Abstract Borehole lithology discrimination is the foundation of formation evaluation and reservoir characterization. Due to the limitation of costing or accuracy, direct discrimination methods such as borehole core and drilling cutting analysis are unable to be deployed to every well, while logging lithology interpretation provides an alternative solution for this. Recently, several machine learning algorithms such as the neural network, support vector machine, decision tree, and random forest have already been employed by researchers for automatic logging lithology interpretation. However, the vast majority of these studies belong to the category of deterministic lithology characterization. In this article, we propose a probability based fuzzy characterization method for more effective logging lithology interpretation. Moreover, to improve the accuracy of lithology probability estimation, we propose the probabilistic random forest algorithm and investigate its advantages referred to 8 existing probability estimation algorithms. Through the comparative experiments on 9 real-world logging lithology interpretation tasks, the feasibility and advantage of the proposed method are confirmed. Application case demonstrates that compared with traditional deterministic lithology characterization methods, probabilistic lithology characterization is able to provide more information about rhythm, heterogeneity, and formation properties, which worths further application and promotion to improve the fineness of formation evaluation and reservoir characterization.

中文翻译：

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随机森林概率估计的概率测井岩性表征

摘要 井眼岩性判别是地层评价和储层表征的基础。由于成本或精度的限制，钻孔岩心和钻屑分析等直接判别方法无法应用于每一口井，而测井岩性解释为此提供了替代解决方案。最近，研究人员已经采用神经网络、支持向量机、决策树和随机森林等几种机器学习算法进行自动测井岩性解释。然而，这些研究中的绝大多数属于确定性岩性特征的范畴。在本文中，我们提出了一种基于概率的模糊表征方法，以实现更有效的测井岩性解释。而且，为了提高岩性概率估计的准确性，我们提出了概率随机森林算法，并参考现有的8种概率估计算法研究了其优点。通过9个真实世界测井岩性解释任务的对比实验，证实了该方法的可行性和优势。应用案例表明，与传统的确定性岩性表征方法相比，概率岩性表征能够提供更多的韵律、非均质性和地层性质信息，值得进一步应用和推广，以提高地层评价和储层表征的精细度。我们提出了概率随机森林算法，并参考了现有的 8 种概率估计算法研究了其优点。通过9个真实世界测井岩性解释任务的对比实验，证实了该方法的可行性和优势。应用案例表明，与传统的确定性岩性表征方法相比，概率岩性表征能够提供更多的韵律、非均质性和地层性质信息，值得进一步应用和推广，以提高地层评价和储层表征的精细度。我们提出了概率随机森林算法，并参考了现有的 8 种概率估计算法研究了其优点。通过9个真实世界测井岩性解释任务的对比实验，证实了该方法的可行性和优势。应用案例表明，与传统的确定性岩性表征方法相比，概率岩性表征能够提供更多的韵律、非均质性和地层性质信息，值得进一步应用和推广，以提高地层评价和储层表征的精细度。证实了所提出方法的可行性和优势。应用案例表明，与传统的确定性岩性表征方法相比，概率岩性表征能够提供更多的韵律、非均质性和地层性质信息，值得进一步应用和推广，以提高地层评价和储层表征的精细度。证实了所提出方法的可行性和优势。应用案例表明，与传统的确定性岩性表征方法相比，概率岩性表征能够提供更多的韵律、非均质性和地层性质信息，值得进一步应用和推广，以提高地层评价和储层表征的精细度。