TOC, quality, and maturity of source rocks. This comprehensive review provides systematic and novel insights in applications of well logs in source rock evaluation, and has potential to fill gaps between geologists, geochemists and petrophysicists.

中文翻译：

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总有机碳测井预测：综合综述


烃源岩是石油系统的基本元素，总有机碳 （TOC） 是烃源岩性质评价中最重要的地球化学参数之一。使用实验室测试的 TOC 测定方法昂贵且有限，因此使用地球物理测井测井预测 TOC 对于烃源岩表征至关重要。尽管存在多种提出的 TOC 定量方法，但由于 TOC 与测井信息之间复杂的非线性关系，以及各种 TOC 预测方法的固有局限性，使用测井数据对 TOC 的描述和定量仍然存在很大的不确定性。为了填补 TOC 和测井之间的空白，并消除 ΔlgR 法等经验方法中存在的不确定性，整合了地质、地球物理和地球化学数据。回顾了使用测井评估烃源岩的历史，并选择了烃源岩的敏感测井参数。TOC 含量与测井序列相关联，以揭示烃源岩层段的测井响应，富含有机质的层段具有高铀 （U） 浓度和伽马射线 （GR） 读数、高声波传输时间 （AC） 和补偿中子测井 （CNL）、高电阻率但低密度读数。然后，从原理、解释过程、优点和局限性方面总结了用于 TOC 定量的各种方法。Schmoker 方法不适用于页岩，钻孔规则性会影响 TOC 与堆积密度之间的线性回归关系。Passey's ΔlgR 方法被广泛使用，但基线选择会降低精度，并且 ΔlgR 方法不适用于高度成熟或深埋藏烃源岩。 多元回归分析很难扩展到其他烃源岩中。光谱 GR 方法几乎不能用于湖相烃源岩分析。核磁共振 （NMR） 和光谱矿物成分测井（斯伦贝谢的 Litho-Scanner 测井）的高购置成本限制了它们在烃源岩评估中的扩展。人工智能方法，如反向传播 （BP） 神经网络、极端梯度提升 （XGBOOST） 可用于通过常规测井预测 TOC 含量，并将结果与地球化学测量的 TOC 和 ΔlgR 方法进行比较。各种TOC预测方法的优化应充分考虑其优点和局限性。此外，对烃源岩的全面评估应确定烃源岩的 TOC、质量和成熟度。本综述为测井在烃源岩评估中的应用提供了系统和新颖的见解，并有可能填补地质学家、地球化学家和岩石物理学家之间的空白。