This work analyzes the utilization of classification models in the context of the oil industry and presents examples of application. Particularly, we analyze three case studies, two to explain the behavior of oil wells that produce via artificial methods (the classification as a descriptive model), and another to predict the oil prices (the classification as a predictive model). The classification technique used in this work is LAMDA-HAD, which is an improvement to the well-known technique called learning algorithm multivariable and data analysis (LAMDA), that has been used in diagnostic tasks. Finally, the results with the descriptive and predictive models are discussed, in order to analyze the importance of the classification in the context of the oil business.

中文翻译：

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LAMDA 在石油生产中作为分类模型的适用性

这项工作分析了石油工业背景下分类模型的使用，并提供了应用示例。特别地，我们分析了三个案例研究，两个用于解释通过人工方法生产的油井的行为（分类为描述模型），另一个用于预测油价（分类为预测模型）。这项工作中使用的分类技术是 LAMDA-HAD，它是对称为学习算法多变量和数据分析 (LAMDA) 的众所周知的技术的改进，该技术已用于诊断任务。最后，讨论了描述性和预测性模型的结果，以分析分类在石油业务背景下的重要性。