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

TL;DR

VPI-MLogs is a web-based platform that integrates data analysis and machine learning tools to enhance petrophysical log interpretation and prediction, making complex data more accessible and insightful for users.

Contribution

The paper introduces VPI-MLogs, a novel web-based platform that combines data preprocessing, visualization, and machine learning for petrophysics applications.

Findings

Effective prediction models for missing logs and fracture zones.

Integration of data analysis and visualization tools.

Facilitates better understanding of petrophysical data.

Abstract

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.