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油气行业人工智能大模型应用研究现状及展望

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阐述了大模型技术的概念,总结了大模型技术的国内外研究现状,综述了大模型在垂直领域的应用现状,梳理了油气行业大模型应用面临的挑战,并对油气行业大模型应用进行了展望.现有大模型可粗略分为3类,即大语言模型、视觉大模型和多模态大模型.油气行业大模型应用刚刚起步,部分油气企业基于开源大语言模型,利用微调、检索增强等方式发布大语言模型产品,部分学者尝试利用视觉/多模态基础模型研发面向油气业务的场景模型,还有少数学者构建地震资料处理解释、岩心分析等领域的预训练基础模型.油气行业大模型应用面临数据量和数据质量难以支撑大模型训练、研发投入成本高、难以实现算法自主可控等挑战.油气行业在应用大模型时应始终聚焦油气主营业务需求,以大模型应用为契机,加强数据全生命周期管理,提升数据治理能力,推动融合算力建设,加强"人工智能+能源"复合团队建设,推动大模型技术自主可控.
Research status and application of artificial intelligence large models in the oil and gas industry
This article elucidates the concept of large model technology,summarizes the research status of large model technology both domestically and internationally,provides an overview of the application status of large models in vertical industries,outlines the challenges and issues confronted in applying large models in the oil and gas sector,and offers prospects for the application of large models in the oil and gas industry.The existing large models can be briefly divided into three categories:large language models,visual large models,and multimodal large models.The application of large models in the oil and gas industry is still in its infancy.Based on open-source large language models,some oil and gas enterprises have released large language model products using methods like fine-tuning and retrieval augmented generation.Scholars have attempted to develop scenario-specific models for oil and gas operations by using visual/multimodal foundation models.A few researchers have constructed pre-trained foundation models for seismic data processing and interpretation,as well as core analysis.The application of large models in the oil and gas industry faces challenges such as current data quantity and quality being difficult to support the training of large models,high research and development costs,and poor algorithm autonomy and control.The application of large models should be guided by the needs of oil and gas business,taking the application of large models as an opportunity to improve data lifecycle management,enhance data governance capabilities,promote the construction of computing power,strengthen the construction of"artificial intelligence+energy"composite teams,and boost the autonomy and control of large model technology.

foundation modellarge language modevisual large modelmultimodal large modellarge model of oil and gas industrypre-trainingfine-tuning

刘合、任义丽、李欣、邓岳、王勇涛、曹倩雯、杜金阳、林志威、汪文洁

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多资源协同陆相页岩油绿色开采全国重点实验室,黑龙江大庆 163000

中国石油勘探开发研究院,北京 100083

北京航空航天大学,北京 100191

北京大学,北京 100871

中国石油大学(北京),北京 102249

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基础模型 大语言模型 视觉大模型 多模态大模型 油气行业大模型 预训练 微调

国家自然科学基金科学中心项目基础科学中心项目国家自然科学基金面上项目中国石油科技创新基金研究项目中国石油-北京大学战略合作协议基础研究合作项目

72088101423721752021DQ02-0904

2024

10.11698/PED.20240254

石油勘探与开发
中国石油天然气股份有限公司勘探开发研究院 中国石油集团科学技术研究院

石油勘探与开发

CSTPCD北大核心
影响因子：4.977
ISSN：1000-0747
年,卷(期)：2024.51(4)