VPI-MLogs - Web-based machine learning solution for petrophysics - summary and application overview

Aug 3, 2026

VPI-MLogs is a web-based deployment platform developed to support petrophysical log interpretation using machine learning. It integrates data preprocessing, exploratory data analysis, visualization, and model execution to perform tasks such as missing log prediction and fracture zone or fracture density forecasting. Built with Python and interactive visualization components, the system helps users process LAS log data, remove outliers, and obtain actionable model results through an accessible dashboard interface. The approach narrows the gap between general knowledge and domain-specific petrophysics insights.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

AccessFree to download

Introduction

Interactive visualisation and web deployment

Recent work and new approach

Limitations of local professional software

Advantages of web-based execution

Research method

Python ecosystem and machine learning tooling

Interactive visuals

PETROLEUM TECHNOLOGIES PETROVIETNAM JOURNAL Volume 10/2022, pp. 46-52 ISSN 2615-9902 VPI-MLOGS: AWEB-BASED MACHINE LEARNING SOLUTION FOR APPLICATIONS IN PETROPHYSICS Nguyen Anh Tuan Vietnam Petroleum Institute Email: tuan.a.nguyen@vpi.pvn.vn [https://doi.org/10.47800/PVJ.2022.10-06](https://doi.org/10.47800/PVJ.2022.10-06) Summary 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 webbased 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. Key words: Petrophysics, outliers removing, log prediction, interactive visualisation, web application, VPI-MLogs. 1. Introduction Understanding data is a crucial step in any aspect of technological fields and research domains. In data science, clearly and precisely understanding data always requires time. In the petroleum field, petrophysics data has several unique features that require users to have not only domain knowledge but also specialised software to deal with data problems. The most notable programming languages (such as Python) give developers tools to address issues and validate data without any special softwares or payments. In addition, some valuable functions could be designed to fit the user’s machine learning requirements such as data processing, data cleaning, exploratory data analysis and model deployment. The dashboard is basically fulfilled by charts, model results, and data insights. For example, Power BI and Tableau take a lot of advantages by their powerful organised abilities. However, because of their limited modification, several innovative ideas cannot be presented. Alternatively, many Python libraries appeared to support presentation and graphic user interface functions. [Streamlit.io](Streamlit.io) is one of these answers, combined with interactive visualisation by Altair library helping improve display features and data exploration. In the end, a solution integrating interactive visualsand web applications has completely erected to deal with petrophysical log data which include several steps from data preprocessing (LAS files loading and re-organising, EDA, outliers removal, etc.) to model deployment (missing log forecast or fracture prediction). A web-based application is also more friendly than rigid coding lines. 2. Recent work and new approach Traditionally, most of petrophysical tasks require custom software such as Petrel, Techlog (Schlumberger), IP Interactive Petrophysics (LIoyd’s Register)... During log interpretation, interactive function is performed beside advanced operations to provide information for exploration progress. On the other hand, recently, machine learning algorithms have become more and more popular and embedded in almost all industrial sectors. However, updating the latest technology always faces many restrictions, especially in financial aspect. From the user's perspective, VPI's team has researched and experienced applications of machine learning to address missing log data or erect fracture predictive models. 46 PETROVIETNAM-JOURNAL VOL 10/2022 In operation perspective, professional software runs locally in user’s devices. It always requires a computer with high performance, and in some cases, it needs a workstation. This traditional approa

FAQs

What problems does VPI-MLogs target in petrophysics?

It targets petrophysical log analytics tasks such as missing log prediction and forecasting fracture zone or fracture density. The platform is designed to turn petrophysical insights into practical model outputs.

How does VPI-MLogs work from input data to results?

Users upload their log data to the application host. The workflow includes LAS loading and re-organization, exploratory data analysis, outlier removal, and then model execution to return predictions and insights.

Why is a web-based application preferred over traditional local software?

The web-based approach improves execution convenience by enabling access online and running on medium performance computation. It avoids limitations such as high cost and rigid local deployment compared with workstation-based tools.

Aug 3, 2026

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