Construction and preliminary application of large language model for reservoir performance analysis  

作　　者：PAN Huanquan LIU Jianqiao GONG Bin ZHU Yiheng BAI Junhui HUANG Hu FANG Zhengbao JING Hongbin LIU Chen KUANG Tie LAN Yubo WANG Tianzhi XIE Tian CHENG Mingzhe QIN Bin SHEN Yujiang 

机构地区：[1]School of Earth Resources of China University of Geosciences,Wuhan 430074,China [2]Heilongjiang Key Laboratory of Reservoir Physics&Fluid Mechanics in Porous Medium,Daqing 163712,China [3]Exploration and Development Research Institute of PetroChina Daqing Oilfield Co.,Ltd.,Daqing 163712,China

出　　处：《Petroleum Exploration and Development》2024年第5期1357-1366,共10页石油勘探与开发（英文版）

基　　金：Supported by the National Talent Fund of the Ministry of Science and Technology of China(20230240011);China University of Geosciences(Wuhan)Research Fund(162301192687)。

摘　　要：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.

关 键 词：reservoir performance analysis artificial intelligence large model application-specific large language model in-cremental pre-training fine-tuning subsystems coupling entity recognition tool invocation 

分 类 号：TE331[石油与天然气工程—油气田开发工程] TP18[自动化与计算机技术—控制理论与控制工程]