Field-Scale Automatic Facies Classification Using Machine Learning Algorithms

A. Kuvichko, P. Spesivtsev, V. Zyuzin, S. Istomin, Alexey Kalistratov, M. Kuznetsov, S. Igitov
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引用次数: 2

Abstract

We propose a novel approach to facies classification based on a supervised machine learning algorithm. This approach allows for the automatic facies classification on a field scale based on an ensemble of Decision Trees algorithm associated with gradient boosting. Major steps of the workflow include data integrity assessment, data scaling, identification and correction of gaps in data, log processing, feature engineering, training, testing, and tuning the hyperparameters on the validated set of data. At the ultimate stage of the workflow, the algorithm accepts a set of well logs as an input and produces a discrete facies type as an output. This method substantially increases the quality of the facies classification, that is key to further geological modelling and dynamic simulation that help reduce drastically the risk of incorrect well planning, fracturing and other operations, thus avoiding a huge negative financial impact. The novelty of approach is related to the selection of machine learning algorithms that are best fitting the dataset, combined with a workflow to enhance the dataset itself.
使用机器学习算法的现场尺度自动相分类
我们提出了一种基于监督机器学习算法的相分类新方法。该方法允许基于与梯度提升相关的决策树算法的集成,在现场尺度上自动进行相分类。工作流的主要步骤包括数据完整性评估、数据缩放、识别和纠正数据中的差距、日志处理、特征工程、训练、测试和调优验证数据集上的超参数。在工作流程的最后阶段,该算法接受一组测井数据作为输入,并产生一个离散相类型作为输出。这种方法大大提高了相分类的质量,这是进一步地质建模和动态模拟的关键,有助于大大降低错误的井规划、压裂和其他操作的风险,从而避免巨大的负面财务影响。该方法的新颖性与选择最适合数据集的机器学习算法有关,并结合工作流来增强数据集本身。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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