Accurately estimating total organic carbon (TOC) from suites of well logs is essential as it is too costly and time consuming to take direct measurements from core samples in many wells. Unfortunately, the several methods developed over recent decades, based on various correlations and correlation-based machine learning methods, do not provide universally reliable, accurate or easily auditable TOC predictions. A method is developed and its viability evaluated exploiting a promising correlation-free, data-matching routine. This is applied to published well-log curves, with supporting mineralogical data and measured TOC, for two wells penetrating the Lower Barnett Shale formation at distinct settings within the Fort Worth Basin (Texas, U.S.). The method combines between 5 and 10 well log features and evaluates, on a supervised learning basis, multiple cases for nine distinct models at data- record-sampling densities ranging from one record for every 0.5 ft to one record for every 0.04 ft. At zoomed-in sampling densities the model achieves TOC prediction accuracies for the models combining data from both wells of (RMSE ≤ 0.3% and R2 ≥ 0.955) for models involving 6 and 10 input variables. It is the models involving six input variables that have the potential to be applied in unsupervised circumstances to predict TOC in surrounding wells lacking measured TOC, but that potential requires confirmation in future multi-well studies.

中文翻译：

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在不同采样密度下应用优化的数据匹配算法从下巴尼特页岩测井数据预测总有机碳

从一系列测井中准确估算总有机碳 (TOC) 至关重要，因为从许多井中的岩心样品中直接测量成本太高且耗时太长。不幸的是，近几十年来开发的几种方法，基于各种相关性和基于相关性的机器学习方法，并不能提供普遍可靠、准确或易于审核的 TOC 预测。开发了一种方法，并利用有前途的无相关性数据匹配例程来评估其可行性。这适用于已发布的测井曲线，以及支持矿物学数据和测量的 TOC，对于在沃思堡盆地（美国德克萨斯州）内的不同环境下穿透下巴尼特页岩地层的两口井。该方法结合了 5 到 10 个测井特征，并在监督学习的基础上评估，九个不同模型的多个案例，数据记录采样密度从每 0.5 英尺一条记录到每 0.04 英尺一条记录。在放大的采样密度下，该模型实现了模型的 TOC 预测精度，这些模型结合了来自两个井的数据（RMSE ≤ 0.3% 和 R2 ≥ 0.955）适用于包含 6 个和 10 个输入变量的模型。涉及六个输入变量的模型有可能在无人监督的情况下应用于预测缺乏测量 TOC 的周围井中的 TOC，但这种潜力需要在未来的多井研究中得到证实。在放大的采样密度下，对于包含 6 个和 10 个输入变量的模型，该模型结合了来自 (RMSE ≤ 0.3% 和 R2 ≥ 0.955) 两口井的数据的模型实现了 TOC 预测精度。涉及六个输入变量的模型有可能在无人监督的情况下应用于预测缺乏测量 TOC 的周围井中的 TOC，但这种潜力需要在未来的多井研究中得到证实。在放大的采样密度下，对于包含 6 个和 10 个输入变量的模型，该模型结合了来自 (RMSE ≤ 0.3% 和 R2 ≥ 0.955) 两口井的数据的模型实现了 TOC 预测精度。涉及六个输入变量的模型有可能在无人监督的情况下应用于预测缺乏测量 TOC 的周围井中的 TOC，但这种潜力需要在未来的多井研究中得到证实。