Getting started with acoustic well log data using the dlisio Python library on the Volve Data Village dataset
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Three issues have long impeded academic research and teaching on well logging. First, real measured data has been hard to come by. This has now been alleviated by Equinor's 2018 release of the Volve Data Village dataset. Among its 5 TB of data, it contains 16.3 GB of various well log data, plots, and analyses. Second, no free and effective software tools to programmatically read DLIS files, one of the most common file formats for well log data today and by far the most common format in the Volve Data Village, have been available. This has now been remedied by the free and open-source Python library dlisio, first released by Equinor in 2018 and still under heavy development. Third, the data is often difficult to understand, as sufficient documentation is often not publicly available. As different tools measure, process, and store their data differently, different tools must be understood individually. This article aims to stimulate research into well logging, by showing how to use dlisio to investigate well log data from the Volve Data Village dataset. While the investigative methods used here can be adapted to other kinds of data, this article focuses on acoustic integrity logs. Specifically, we investigate data from a sonic tool (DSLT) and an ultrasonic tool (USIT), both extensively used in the dataset. In addition to identifying what the most fundamental pieces of data represent, we also show some simple examples of how this data can be reprocessed to find new results not provided in the well log file. We provide the code underlying this article in an accompanying Jupyter Notebook.