The aim of this work is to demonstrate how geologically interpretative features can improve machine learning facies classification with uncertainty assessment. Manual interpretation of lithofacies from wireline log data is traditionally performed by an expert, can be subject to biases, and is substantially laborious and time consuming for very large datasets. Characterizing the interpretational uncertainty in facies classification is quite difficult, but it can be very important for reservoir development decisions. Thus, automation of the facies classification process using machine learning is a potentially intuitive and efficient way to facilitate facies interpretation based on large-volume data. It can also enable more adequate quantification of the uncertainty in facies classification by ensuring that possible alternative lithological scenarios are not overlooked. An improvement of the performance of purely data-driven classifiers by integrating geological features and expert knowledge as additional inputs is proposed herein, with the aim of equipping the classifier with more geological insight and gaining interpretability by making it more explanatory. Support vector machine and random forest classifiers are compared to demonstrate the superiority of the latter. This study contrasts facies classification using only conventional wireline log inputs and using additional geological features. In the first experiment, geological rule-based constraints were implemented as an additional derived and constructed input. These account for key geological features that a petrophysics or geological expert would attribute to typical and identifiable wireline log responses. In the second experiment, geological interpretative features (i.e., grain size, pore size, and argillaceous content) were used as additional independent inputs to enhance the prediction accuracy and geological consistency of the classification outcomes. Input and output noise injection experiments demonstrated the robustness of the results towards systematic and random noise in the data. The aspiration of this study is to establish geological characteristics and knowledge to be considered as decisive data when used in machine learning facies classification.

中文翻译：

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地质学特征在机器学习相分类中的价值

这项工作的目的是证明地质解释特征如何通过不确定性评估改善机器学习相的分类。传统上，由电缆测井数据对岩相进行人工解释通常由专家进行，可能会产生偏差，并且对于非常大的数据集而言，这非常费力且费时。表征相分类中解释的不确定性是相当困难的，但是对于储层开发决策来说可能非常重要。因此，使用机器学习对相分类过程进行自动化是一种潜在的直观，有效的方式，可促进基于大量数据的相解释。通过确保不会忽略可能的替代岩性，它也可以对相分类的不确定性进行更充分的量化。本文提出了通过整合地质特征和专家知识作为附加输入来改进纯数据驱动的分类器的性能的目的，目的是使分类器具有更多的地质洞察力并通过使其更具解释性而获得可解释性。比较了支持向量机和随机森林分类器，以证明后者的优越性。这项研究对比了仅使用常规测井测井输入和使用其他地质特征的相分类。在第一个实验中，基于地质规则的约束被实现为附加的导出和构造输入。这些解释了岩石物理学或地质专家将归因于典型且可识别的测井测井响应的关键地质特征。在第二个实验中，将地质解释特征（即粒度，孔径和泥质含量）用作附加的独立输入，以增强分类结果的预测准确性和地质一致性。输入和输出噪声注入实验证明了结果对数据中系统性和随机性噪声的鲁棒性。这项研究的目的是要建立用于机器学习相分类的地质特征和知识，并将其视为决定性数据。在第二个实验中，将地质解释特征（即粒度，孔径和泥质含量）用作附加的独立输入，以增强分类结果的预测准确性和地质一致性。输入和输出噪声注入实验证明了结果对数据中系统性和随机性噪声的鲁棒性。这项研究的目的是要建立用于机器学习相分类的地质特征和知识，并将其视为决定性数据。在第二个实验中，将地质解释特征（即粒度，孔径和泥质含量）用作附加的独立输入，以增强分类结果的预测准确性和地质一致性。输入和输出噪声注入实验证明了结果对数据中系统性和随机性噪声的鲁棒性。这项研究的目的是要建立用于机器学习相分类的地质特征和知识，并将其视为决定性数据。输入和输出噪声注入实验证明了结果对数据中系统性和随机性噪声的鲁棒性。这项研究的目的是要建立用于机器学习相分类的地质特征和知识，并将其视为决定性数据。输入和输出噪声注入实验证明了结果对数据中系统性和随机性噪声的鲁棒性。这项研究的目的是建立用于机器学习相分类的地质特征和知识，将其视为决定性数据。