摘要：

深水气田开发面临高水深、强耦合、高风险等复杂挑战，存在船体稳定性风险高、气藏分析难度大、监测数据获取困难、一体化生产管理复杂等技术难题。传统方法往往依赖分散的单模块仿真与人工决策，模型更新滞后、信息孤立，难以实现对气藏、管网、平台等全流程的协同优化。数字孪生技术能够突破传统方法在数据孤立、模型耦合不足和决策滞后等方面的局限，实现实时交互、闭环控制和全链条一体化协同管理，打通气藏、井筒、管网与平台间的信息壁垒，为深水气田提供协同高效的生产调控与安全保障。为此，本文以“深海一号”生产平台为研究对象，探索构建了贯通气藏—井筒—管网—平台—业财全业务链条的深水气田生产数字孪生系统。首先，系统梳理了油气生产数字孪生研究进展，分析了典型建模方法、技术体系与工程实践。其次，提出融合物理机制与数据驱动的模块化混合建模方法，并形成系统解构、模型构建、数据集成、优化求解与反馈闭环的完整建模流程。接着，面向“深海一号”实际应用场景，开发了船体系泊管理、流动保障管理、智能气藏管理、可视化管理等数字孪生模块，实现了预警监测、预测性维护、辅助决策、三维可视化和全链闭环控制。现场应用表明，该系统显著提升了平台自动化与智能化程度，将配产核算时间由4~5天缩短至1 h内，预测准确率超过90%。最后，针对当前存在的模型可迁移性差、人工干预多等问题，提出引入大模型与小模型联动、集成水下控制系统、构建覆盖全生命周期孪生系统的建议。研究成果为深水气田的智能高效开发提供了可行的技术路径与工程案例参考。

关键词: 气藏, 井筒, 管网, 人工智能, 数字孪生, 智能气田

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 interaction, 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 digital-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, model 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.

Key words: gas reservoir, wellbore, pipe network, artificial intelligence, digital twin, intelligent gas field

中图分类号: 

• TE53

• TP18