Adopting data-based approaches leads to model improvement in numerous Oil&Gas logging data processing problems. These improvements become even more sound due to new capabilities provided by deep learning. However, usage of deep learning is limited to areas where researchers possess large amounts of high-quality data. We present an approach that provides universal data representations suitable for solutions to different problems for different oil fields with little additional data. Our approach relies on the self-supervised methodology for sequential logging data for intervals from well, so it also doesn't require labelled data from the start. For validation purposes of the received representations, we consider classification and clusterization problems. We as well consider the transfer learning scenario. We found out that using the variational autoencoder leads to the most reliable and accurate models. approach We also found that a researcher only needs a tiny separate data set for the target oil field to solve a specific problem on top of universal representations.
翻译:采用基于数据的方法导致许多石油和天然气伐木数据处理问题的示范改进。这些改进由于深层学习所提供的新能力而变得更加健全。然而,深层学习的使用仅限于研究人员拥有大量高质量数据的领域。我们提出一种方法,提供通用的数据表述,适合于解决不同油田的不同问题,而没有额外数据。我们的方法依靠自监督的顺序伐木数据方法,从一开始间隔,因此它也不要求从一开始就有标签数据。为了验证所收到的表述,我们考虑分类和分类问题。我们还考虑了转移学习设想。我们发现,使用变式自动编码可导致最可靠和最准确的模式。我们还发现,一个研究人员只需要为目标石油领域制定一套微小的单独数据,以便在普遍表述之前解决一个具体问题。