Petrofacies classification using machine learning algorithms
Petrofacies classification using machine learning algorithms
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DOI：
10.1190/geo2019-0439.1
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发表时间：
2020-07-01
期刊：
影响因子：
3.3
通讯作者：
Ceia, Marco
中科院分区：
文献类型：
--
作者：
Silva, Adrielle A.;Tavares, Monica W.;Ceia, Marco
碳酸盐岩储层代表了世界石油和天然气储量的很大一部分，表现出对储层表征、生产和管理提出复杂挑战的特定特征。因此，由于地质非均质性，仅考虑测井数据，评价关键参数（如孔隙度、渗透率、含水饱和度和孔径分布）之间的关系是一项复杂的任务。因此，岩石物理参数是评价碳酸盐岩储层原始成分和后期地质特征的关键。储层岩相的概念是作为表征和预测储层质量的工具提出的，因为它将主要的结构分析与孔隙度、渗透率、毛细管压力、显微照片描述和其他技术的实验室测量相结合，这有助于理解成岩后事件。我们采用了巴西东南部坎波斯盆地碳酸盐岩储层岩相分类的工作流程，使用以下机器学习方法：决策树，随机森林，梯度提升，Knearest neighbors和朴素贝叶斯。该数据集包括来自两口威尔斯井（A3和A10）的1477个电缆数据，这些井已经根据岩心描述指定了岩相类别。它被分成两个子集，一个用于训练，一个用于测试训练模型分配岩相的能力。监督学习模型使用标记的训练数据来学习输入测量值与待分配岩相之间的关系。此外，我们还根据准确度、精确度、召回率和F1评分评估指标，使用测试集对模型的性能进行了比较。我们的方法已被证明是一个有价值的盟友在岩相分类，特别是分析测井数据库没有事先岩石物理信息。
Carbonate reservoirs represent a large portion of the world's oil and gas reserves, exhibiting specific characteristics that pose complex challenges to the reservoirs' characterization, production, and management. Therefore, the evaluation of the relationships between the key parameters, such as porosity, permeability, water saturation, and pore size distribution, is a complex task considering only well-log data, due to the geologic heterogeneity. Hence, the petrophysical parameters are the key to assess the original composition and posts-dimentological aspects of the carbonate reservoirs. The concept of reservoir petrofacies was proposed as a tool for the characterization and prediction of the reservoir quality as it combines primary textural analysis with laboratory measurements of porosity, permeability, capillary pressure, photomicrograph descriptions, and other techniques, which contributes to understanding the postdiagenetic events. We have adopted a workflow to petrofacies classification of a carbonate reservoir from the Campos Basin in southeastern Brazil, using the following machine learning methods: decision tree, random forest, gradient boosting, Knearest neighbors, and naive Bayes. The data set comprised 1477 wireline data from two wells (A3 and A10) that had petrofacies classes already assigned based on core descriptions. It was divided into two subsets, one for training and one for testing the capability of the trained models to assign petrofacies. The supervised-learning models have used labeled training data to learn the relationships between the input measurements and the petrofacies to be assigned. Additionally, we have developed a comparison of the models' performance using the testing set according to accuracy, precision, recall, and F1-score evaluation metrics. Our approach has proved to be a valuable ally in petrofacies classification, especially for analyzing a well-logging database with no prior petrophysical information.