网站版权 © 《油气储运》编辑部 陕ICP备11014090号-10
地址：河北省廊坊市金光道51号（065000）；电话：0316-2176193 / 0316-2072055； 传真:0316-2177392； 网址：yqcy.paperonce.org
技术支持：西安三才科技实业有限公司 029-89381847；029-88222991
[1]宫敬,沈思亨,刘大千,等.逻辑脑与关联脑协同创新在油气集输系统场景中的应用[J].油气储运,2025,44(05):1-17.
GONG Jing,SHEN Siheng,LIU Daqian,et al.Synergistic application of logical brain and associative brain in oil-gas gathering and transportation systems[J].Oil & Gas Storage and Transportation,2025,44(05):1-17.
点击复制
GONG Jing,SHEN Siheng,LIU Daqian,et al.Synergistic application of logical brain and associative brain in oil-gas gathering and transportation systems[J].Oil & Gas Storage and Transportation,2025,44(05):1-17.
逻辑脑与关联脑协同创新在油气集输系统场景中的应用
《油气储运》[ISSN:1000-8241/CN:13-1093/TE]
卷:
44
期数:
2025年05期
页码:
1-17
栏目:
出版日期:
2025-05-25
- Title:
-
Synergistic application of logical brain and associative brain in oil-gas gathering and transportation systems
- Keywords:
- intelligent oil-gas gathering and transportation system; digital-intelligent agent; logical brain; associative brain; dual-brain synergy; artificial intelligence engineering
- 分类号:
- TE832
- 文献标志码:
- A
- 摘要:
- [目的]随着复杂系统在自然与工程领域的广泛应用，其非线性、涌现性等特性带来的管理难题日益凸显，传统简化分析方法难以应对动态不确定性挑战，而现有数据驱动算法又面临可解释性缺失与鲁棒性不足的双重困境。[方法]为应对这些挑战，提出了逻辑脑与关联脑的概念，构建一个双脑协同的数智体，旨在为工业领域的复杂系统智能化提供兼具智能水平与可靠性的解决方案。首先，介绍了以数字化为基础、以智能决策与控制为目标的数智体的内涵；随后，提出逻辑脑与关联脑的概念，逻辑脑负责逻辑规则，关联脑负责关联提取，共同构建数智体的核心模块；最后，分析了双脑协同的优势，提出3种建模过程的融合方法，以提升复杂系统智能化解决方案的有效性。[结果]在全球能源转型与"双碳"目标驱动下，油气集输系统正加速迈向智能化，但也面临管网复杂、多源干扰及全生命周期管理的技术挑战。当前国内外智能化建设仍处于数字化转型初期，亟需突破算法融合、动态数据共享及AI深度赋能等瓶颈，实现从局部优化到全系统智能决策的跨越。以油气集输系统中的油井虚拟计量、油气田生产优化及集输接转站故障归因分析3个案例，验证了逻辑脑与关联脑协同机制在油气集输系统智能化中的有效性。[结论]逻辑脑与关联脑双脑协同机制通过机理与数据融合，不仅确保了模型精度，还提升了预测速度、计算效率及故障诊断准确性，可显著增强油气集输系统实时监控、优化决策及故障响应能力，有效验证了所提方法对复杂系统的有效性。未来需突破小样本多模态数据融合、跨尺度建模及低碳化智能协同技术，构建机理驱动的动态强化学习框架，推动系统向全生命周期管理与多能协同优化演进，为能源绿色转型提供核心支撑。
- Abstract:
- [Objective] The widespread application of complex systems in natural and engineering domains has led to increasingly prominent management challenges due to their inherent characteristics, such as nonlinearity and emergence. Traditional simplified analytical methods struggle to address dynamic uncertainties while existing data-driven algorithms face dual challenges: a lack of explainability and insufficient robustness. [Methods] To address these challenges, this paper proposes the development of dual-brain synergistic digital-intelligent agents based on the concepts defined respectively for the logical brain and associative brain, aiming to provide solutions that integrate intelligence and reliability for the construction of intelligent complex industrial systems. The essence of agents, which are rooted in digitalization and designed for intelligent decision-making and control, was introduced. Then the paper elaborates on the logical brain and associative brain from a conceptual perspective: the logical brain operates based on logical rules, while the associative brain is designed for association extraction. Together, the core modules of digital-intelligent agents were formed. Finally, the advantages of dual-brain synergy were emphasized and three fusion methods for modeling were proposed , aiming to enhance the effectiveness of solutions for constructing intelligent complex systems. [Results] Driven by the global energy transition and the “dual carbon” goals, oil-gas gathering and transportation systems are evolving toward intelligence at a faster pace. However, technical challenges exists such as complex pipeline networks, multi-source interference, and full-lifecycle management. Currently, the construction of intelligent systems, both in China and abroad, remains in the initial stages of digital transformation. There is an urgent need to overcome bottlenecks in areas such as algorithmic fusion, dynamic data sharing, and deep AI integration to enable a leap from localized optimization to system-wide intelligent decision-making. The effectiveness of the logical brain and associative brain synergy mechanism in the construction of intelligent oil-gas gathering and transportation systems was validated through three case studies: virtual metering of oil wells, production optimization for an oil-gas field, and fault attribution analysis for a transfer station. [Conclusion] The dual-brain synergy mechanism is based on the integration of mechanisms and data, which ensures model accuracy while enhancing prediction speed, computational efficiency, and fault diagnosis accuracy. Case studies demonstrate significant improvements in real-time monitoring, decision-making optimization, and fault response capabilities, effectively validating the proposed approach for complex systems. Future research should focus on overcoming bottlenecks in multimodal data fusion with small sample sizes and addressing challenges in cross-scale modeling and intelligent collaboration techniques to support low-carbon transitions. Additionally, studies are expected to develop a mechanism-driven dynamic reinforcement learning framework, advancing system evolution toward full-lifecycle management and multi-energy collaborative optimization. These efforts aim to provide core support for green energy transformation.
更新日期/Last Update:
2025-03-24