大模型在油气管道行业的应用与展望
Application and prospect of large models in the oil and gas pipeline industry
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- 引用格式:
-
王玉霞,贾韶辉,林嵩,张新建,陈天民,苑浩鹏.大模型在油气管道行业的应用与展望[J].天然气与石油,2026,44(2):104-111.doi:10.3969/j.issn.1006-5539.2026.02.013
Wang Yuxia, Jia Shaohui, Lin Song, Zhang Xinjian, Chen Tianmin, Yuan Haopeng.Application and prospect of large models in the oil and gas pipeline industry[J].Natural Gas and Oil,2026,44(2):104-111.doi:10.3969/j.issn.1006-5539.2026.02.013
- DOI:
- 10.3969/j.issn.1006-5539.2026.02.013
- 作者:
- 王玉霞 贾韶辉 林嵩 张新建 陈天民 苑浩鹏
Wang Yuxia, Jia Shaohui, Lin Song, Zhang Xinjian, Chen Tianmin, Yuan Haopeng
- 作者单位:
- 国家石油天然气管网集团有限公司科学技术研究总院分公司, 天津 300457
PipeChina Institute of Science and Technology, Tianjin, 300457, China
- 关键词:
- 大模型;油气管道;自然语言处理;计算机视觉;多模态
Large models; Oil and gas pipeline; Natural language processing; Computer vision; Multi-modal
- 摘要:
- 随着大模型技术的不断发展,构建领域专有大模型,为领域发展提供智能化服务及问题解决路径,成为各行业研究热点。油气管道领域存在着数据分散、标注数据少、场景复杂多样等问题,难以实现多场景、多模态数据的协同分析。大模型有望为油气管道领域智能化发展提供新的解决方案。介绍了大模型及相关技术的发展现状;结合油气管道领域数据特点、具体场景需求,聚焦自然语言处理和计算机视觉的具体任务,研究在通用大模型上构建油气管道行业大语言模型、视觉大模型和多模态大模型的方法;重点探讨了油气管道行业大模型在智能办公、智能运行调度、设备预测性维护等场景的落地应用;从增强大模型知识性、降低部署成本、提高安全性等方面对油气管道行业大模型的研究做了进一步展望。结论认为,构建油气管道行业大模型可破解数据碎片化、知识经验传承难等行业痛点,推动油气管道行业智能化转型。但大模型的幻觉性、数据治理不规范等问题还需结合具体业务逐步解决。
With the continuous development of large models, building domain-specific large models to provide intelligent services and problem-solving solutions has become a research hotspot across various industries. The oil and gas pipeline industry faces challenges such as dispersed data, limited annotated data, and diverse complex scenarios, making it difficult to realize the collaborative analysis of multi-scenario and multi-modal data. Large models are expected to offer new solutions for the intelligent development of the oil and gas pipeline industry. Firstly, an overview of large models and related technologies is provided. Secondly, based on the data characteristics and specific scenario requirements of the oil and gas pipeline industry, this paper focuses on natural language processing and computer vision tasks to explore methods for constructing industry-specific large language models, visual large models, and multi-modal large models built upon general large models. The practical applications of large models in the oil and gas pipeline industry, including intelligent office automation, intelligent operation scheduling, and predictive maintenance of equipment are discussed. Finally, future directions for research and development of large models in this industry are envisioned, including enhancing the knowledge capabilities of large models, reducing deployment costs, and improving security. It is concluded that large pipeline model can solve data fragmentation and knowledge transfer issues, driving intelligent transformation. However, data governance problems must be solved step by step based on specific business needs.
