Self-supervised similarity models based on well-logging data
This addresses data scarcity in oil and gas logging for researchers, though it is incremental as it builds on existing self-supervised and transfer learning methods.
The paper tackles the challenge of applying deep learning to oil and gas logging data where high-quality labeled data is scarce, by developing a self-supervised approach using variational autoencoders to create universal data representations that require only a small additional dataset for specific tasks, achieving reliable and accurate models.
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.