输油管道调控运行智能化探索与实践

Exploration and practice of intelligent control and operation of oil pipelines

  • 摘要:
    目的 随着中国能源需求持续增长,输油管道运营里程显著增加,管道系统的复杂性与调控难度也随之急剧上升,传统以人为核心的调控模式暴露出工艺方案受人员能力限制、工况识别准确性不足、应急决策能力薄弱等问题,制约了管道安全高效运行。为解决上述瓶颈,亟需构建一套覆盖全链路调控业务的智能调控系统,实现调控业务由“人控”向“机控”的转变,从根本上提升管道运行安全与效率。
    方法 归纳总结输油管道调控工作,提出以“感知、认知、决策、执行”为核心的全链路闭环业务流程。基于“机控为核、全域智控、常态自主”的智能调控新范式,融合大模型、机理模型、深度学习、决策推理及自动化指令集等关键技术,开发出涵盖数据采集与实时感知、运行方案编制与下发、运行工况识别与分析、自动决策与处置执行4大功能模块的智能调控系统,实现调控业务流程智能化,并在典型原油管道开展试点应用。
    结果 通过构建智能调控系统,实现业务流程智能化,有效推动调控模式从“人控”向“机控”转变。通过在某原油管道对该智能调控系统进行试点应用,成功验证了系统在缩短运行方案编制时长、提升工况识别准确性与及时性、减少设备操作频率和提高应急处置能力等方面的有效性,显著提升调控效率与安全性,全面化解调控运行的突出挑战。
    结论 研究形成了输油管道智能调控解决方案,为油气管道实现智能调控提供了可复制的实施框架。未来,随着人工智能技术持续演进、油气数据治理不断深化、工控系统逐步优化,输油管道智能调控技术将持续迭代并推广至各类油气管网,推动油气管网进入全面智能调控新阶段。

     

    Abstract:
    Objective With China’s rising energy demand, oil pipeline mileage and system complexity have increased significantly, posing control challenges. Traditional human-centered control suffers from reliance on personnel skills, limited accuracy in identifying operating conditions, and weak emergency decision-making, hindering pipeline safety and efficiency. To overcome these limitations, an intelligent control system spanning the entire business process is urgently needed to transition from “human control” to “machine control” and fundamentally enhance pipeline safety and efficiency.
    Methods The oil pipeline control work was summarized, and a closed-loop business process centered on “perception, cognition, decision-making, and execution” was proposed. Based on the intelligent control paradigm emphasizing “machine control as core, full-domain intelligent control, and normal-state autonomy”, key technologies including large models, mechanism models, deep learning, decision-making reasoning, and automated instruction sets were integrated. An intelligent control system comprising four functional modules—data collection and real-time perception, operation plan compilation and issuance, operating condition identification and analysis, and automatic decision-making and disposal execution—was developed. The intelligence of the control process was realized, and a pilot application was conducted on a typical crude oil pipeline.
    Results The intelligent control system was developed to automate the business process, effectively enabling the shift from “human control” to “machine control”. Its pilot application on a crude oil pipeline demonstrated significant reduction in operation plan compilation time, improved accuracy and timeliness in operating condition identification, decreased equipment operation frequency, and enhanced emergency response capabilities. Consequently, control efficiency and safety were markedly improved, and key operational challenges were comprehensively addressed.
    Conclusion The solution proposed for the intelligent control of oil pipelines provides a replicable framework for oil and gas pipelines. In the future, with the ongoing evolution of artificial intelligence, enhanced data governance, and the optimization of industrial control systems, the intelligent control technology will keep evolving and be promoted across various oil and gas pipeline networks, ushering in a new era of comprehensive intelligent control.

     

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