人工智能大模型在天然气勘探开发中的应用现状OA
Application of large-scale AI models in natural gas exploration and development
为了破解我国天然气供应面临的技术瓶颈,系统梳理了人工智能大模型在天然气勘探开发中的可行应用场景.以DeepSeek为范例,采用技术迁移与案例实证相结合的方法,通过归纳其行业应用范式,构建了适用于天然气领域的技术迁移路径;进而结合国内油气企业实践,系统论证了相关大模型的应用场景.研究结果表明:①在知识管理方面,大模型能构建智能问答系统,内化海量非结构化资料,系统化专家经验,显著提升决策支持效率;②在数据处理与解释方面,其多模态融合能力可统一处理地震、测井等多源数据,实现地质特征智能提取与储层精准表征,助力"甜点"预测;③在工程作业方面,基于计算机视觉的岩心薄片智能识别技术,可实现地质描述的自动化与客观化;④在生产优化方面,依托时序预测与强化学习模型,可实现全气田的实时调度、故障预警与措施优化,从而提高油气采收率;⑤人工智能大模型在天然气勘探开发中要实现落地,仍面临数据安全、领域知识融合、模型泛化及系统集成等挑战.结论认为,以DeepSeek为代表的人工智能大模型为实现从"经验驱动"到"数据与模型双驱动"的范式变革提供了关键技术路径;未来通过深化领域知识嵌入、探索大小模型协同、构建人机平台及完善安全体系,将有力推动天然气勘探开发的智能化进程,进而为保障国家能源安全提供技术支撑.
To break through the technical bottlenecks constraining China's natural gas supply,this study systematically reviews the feasible application scenarios of large-scale artificial intelligence(AI)models in natural gas exploration and development.Taking DeepSeek as an example,an approach integrating technology transfer with case study is adopted,to construct a technology transfer pathway suitable for the natural gas sector by summarizing the paradigm of DeepSeek in industry applications.Furthermore,based on the practices of domestic oil and gas enterprises,the application scenarios of foundation models are thoroughly demonstrated.The following results are obtained.(i)In terms of knowledge management,foundation models can establish intelligent Q&A systems,internalize vast amounts of unstructured data,systematize expert experiences,and significantly enhance decision-support efficiency.(ii)Regarding data processing and interpretation,the multimodal fusion capability of foundation models enables the unified handling of multi-source data such as seismic and logging data,achieving intelligent extraction of geological features and accurate reservoir characterization to facilitate"sweet spot"prediction.(iii)For engineering operations,computer vision-based intelligent recognition technology for core thin sections allows for automatic and objective geological description.(iv)In production optimization,time-series forecasting and reinforcement learning models are leveraged to achieve real-time field-wide scheduling,fault warning,and operational optimization,thereby improving oil and gas recovery.(v)The deployment of large-scale AI models in natural gas exploration and development still faces challenges in respect to data security,domain-specific knowledge integration,model generalization,and system integration.In conclusion,exemplified by DeepSeek,large-scale AI models provide a key technological pathway for shifting the paradigm from"experience-driven"to"data-and model-driven".In the future,by deepening domain knowledge embedding,exploring the synergy between large-scale and small-scale models,constructing human-machine collaborative platforms,and refining security frameworks,the intelligentization of natural gas exploration and development will be vigorously promoted,offering technical support for ensuring national energy security.
张烈辉;倪美琳;赵玉龙;李慧琳;曾星杰;杨春艺;罗山贵
油气藏地质及开发工程全国重点实验室(西南石油大学) 四川 成都 610500油气藏地质及开发工程全国重点实验室(西南石油大学) 四川 成都 610500油气藏地质及开发工程全国重点实验室(西南石油大学) 四川 成都 610500中国石化江汉油田分公司清河采油厂 山东寿光 262700西南石油大学计算机科学学院 四川 成都 610500油气藏地质及开发工程全国重点实验室(西南石油大学) 四川 成都 610500西南石油大学理学院 四川 成都 610500
能源科技
人工智能天然气勘探开发DeepSeek语言大模型多模态大模型行业大模型应用场景
Artificial intelligenceNatural gas exploration and developmentDeepSeekLarge language modelMultimodal foundation modelDomain-specific foundation modelApplication scenario
《天然气勘探与开发》 2026 (1)
1-14,14
青年科学基金项目(A类)(原国家杰出青年科学基金项目)"油气藏渗流力学"(编号:51125019)国家自然科学基金委员会青年基金项目"基于物理图神经网络的井间连通性智能识别理论与方法"(编号:52404040).
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