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<p>Natural language to structured query language (NL2SQL) techniques, offering zero-code access to relational databases, have recently gained significant attention in the Oilfield data analysis, where domain experts often lack expertise in SQL programming and database manipulation. Retrieval-Augmented Generation (RAG) further enhances NL2SQL by incorporating external knowledge repositories. Nevertheless, practical benchmark datasets for oilfield data analysis remain scarce, existing NL2SQL approaches frequently overlook domain-specific database characteristics, and conventional RAG methods struggle with complex queries due to single-path retrieval and limited relevance. To overcome these challenges, we developed a specialized NL2SQL benchmark dataset for oilfield data analysis and proposed the OilRAGSQL framework, which integrates multi-hop retrieval and context reranking to enhance the generative reasoning performance of large language models (LLMs) in complex query generation. In particular, we established a multi-source knowledge base that integrates question–answer pairs, specialized terminology, and database schema documentation. During retrieval, a two-hop strategy integrating semantic matching, schema-aware filtering, and domain relevance is employed to refine the search space, whilst a unified scoring and reranking mechanism optimises the retrieved content to improve prompt quality for LLMs. Experiments on oilfield datasets demonstrate the framework’s strong ability to handle complex queries in specialized domains, underscoring its practical applicability in petroleum data analysis.</p>

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OilRAGSQL: A RAG-based large language model framework for NL2SQL tasks in oilfield data analysis

  • 期刊论文

  • 发表日期：

    2026年5月18日

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  • Tingting He,
  • Qiang Zhang,
  • Guanyu Zheng,
  • Haochang Wang

摘要

Natural language to structured query language (NL2SQL) techniques, offering zero-code access to relational databases, have recently gained significant attention in the Oilfield data analysis, where domain experts often lack expertise in SQL programming and database manipulation. Retrieval-Augmented Generation (RAG) further enhances NL2SQL by incorporating external knowledge repositories. Nevertheless, practical benchmark datasets for oilfield data analysis remain scarce, existing NL2SQL approaches frequently overlook domain-specific database characteristics, and conventional RAG methods struggle with complex queries due to single-path retrieval and limited relevance. To overcome these challenges, we developed a specialized NL2SQL benchmark dataset for oilfield data analysis and proposed the OilRAGSQL framework, which integrates multi-hop retrieval and context reranking to enhance the generative reasoning performance of large language models (LLMs) in complex query generation. In particular, we established a multi-source knowledge base that integrates question–answer pairs, specialized terminology, and database schema documentation. During retrieval, a two-hop strategy integrating semantic matching, schema-aware filtering, and domain relevance is employed to refine the search space, whilst a unified scoring and reranking mechanism optimises the retrieved content to improve prompt quality for LLMs. Experiments on oilfield datasets demonstrate the framework’s strong ability to handle complex queries in specialized domains, underscoring its practical applicability in petroleum data analysis.