Python Dash for Well Data Validation, Visualization, and Processing

Abstract

Open-source Python libraries play a critical role in facilitating the digital transformation of the energy industry by enabling quick deployment of intelligent data-driven solutions. In this paper, we demonstrate an example of using Dash, a Python framework introduced by Plotly for creating interactive web applications. A fit-for-purpose software was tailored for an in-house research project in well-data validation, visualization, and processing. The application automates quality control of different sets of well-log data files (DLIS/LIS or LAS) for completeness, validity, and repeatability. For this tedious and critical process, a human expert is required to perform tasks using well-log interpretation software. A typical digital log file may contain hundreds or thousands of data channels that are difficult are difficult to visualize and validate manually. Sometimes it takes multiple iterations of communication between the data provider and the data receiver to achieve a final valid deliverable copy. By utilizing open-source Python libraries, such as DLISIO (Equinor ASA, 2022) and LASIO (Inverarity, 2023), a web interface based on Plotly-Dash is developed to visualize and check all data channels automatically and then produce a compliance summary report in PDF or HTML format. The time for validating one DLIS file that has hundreds of data channels is significantly reduced. Implementation of this automated data quality control workflow demonstrates that open-source Python libraries can significantly reduce the time from development to the deployment cycle. Quick implementation of intelligent software based on Python Plotly-Dash enables customized solutions or workflows that further improve both the effectiveness and efficiency of routine data quality control processes.