摘要
为提升海上油田录井作业的实时数据解析与智能化水平,构建了面向录井任务的大语言模型OffshoreGPT。该模型依托7405条结构化领域段落和约6000条高质量“问答对”进行预训练,并结合监督式微调与指令微调策略,提升了领域术语解析与专业文本生成能力,且全流程训练在配备多卡GPU的高性能服务器环境中完成,可确保在复杂工况下具备稳定性与快速响应能力。测试结果表明,OffshoreGPT在领域知识问答和故障诊断任务中的BLEU⁃4得分提高81.77%,ROUGE⁃L得分提高63.43%,在模拟录井场景中能够实时识别关键作业事件并生成风险提示,从而提高作业准确性与安全性,减少人工干预。该模型在现场技术支持中表现出良好的适配性,表明其在海上油田录井作业智能化应用中兼具可行性与优势。
To enhance the real⁃time data analysis and intelligence level of offshore oilfield mud logging operations,a large language model OffshoreGPT for mud logging tasks has been constructed.This model was pre⁃trained based on 7405 structured domain paragraphs and approximately 6000 high⁃quality"question⁃answer pairs".The Supervised Fine⁃Tuning and Instruction Tuning strategies are combined to improve the domain term analysis and professional text generation ability.And the full⁃process training is completed in a high⁃performance server environment equipped with multiple GPU cards,ensuring stability and fast response capabilities under complex working conditions.The test results show that OffshoreGPT has achieved an increase of 81.77%in BLEU⁃4 score and 63.43%in ROUGE⁃L score in domain knowledge questions and answers and fault diagnosis tasks.In the simulated mud logging scenarios,it can real⁃time identify key operational events and generate risk alerts,thereby improving operational accuracy and safety while reducing manual intervention.The model has shown good adaptability in on⁃site technical support,indicating that it is both feasible and advantageous for the intelligent application of mud logging operations in offshore oilfields.
作者
周光元
方振东
王红娜
蒋辉
白林坤
ZHOU Guangyuan;FANG Zhendong;WANG Hongna;JIANG Hui;BAI Linkun(China France Bohai Geoservices Co.,Ltd.,Tianjin 300457,China)
出处
《录井工程》
2026年第1期1-7,共7页
Mud Logging Engineering
