Proceedings of the 4th International Conference on Public Management and Intelligent Society, PMIS 2024, 15–17 March 2024, Changsha, China

Research Article

CISFA: A Decision-Support Agent Framework and its Allied Implementation with Generated AI in Oil and Gas Industry

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  • @INPROCEEDINGS{10.4108/eai.15-3-2024.2346419,
        author={Hongzhi  Chen and Wei  Jin and Xiufeng  Lin},
        title={CISFA: A Decision-Support Agent Framework and its Allied Implementation with Generated AI in Oil and Gas Industry},
        proceedings={Proceedings of the 4th International Conference on Public Management and Intelligent Society, PMIS 2024, 15--17 March 2024, Changsha, China},
        publisher={EAI},
        proceedings_a={PMIS},
        year={2024},
        month={6},
        keywords={aigc multiple-agent allied implementation oil \& gas industry tasks division self-supervision},
        doi={10.4108/eai.15-3-2024.2346419}
    }
    
  • Hongzhi Chen
    Wei Jin
    Xiufeng Lin
    Year: 2024
    CISFA: A Decision-Support Agent Framework and its Allied Implementation with Generated AI in Oil and Gas Industry
    PMIS
    EAI
    DOI: 10.4108/eai.15-3-2024.2346419
Hongzhi Chen1,*, Wei Jin1, Xiufeng Lin1
  • 1: AI business unit Kunlun Digital Technology, PeroChina, Co, Ltd
*Contact email: Chenhongzhi@cnpc.com.cn

Abstract

With a rapid development of artificial intelligence generative content (AIGC), a set of human-machine interactive models have been changed. However, with limited understanding of the purposes of industry, even though fined-tuned by the professional data. The manuscript proposes a multi-role, self-closed-loop intelligent agent collaborative system framework (CISFA) that can compensate for the shortcomings of LLM in professional semantic understanding, multi-round self-interaction, and judgment and decision-making application scenes based on feedback and self-supervision between multiple role agents in multi-round Q&A based decision-making scenarios. Meanwhile, feasibility of applying medium-sized LLMs to aforementioned industry scenarios to achieve performance similar to that of very large-scale base models have also been considered. Through joint application with AIGC large models in three standard industry scenarios: drilling well control, device asset operation and maintenance management, as well as refining device operation guidance searching, it is proven that CISFA agent framework is effective in reducing the engineering application threshold of large models, simplify the prompt process and interpretation of industry mechanisms, and reducing application costs since medium-sized LLM been proven to show similar performance as very-large LLM by the allied application with CISFA.