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

Anh Tuan Nguyen
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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.
VPI-Mlogs:一个基于网络的机器学习解决方案,适用于岩石物理学应用
机器学习是数据科学领域的重要组成部分。在岩石物理学中,机器学习算法和应用已经得到了广泛的研究。在此背景下,越南石油研究所(VPI)研究并部署了几种有效的预测模型,即缺失测井预测、裂缝带预测和裂缝密度预测等。作为我们的解决方案之一,VPI-MLogs是一个基于web的部署平台,它集成了数据预处理、探索性数据分析、可视化和模型执行。使用最流行的数据分析编程语言Python,该方法为用户提供了处理岩石物理测井剖面的强大工具。该解决方案有助于缩小普通知识与岩石物理学见解之间的差距。本文将重点介绍基于web的应用程序,该应用程序集成了许多解决方案来获取岩石物理数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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