针对自然语言到结构化查询语言(Natural Language to SQL,NL2SQL)问题在油气田勘探开发领域数据检索中的挑战,提出了一种基于大型NLP模型并融合外部知识库的智能数据检索新方法.首先,根据油气田勘探开发的业务场景构建种子数据,为模型训练奠定基础.借助"思维链"策略扩充数据集,提升数据覆盖度和多样性.接着,通过引入低秩适应(Low-Rank Adaptation of Large Language Models,LoRA)算法流程,优化模型在油气田数据检索任务上的表现.同时,整合外部知识库以提高模型对油气田专业数据的预测准确性和鲁棒性.实验结果表明,该方法在油气田勘探开发领域私有数据的检索准确率相较现有技术提高了 20％.基于此,开发了一套用户友好的应用系统,具有直观的界面和强大的功能,展示了该研究方法在油气田数据智能检索中的实用性和优越性.
Method for Intelligent Retrieval of Exploration and Development Data of Oil and Gas Fields Driven by Large Language Model
In order to meet the challenges that NL2SQL has in querying exploration and development data of oil and gas fields,the paper proposes a novel intelligent retrieval method by employing large NLP models and integrating external knowledge bases.Initially,the seed data is constructed for the training of the model.The dataset is expanded with a'thought-chain'strategy to increase data coverage and diversity.Subsequently,the Low-Rank Adaptation of Large Language Models(LoRA)algorithm process is introduced to optimize the model's performance in specific tasks.And also,the external knowledge bases are integrated to enhance the accuracy and robustness of the model in the predictions of specialized oil and gas field data.Experimental results indicate that by this method,compared to existing technologies,the retrieval accuracy of specific private data is improved by 20％.Additionally,a user-friendly application system has been developed,featuring a intuitive interface and robust functionality,which demonstrates the research method is practical and superior in intelligent retrieval of oil and gas field data.
NL2SQLexploration and development data of oil and gas fieldsLow-Rank Adaptation(LoRA)external knowledge basethought-of-chain
NETL
NSTL
万方数据
