论文标题
管理O＆G机器学习模型的数据谱系：页岩用例的最佳位置
Managing Data Lineage of O&G Machine Learning Models: The Sweet Spot for Shale Use Case
论文作者
论文摘要
机器学习（ML）提高了其作用，在多个行业中变得至关重要。但是，围绕培训数据血统的问题，例如“数据集用于训练该模型的位置？”；引入了几项新的数据保护立法；而且，对数据治理要求的需求阻碍了现实世界中ML模型的采用。在本文中，我们讨论如何利用数据谱系来使ML生命周期受益，以建立ML模型，以发现页岩石油和天然气生产的甜点，这是石油和天然气O＆G行业的主要应用。
Machine Learning (ML) has increased its role, becoming essential in several industries. However, questions around training data lineage, such as "where has the dataset used to train this model come from?"; the introduction of several new data protection legislation; and, the need for data governance requirements, have hindered the adoption of ML models in the real world. In this paper, we discuss how data lineage can be leveraged to benefit the ML lifecycle to build ML models to discover sweet-spots for shale oil and gas production, a major application in the Oil and Gas O&G Industry.
