Kharazi Esfahani Parsa, Mahdavi Basir Hadi, Rabbani Ahmad Reza
Department of Petroleum Engineering, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Avenue, Box 15875-4413, Tehran, 1591634311, Iran.
Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Avenue, Box 15875-4413, Tehran, 1591634311, Iran.
Sci Rep. 2024 Sep 2;14(1):20366. doi: 10.1038/s41598-024-71521-0.
Vitrinite reflectance (VR) is a critical measure of source rock maturity in geochemistry. Although VR is a widely accepted measure of maturity, its accurate measurement often proves challenging and costly. Rock-Eval pyrolysis offers the advantages of being cost-effective, fast, and providing accurate data. Previous studies have employed empirical equations and traditional machine learning methods using T-max data for VR prediction, but these approaches often yielded subpar results. Therefore, the quest to develop a precise method for predicting vitrinite reflectance based on Rock-Eval data becomes particularly valuable. This study presents a novel approach to predicting VR using advanced machine learning models, namely ExtraTree and XGBoost, along with new ways to prepare the data, such as winsorization for outlier treatment and principal component analysis (PCA) for dimensionality reduction. The depth and three Rock-Eval parameters (T-max, S1/TOC, and HI) were used as input variables. Three model sets were examined: Set 1, which involved both Winsorization and PCA; Set 2, which only included Winsorization; and Set 3, which did not include either. The results indicate that the ExtraTree model in Set 1 demonstrated the highest level of predictive accuracy, whereas Set 3 exhibited the lowest level of accuracy, confirming the methodology's effectiveness. The ExtraTree model obtained an overall R2 score of 0.997, surpassing traditional methods by a significant margin. This approach improves the accuracy and dependability of virtual reality predictions, showing significant advancements compared to conventional empirical equations and traditional machine learning methods.
镜质体反射率（VR）是地球化学中衡量烃源岩成熟度的关键指标。尽管VR是一种被广泛接受的成熟度衡量指标，但其准确测量往往具有挑战性且成本高昂。岩石热解分析具有成本效益高、速度快且能提供准确数据的优点。以往的研究采用经验方程和使用T-max数据的传统机器学习方法来预测VR，但这些方法往往效果不佳。因此，开发一种基于岩石热解分析数据预测镜质体反射率的精确方法变得尤为重要。本研究提出了一种使用先进机器学习模型（即ExtraTree和XGBoost）预测VR的新方法，以及数据预处理的新方法，如采用 Winsorization 处理异常值和主成分分析（PCA）进行降维。深度和三个岩石热解参数（T-max、S1/TOC和HI）用作输入变量。研究了三组模型：第一组同时涉及Winsorization和PCA；第二组仅包括Winsorization；第三组两者都不包括。结果表明，第一组中的ExtraTree模型表现出最高的预测精度，而第三组的精度最低，证实了该方法的有效性。ExtraTree模型的整体R2得分达到0.997，大大超过了传统方法。这种方法提高了虚拟现实预测的准确性和可靠性，与传统经验方程和传统机器学习方法相比有了显著进步。
