VPI-MLogs用网页平台整合测井数据分析全流程。
VPI-Mlogs: A web-based machine learning solution for applications in petrophysics
- 基于Python构建网页平台，集成数据预处理与可视化
- 支持缺失测井数据、裂缝带及密度预测等应用
- 降低非专业人员使用机器学习的门槛
机器学习是数据科学的重要组成部分。在测井学领域，机器学习算法和应用已得到广泛应用。越南石油研究所（VPI）研发并部署了多项有效预测模型，包括缺失测井曲线预测、裂缝带与裂缝密度预测等。作为其中一项解决方案，VPI-MLogs是一个基于网页的部署平台，集成了数据预处理、探索性数据分析、可视化和模型执行功能。该平台采用主流的数据分析编程语言Python，为用户提供处理测井数据的强大工具，有助于缩小普通知识与测井学洞察之间的差距。本文聚焦于这一整合多种解决方案的网页应用，以实现对测井数据的高效分析。
原文摘要 · Abstract (English)
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.
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