青环海蛇属海蛇科,在我国南海与东海很常见。它的毒液有很强的毒性,其中含有神经毒素及多种蛋白质(Liuet al.,1973,1974;苏拔贤,曾家荣等,1984)。已知蝰蛇科与眼镜蛇科蛇毒中含有蛋白酶抑制剂(Sadaaki Iwanaga et al.,1975与Yasuji Hoka...青环海蛇属海蛇科,在我国南海与东海很常见。它的毒液有很强的毒性,其中含有神经毒素及多种蛋白质(Liuet al.,1973,1974;苏拔贤,曾家荣等,1984)。已知蝰蛇科与眼镜蛇科蛇毒中含有蛋白酶抑制剂(Sadaaki Iwanaga et al.,1975与Yasuji Hokama et al,1976)。但尚未见关于海蛇科蛇毒中有胰蛋白酶抑制剂的实验报道。我们的工作中,初步发现海蛇科的青环海蛇蛇毒及其组份表现胰蛋白酶抑制剂活性。展开更多
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 re...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.展开更多
文摘青环海蛇属海蛇科,在我国南海与东海很常见。它的毒液有很强的毒性,其中含有神经毒素及多种蛋白质(Liuet al.,1973,1974;苏拔贤,曾家荣等,1984)。已知蝰蛇科与眼镜蛇科蛇毒中含有蛋白酶抑制剂(Sadaaki Iwanaga et al.,1975与Yasuji Hokama et al,1976)。但尚未见关于海蛇科蛇毒中有胰蛋白酶抑制剂的实验报道。我们的工作中,初步发现海蛇科的青环海蛇蛇毒及其组份表现胰蛋白酶抑制剂活性。
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)。
文摘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.
