Against the background of the deep coupling between the global energy structure transition and the national energy security strategy, offshore oil and gas development is regarded as a core field for ensuring China’s independent energy supply. However, the long-term development of this field has been hindered by three major technical bottlenecks: low efficiency in multimodal data recognition; insufficient accuracy in extracting geological parameters from small-sample scenarios; and weak capabilities for structurally integrating knowledge across different sources.A full-process integrated scheme of data acquisition-knowledge transformation-application implementation was innovatively constructed. Multimodal recognition technology, the fine-tuning strategy of the Qwen large model, and the knowledge graph construction method were combined. Chinese and English geological reservoir reports were used as the core data sources. Digital characterization and structured integration of four typical oil and gas reservoirs were completed. These reservoirs include fluvial facies reservoirs, delta facies reservoirs, marine facies oil reservoirs, and marine facies gas reservoirs.Experimental results show that the conversion rate from unstructured data to structured knowledge is over 90%. The extraction accuracy of core geological parameters is proven to reach 87.07%. Data processing efficiency is increased by 85% compared with the traditional manual method.Reliable knowledge support is provided for the optimization of offshore oil and gas development plans and scientific decision-making. Important theoretical and practical value is offered for the promotion of digital transformation in the energy sector.