Lithology Classification by Depositional Environment and Well Log Data Using XGBoost Algorithm

T. M. Ponjiger, S. Šešum, M. V. Naugolnov, O. Pilipenko
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引用次数: 1

Abstract

Summary The aim of this paper is to obtain an automatic lithology prediction model by using machine learning (ML) algorithms, with selected well log curves, core description data and sedimentary environment information. This model is applicable for several depositional systems for fields in Pannonian Basin and it’s locally integrated in standard software platform for petrophysicist in Company „Naftna Industrija Srbije“.
基于沉积环境和测井资料的XGBoost算法岩性分类
本文的目的是利用选定的测井曲线、岩心描述数据和沉积环境信息,利用机器学习(ML)算法获得岩性自动预测模型。该模型适用于潘诺尼亚盆地油田的多个沉积体系,并局部集成到“Naftna Industrija Srbije”公司的岩石物理学家标准软件平台中。
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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