VPI-Mlogs: A web-based machine learning solution for applications in petrophysics

  • Nguyen Anh Tuan Vietnam Petroleum Institute
Keywords: Petrophysics, outliers removing, log prediction, interactive visualisation, web application, VPI-MLogs


Machine learning is an important part of the data science field. In petrophysics, machine learning algorithms and applications have been widely approached. In this context, Vietnam Petroleum Institute (VPI) has researched and deployed several effective prediction models, namely missing log prediction, fracture zone and fracture density forecast, etc. As one of our solutions, VPI-MLogs is a web-based deployment platform which integrates data preprocessing, exploratory data analysis, visualisation and model execution. Using the most popular data analysis programming language, Python, this approach gives users a powerful tool to deal with the petrophysical logs section. The solution helps to narrow the gap between common knowledge and petrophysics insights. This article will focus on the web-based application which integrates many solutions to grasp petrophysical data.


Jacob VanderPlas, Brian E. Granger, Jeffrey Heer, Dominik Moritz, Kanit Wongsuphasawat, Arvind Satyanarayan, Eitan Lees, Ilia Timofeev, Ben Welsh, and Scott Sievert, "Altair interactive statistical visualizations", Journal of Open Source Software, Vol. 3, No. 32, 2018. DOI: 10.21105/joss.01057.

Mohammad Khorasani, Mohamed Abdou, and Javier Hernández Fernández, Web application development with streamlit: Develop and deploy secure and scalable web applications to the cloud using a pure Python framework. Apress, 2022.

Suresh Kumar Mukhiya and Usman Ahmed, Hands-on exploratory data analysis with Python. Packt Publishing, 2020.

Pramod Singh, Deploy machine learning models to production: With Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform. Apress, 2021.

How to Cite
Nguyen, A. T. (2022). VPI-Mlogs: A web-based machine learning solution for applications in petrophysics. Petrovietnam Journal, 10, 46 - 52. https://doi.org/10.47800/PVJ.2022.10-06