Department of Petroleum Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Box: 5049, Saudi Arabia.
Comput Intell Neurosci. 2021 Aug 30;2021:7390055. doi: 10.1155/2021/7390055. eCollection 2021.
Unconventional resources have recently gained a lot of attention, and as a consequence, there has been an increase in research interest in predicting total organic carbon (TOC) as a crucial quality indicator. TOC is commonly measured experimentally; however, due to sampling restrictions, obtaining continuous data on TOC is difficult. Therefore, different empirical correlations for TOC have been presented. However, there are concerns about the generalization and accuracy of these correlations. In this paper, different machine learning (ML) techniques were utilized to develop models that predict TOC from well logs, including formation resistivity (FR), spontaneous potential (SP), sonic transit time (Δ), bulk density (RHOB), neutron porosity (CNP), gamma ray (GR), and spectrum logs of thorium (Th), uranium (Ur), and potassium (K). Over 1250 data points from the Devonian Duvernay shale were utilized to create and validate the model. These datasets were obtained from three wells; the first was used to train the models, while the data sets from the other two wells were utilized to test and validate them. Support vector machine (SVM), random forest (RF), and decision tree (DT) were the ML approaches tested, and their predictions were contrasted with three empirical correlations. Various AI methods' parameters were tested to assure the best possible accuracy in terms of correlation coefficient () and average absolute percentage error (AAPE) between the actual and predicted TOC. The three ML methods yielded good matches; however, the RF-based model has the best performance. The RF model was able to predict the TOC for the different datasets with values range between 0.93 and 0.99 and AAPE values less than 14%. In terms of average error, the ML-based models outperformed the other three empirical correlations. This study shows the capability and robustness of ML models to predict the total organic carbon from readily available logging data without the need for core analysis or additional well interventions.
非常规资源最近受到了广泛关注，因此，人们对预测总有机碳（TOC）作为关键质量指标的研究兴趣也有所增加。TOC 通常通过实验测量；然而，由于采样限制，很难获得 TOC 的连续数据。因此，已经提出了不同的 TOC 经验相关性。然而，人们对这些相关性的泛化和准确性存在担忧。在本文中，利用不同的机器学习（ML）技术来开发从测井中预测 TOC 的模型，包括地层电阻率（FR）、自然电位（SP）、声波时差（Δ）、体密度（RHOB）、中子孔隙度（CNP）、伽马射线（GR）和钍（Th）、铀（Ur）和钾（K）的谱测井。利用来自泥盆纪 Duvernay 页岩的超过 1250 个数据点来创建和验证模型。这些数据集来自三口井；第一口井用于训练模型，而其他两口井的数据用于测试和验证模型。测试了支持向量机（SVM）、随机森林（RF）和决策树（DT）等 ML 方法，并将它们的预测与三个经验相关性进行了对比。测试了各种 AI 方法的参数，以确保在相关系数（）和实际与预测 TOC 之间的平均绝对百分比误差（AAPE）方面具有最佳的准确性。三种 ML 方法都得到了很好的匹配；然而，基于 RF 的模型具有最佳性能。RF 模型能够预测不同数据集的 TOC，相关系数（）范围在 0.93 到 0.99 之间，平均绝对百分比误差（AAPE）小于 14%。就平均误差而言，基于 ML 的模型优于其他三个经验相关性。本研究表明，ML 模型能够从易于获得的测井数据中预测总有机碳，而无需进行岩心分析或额外的井干预，具有很强的能力和稳健性。
