Application of collaborative innovation between the logical brain and the associative brain in oil and gas gathering and transportation systems
ObjectiveWith the continuous deepening of human understanding of nature and the rapid advancement of technology, complex systems have been widely applied in both natural and engineering domains. However, the inherent characteristics of these systems, such as nonlinearity and emergence, have posed in...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Oil & Gas Storage and Transportation
2025-05-01
|
| Series: | You-qi chuyun |
| Subjects: | |
| Online Access: | https://yqcy.pipechina.com.cn/article/doi/10.6047/j.issn.1000-8241.2025.05.001 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | ObjectiveWith the continuous deepening of human understanding of nature and the rapid advancement of technology, complex systems have been widely applied in both natural and engineering domains. However, the inherent characteristics of these systems, such as nonlinearity and emergence, have posed increasingly prominent challenges for management and decision-making. Traditional simplified analytical methods struggle to cope with dynamic uncertainties, while existing data-driven algorithms are confronted with the dual challenges of lacking interpretability and insufficient robustness. MethodsTo 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. ResultsDriven 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. ConclusionThe 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. |
|---|---|
| ISSN: | 1000-8241 |