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《工程(英文)》 >> 2022年 第18卷 第11期 doi: 10.1016/j.eng.2022.06.009

智能石油工程

Department of Chemical and Petroleum Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada

收稿日期: 2021-12-09 修回日期: 2022-05-01 录用日期: 2022-06-17 发布日期: 2022-07-19

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摘要

数据驱动方法和人工智能(AI)算法比基于物理的方法更有前景,前者主要来源是数据,这是每个现象的基本要素。这些算法从数据中学习并揭示看不见的模式。这项新技术对每秒产生大量数据的石油行业具有重要意义。由于石油和天然气行业正处于向油田数字化的过渡阶段,在不同的石油工程挑战中,集成数据驱动建模和机器学习(ML)算法的动力越来越大。ML已广泛应用于工业的不同领域。人们已开展大量的研究,探索AI 在该行业各个学科中的适用性。然而,这些研究缺乏两个主要特征,大多数研究要么不够实用,不适用于实际领域的挑战,要么仅限于特定问题,无法推广。必须注意数据本身及其分类和存储方式。尽管有大量来自不同学科的数据,但它们都被存储在部门的数据库中,消费者无法访问。为了从数据中获取尽可能多的信息,需要将数据存储在一个集中的数据库中,不同的应用程序可以从中方便地使用这些数据。

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