Amid escalating global climate change and the pursuit of carbon-neutrality goals, carbon emission management and carbon footprint analysis have become central challenges in the green transition of the petroleum industry. Traditional carbon footprint accounting, however, is constrained by heterogeneous data sources, complex operational procedures, and high technical barriers for non-R&D personnel. To address these challenges, this paper proposes a lightweight multi-agent framework for oilfield carbon emission data analysis. Through natural-language interaction driven by LLM, the system integrates data access, a dual retrieval-augmented generation mechanism with Schema RAG and Document RAG, a Planner-Executor workflow, and automated report generation. Business personnel can complete intent parsing, SQL generation, statistical computation, chart rendering, and report composition through natural-language instructions. The framework is evaluated in real-world business scenarios across 13 oilfields, including basic query tasks, statistical analysis tasks, visualization generation tasks, report generation tasks, and multi-turn follow-up tasks. Experimental results show that the full system configuration, which combines Schema RAG, Planner-Executor, and Document RAG, increases the task completion rate to 93.3%, the SQL semantic consistency rate to 95.8%, and the visualization success rate to 100%, while also improving report quality and multi-turn interaction consistency. This framework lowers the technical barrier of complex data exploration, improves data processing efficiency, and provides a scalable and practical solution for the low-carbon digital transformation of the petroleum industry.
Liu et al. (Tue,) studied this question.