Objective With the rapid development of artificial intelligence (AI), large model technology has been widely applied and has demonstrated significant potential across various fields. Given that oil and gas pipeline networks are key infrastructure for national energy transportation, the potential application of large model technology in their intelligent upgrading process has garnered increasing attention. However, numerous challenges within this field have hindered deeper applications. Therefore, further promoting the fusion of large model technology with the oil and gas pipeline network industry is considered an important task for enhancing the intelligence level of this industry.

Methods This paper presents the challenges encountered in the existing applications of large models within the oil and gas pipeline network industry, through an in-depth analysis that examines four key dimensions: data, computing power, algorithms, and research paradigms. Based on the analysis results, a large model architecture for the oil and gas pipeline network industry is proposed, focusing on two aspects: the construction of intelligent oil and gas pipeline network systems and the development of large models specifically applicable to this industry. This architecture consists of the infrastructure layer, technical foundation layer, model layer, and control layer. Building on the proposed architecture, targeted solutions are presented to address the challenges associated with the development of large models for the industry, along with the formulation of a complete technical roadmap.

Results The large model architecture established for the oil and gas pipeline network industry from multiple dimensions provides systematic technical support for the construction and operation of intelligent systems for oil and gas pipeline networks and the research and development of associated large models. The dataset construction methodology, integrated platform, multi-technology fusion technical roadmap, and layered construction solution for large models proposed based on this architecture effectively address the challenges encountered in the aforementioned four dimensions. The application of these solutions in scenarios such as the intelligent control of natural gas pipeline networks and the forecasting of natural gas demand has significantly enhanced the level of intelligence in related fields.

Conclusion The research outcomes offer comprehensive technical guidance for the intelligent upgrading of the oil and gas pipeline network industry, holding significant importance for enhancing cognitive levels in the intelligent pipeline network field and promoting the development of large models within this industry. Looking ahead, the application of large models in the oil and gas pipeline network industry is expected to expand in both breadth and depth. The key to improving the intelligence level of this industry lies in keeping pace with the advancement of technologies, prioritizing the development of data assets, and actively promoting technology fusion.