Assisting Production Decision Technology in Gas Storage Operation Based on Digital Twin Technologies

计算机科学 数据访问层 可视化 图层(电子) 过程(计算) 点云 实时计算 数据建模 数据挖掘 数据库 人工智能 化学 有机化学 操作系统
作者
Wang Peixian,Zunzhao Li,Xiaolin Wang,Gang Wang,Ming‐Yi Li,Shizhe Yao
出处
期刊:SPE Annual Technical Conference and Exhibition 被引量:6
标识
DOI:10.2118/210056-ms
摘要

Abstract Underground gas storage (UGS) is a method to ensure safe and stable supply of natural gas in China. The digital twin technologies, also known as Industry 4.0 technologies, play a more and more important role in digital and intelligent development of UGS. Based on digital twin technologies, the assisting production decision technology which contains data layer, model layer, logic layer, interaction layer, is constructed. The data layer covers data from SCADA, equipment detection, GIS, etc. Using laser point cloud scanning, oblique photography, and 3d design software, the 3D model in the model layer achieves one-to-one reduction of the ground process of UGS. The simulation model in the model layer integrates reservoir or reservoir boundary, wellbore, pipeline, ground process simulation model, which can realize the whole process simulation of UGS. The logic layer takes the data information stored in the data layer as input, and adopts programming language or algorithm model to analyze and evaluate the process and equipment of UGS. The interaction layer is the visual window of the system, which can realize the visualization and scenario-based interaction of data. The assisting production decision technology fuses historical data, real-time data, and predicted data to track the past, monitor the present, and predict the future. Firstly, it integrates all the needed data with 3D models visually. Secondly, the total elements information perception technology, augmented reality technology, three-dimensional visualization monitoring technology, etc. are integrated to observe and study gas storage. Last but not least, the high fidelity model of gas storage digital twin also helps understand situation, monitor equipment energy consumption, and make optimization decision. Integrating digital twin technologies into intelligent and digital management of gas storage, so as to facilitate the personalized management of gas storage.
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