Macêdo Bruno da Silva, Wayo Dennis Delali Kwesi, Campos Deivid, De Santis Rodrigo Barbosa, Martinho Alfeu Dias, Yaseen Zaher Mundher, Saporetti Camila Martins, Goliatt Leonardo
Department of Computer Science, Federal University of Lavras, Lavras, MG, 37200-000, Brazil.
Faculty of Chemical and Process Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26300, Kuantan, Malaysia.
Sci Rep. 2025 Mar 27;15(1):10658. doi: 10.1038/s41598-025-91224-4.
An accurate assessment of shale gas resources is highly important for the sustainable development of these energy resources. Total organic carbon (TOC) analysis thus becomes fundamental for understanding the distribution and quality of hydrocarbon source rocks within a shale gas reservoir. The elevation of the TOC is often associated with the presence of source rocks, indicating the potential for oil and gas production. TOC assessment is performed using laboratory methods, which can be time-consuming and costly. Data-driven models have been successfully applied to model the relationship between TOC and other constituents and to predict the TOC content. However, these methods depend on extensive parameter adjustments that must be carefully conducted in different sedimentary environments. In this context, Automated Machine Learning (AutoML) is an alternative for accurately predicting TOCs, saving time-consuming fine-tuning steps in model development. This study aims to develop an AutoML strategy for estimating TOC using well log data. This procedure automatically preprocesses the search for the best method parameters, reducing the execution time. Among the methods evaluated, Extremely Randomized Trees (XT) performed best (R = 0.8632, MSE = 0.1806) in the test set. The proposed strategy provides a powerful data-driven method, which allows real-world use of the well to assist in data analysis and subsequent decision-making.
准确评估页岩气资源对这些能源的可持续发展至关重要。因此，总有机碳（TOC）分析成为了解页岩气藏内烃源岩分布和质量的基础。TOC的升高通常与烃源岩的存在相关，表明油气生产的潜力。TOC评估采用实验室方法进行，这可能既耗时又昂贵。数据驱动模型已成功应用于模拟TOC与其他成分之间的关系并预测TOC含量。然而，这些方法依赖于必须在不同沉积环境中仔细进行的大量参数调整。在这种情况下，自动机器学习（AutoML）是准确预测TOC的一种替代方法，可节省模型开发中耗时的微调步骤。本研究旨在开发一种使用测井数据估算TOC的AutoML策略。该过程会自动预处理以寻找最佳方法参数，从而减少执行时间。在所评估的方法中，极端随机树（XT）在测试集中表现最佳（R = 0.8632，MSE = 0.1806）。所提出的策略提供了一种强大的数据驱动方法，可在实际中利用测井数据协助数据分析和后续决策。
