机器学习在基于物理激励特征增强的岩相分类中的应用
Application of Machine Learning in Rock Facies Classification with Physics-Motivated Feature Augmentation
摘要：
随着算法的最新进展和大量计算能力的可用性，机器学习技术的应用正在成为石油和天然气工业中的热门话题。将机器学习应用于上游领域最有前途的方面之一是储层表征中的岩相分类，这对于确定储层的有效厚度至关重要，因此是钻井决策过程中的决定性因素。对于复杂的机器学习任务，如相分类，特征工程通常是至关重要的。本文表明，在特征增强中包含物理动机的特征交互可以进一步提高机器学习在岩相分类中的能力。我们使用SEG 2016机器学习竞赛数据集和获奖算法演示了这种方法。该改进是鲁棒的，并且可以比当前现有的最佳F-1分数好$\sim5\%$，其中F-1是用于量化平均预测准确度的评估度量。
英文摘要：
With recent progress in algorithms and the availability of massive amounts of computation power, application of machine learning techniques is becoming a hot topic in the oil and gas industry. One of the most promising aspects to apply machine learning to the upstream field is the rock facies classification in reservoir characterization, which is crucial in determining the net pay thickness of reservoirs, thus a definitive factor in drilling decision making process. For complex machine learning tasks like facies classification, feature engineering is often critical. This paper shows the inclusion of physics-motivated feature interaction in feature augmentation can further improve the capability of machine learning in rock facies classification. We demonstrate this approach with the SEG 2016 machine learning contest dataset and the top winning algorithms. The improvement is roboust and can be $\sim5\%$ better than current existing best F-1 score, where F-1 is an evaluation metric used to quantify average prediction accuracy.
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