石油科学通报2025，Vol.10Issue(5)：954-966,13.DOI:10.3969/j.issn.2096-1693.2025.03.023
"深海一号"智能气田生产数字孪生建设探索与实践
Exploration and practice of the digital twin construction for production in the"Deep Sea No.1"intelligent gas field
摘要
Abstract
Deepwater gas field development faces complex challenges such as ultra-deep water,strong multi-field coupling,and high operational risks,including hull stability risks,difficulties in reservoir characterization,limited accessibility of monitoring data,and the complexity of integrated production management.Traditional approaches often rely on fragmented single-module simulations and manual decision-making,resulting in delayed model updates,isolated information,and the inability to achieve end-to-end collaborative optimization across reservoirs,pipeline networks,and platforms.Digital-twin technology overcomes these limitations by breaking down data silos,enhancing model coupling,and reducing decision latency.It enables real-time in-teraction,closed-loop control,and full-chain integrated management,thereby eliminating information barriers among reservoirs,wellbores,pipelines,and platforms and providing coordinated,efficient production control and safety assurance for deepwater gas fields.Focusing on the"Deep Sea No.1"production platform,this study explores the construction of a production digi-tal-twin system that spans the entire business chain of reservoir-wellbore-pipeline network-platform-operation-finance.First,the research progress of digital twins in oil and gas production is systematically reviewed,including typical modeling methods,technical frameworks,and engineering practices.Secondly,a modular hybrid modeling approach integrating physical mechanism models and data-driven models is proposed,establishing a complete modeling workflow comprising system decomposition,mod-el construction,data integration,optimization solving,and feedback control.Third,based on the actual application scenario of the"Deep Sea No.1"platform,digital-twin modules are developed for mooring and hull management,flow assurance management,intelligent reservoir management,and 3D visualization,enabling early warning,predictive maintenance,decision support,immersive visualization,and full-chain closed-loop control.Field application results demonstrate that the system significantly improves the automation and intelligence of the platform,reducing production allocation calculation time from 4-5 days to less than 1 hour with prediction accuracy exceeding 90%.Finally,in response to current issues such as limited model transferability and heavy manual intervention,this paper suggests establishing a linkage framework of large and small models,strengthening integration with subsea control systems,and building a full-lifecycle digital-twin system.The research results provide a feasible technical pathway and engineering reference for the intelligent and efficient development of deepwater gas fields.关键词
气藏/井筒/管网/人工智能/数字孪生/智能气田Key words
gas reservoir/wellbore/pipe network/artificial intelligence/digital twin/intelligent gas field分类
能源科技引用本文复制引用
曾楠诺,李力,李劲松,林伯韬,金衍,李靖,王玮,殷启帅,朱海涛.."深海一号"智能气田生产数字孪生建设探索与实践[J].石油科学通报,2025,10(5):954-966,13.基金项目
中国海洋石油集团有限公司"深海一号"智能气田建设项目资助 （）
