Efficient AI-Physics hybrid model with productive capabilities to reduce the time of history matching and scenario assessment; a case study: Minagish oil field

IF 1.3 4区 工程技术 Q4 ENERGY & FUELS Petroleum Science and Technology Pub Date : 2024-03-08 DOI:10.1080/10916466.2024.2324818
Ali Qubian, Mohammed Ahmad Zekraoui, Sina Mohajeri, Emad Mortezazadeh, Reza Eslahi, Maryam Bakhtiari, Abrar Al Dabbous, Asma Al Sagheer, Ali Alizadeh, Mostafa Zeinali
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Abstract

This paper proposes a novel approach that combines physics-based numerical simulation with deep-learning neural networks to create an AI-Physics hybrid model for reservoir simulation. Our primary o...
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具有生产能力的高效人工智能-物理混合模型,可缩短历史匹配和情景评估时间;案例研究:米纳吉什油田
本文提出了一种新方法,将基于物理的数值模拟与深度学习神经网络相结合,为储层模拟创建人工智能-物理混合模型。我们的主要目标是...
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来源期刊
Petroleum Science and Technology
Petroleum Science and Technology 工程技术-工程:化工
CiteScore
2.90
自引率
13.30%
发文量
277
审稿时长
2.7 months
期刊介绍: The international journal of Petroleum Science and Technology publishes original, high-quality peer-reviewed research and review articles that explore: -The fundamental science of fluid-fluid and rock-fluids interactions and multi-phase interfacial and transport phenomena through porous media related to advanced petroleum recovery processes, -The application of novel concepts and processes for enhancing recovery of subsurface energy resources in a carbon-sensitive manner, -Case studies of scaling up the laboratory research findings to field pilots and field-wide applications. -Other salient technological challenges facing the petroleum industry.
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