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油藏动态分析场景大模型构建与初步应用

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针对目前油藏动态分析中井史数据检索与分析、连井剖面绘制、开发生产关键技术指标计算、油藏复杂问题的措施建议等方面的智能化需求,采用增量预训练、指令微调和功能子系统耦合3个步骤构建油藏动态分析场景大模型,提出了基于命名实体识别技术、工具调用技术、Text-to-SQL(自然语言转换成结构化查询语言)技术微调的功能子系统及其高效耦合方法,将人工智能大模型运用到油藏动态分析领域.测试了特征提取模型、工具分类模型、数据检索模型、分析建议模型的准确性,结果表明这些模型在油藏动态分析的各个关键环节均展现出了良好的性能.最后以大庆油田PK3区块部分注采井组为例,测试验证了油藏动态分析场景大模型在辅助油藏工程师进行油藏动态分析方面具有的运用价值和潜力,为大模型在油藏动态分析中的运用提供了较好的技术支持.
Construction and preliminary application of large language model for reservoir performance analysis
A large language model(LLM)is constructed to address the sophisticated demands of data retrieval and analysis,detailed well profiling,computation of key technical indicators,and the solutions to complex problems in reservoir performance analysis(RPA).The LLM is constructed for RPA scenarios with incremental pre-training,fine-tuning,and functional subsystems coupling.Functional subsystem and efficient coupling methods are proposed based on named entity recognition(NER),tool invocation,and Text-to-SQL construction,all aimed at resolving pivotal challenges in developing the specific application of LLMs for RDA.This study conducted a detailed accuracy test on feature extraction models,tool classification models,data retrieval models and analysis recommendation models.The results indicate that these models have demonstrated good performance in various key aspects of reservoir dynamic analysis.The research takes some injection and production well groups in the PK3 Block of the Daqing Oilfield as an example for testing.Testing results show that our model has significant potential and practical value in assisting reservoir engineers with RDA.The research results provide a powerful support to the application of LLM in reservoir performance analysis.

reservoir performance analysisartificial intelligence large modelapplication-specific large language modelincremental pre-trainingfine-tuningsubsystems couplingentity recognitiontool invocation

潘焕泉、刘剑桥、龚斌、朱艺亨、白军辉、黄虎、方政保、敬洪彬、刘琛、匡铁、兰玉波、王天智、谢添、程名哲、秦彬、沈榆将

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中国地质大学(武汉)资源学院,武汉 430074

黑龙江省油层物理与渗流力学重点实验室,大庆 163712

中国石油大庆油田有限责任公司勘探开发研究院,大庆 163712

油藏动态分析 人工智能大模型 场景大模型 增量预训练 指令微调 系统耦合 实体识别 工具调用

中华人民共和国科学技术部国家级人才专项科研基金中国地质大学(武汉)"地大学者"人才岗位科研启动经费

20230240011162301192687

2024

10.11698/PED.20240208

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(5)
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