论文标题
基于良好数据的自我监督相似性模型
Self-supervised similarity models based on well-logging data
论文作者
论文摘要
采用基于数据的方法会导致许多石油和天然气记录数据处理问题的模型改进。由于深度学习提供的新功能，这些改进变得更加合理。但是，深度学习的使用仅限于研究人员拥有大量高质量数据的领域。我们提出了一种提供通用数据表示的方法，适用于针对不同油田的不同问题的解决方案，而其他数据很少。我们的方法依赖于从井的间隔内进行连续记录数据的自我监督方法，因此从一开始就不需要标记的数据。为了验证收到的表示形式，我们考虑分类和聚类问题。我们也考虑转移学习方案。我们发现，使用变异自动编码器会导致最可靠，最准确的模型。方法我们还发现，研究人员只需要一个针对目标油田的微小单独的数据集即可在通用表示之上解决特定问题。
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.
