使用大型语言模型从历史井记录中提取信息
Zhiwei Ma1, Javier E Santos2, Greg Lackey3
1Earth & Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87544, USA. m.ma@lanl.gov.
Scientific reports
|December 31, 2024
概括
本研究介绍了一种使用大语言模型 (LLM) 来从历史石油和天然气井记录中提取数据的计算方法. 这种方法有效地定位了孤儿井,改善了减少环境风险的努力.
科学领域:
- 环境科学与工程环境科学与工程
- 地理科学信息管理 信息管理
- 人工智能在地球科学中的应用
背景情况:
- 孤儿井 (废弃的石油和天然气井) 构成重大环境风险.
- 从历史井记录中手动提取数据是耗时且低效的.
- 准确的井位置和深度数据对于有效的堵塞和整治至关重要.
研究的目的:
- 开发和评估一种计算方法,以快速和经济有效地对孤儿井进行表征.
- 利用大型语言模型 (LLM) 来从历史文档中提取重要信息.
- 从传统的石油和天然气记录中自动化地质科学数据的数字化.
主要方法:
- 使用开源Llama 2大型语言模型开发了一个信息提取工作流.
- 该工作流被测试在160个历史井文件的数据集上.
- 绩效是根据提取井位置和深度的准确性进行评估,并比较基于PDF和基于图像的记录.
主要成果:
- 基于LLM的工作流实现了100%的准确性,以清洁,基于PDF的井报告.
- 在处理非结构化,基于图像的井记录时,准确度降至70%.
- 与手工数字化相比,自动化方法显著减少了劳动力,提高了效率.
结论:
- 大型语言模型提供了一个强大的工具,用于访问和利用大量的地质科学信息存储在历史文档.
- 开发的工作流程展示了LLM加速识别和描述孤儿井的潜力.
- 为了更广泛的应用,需要进一步改进处理非结构化数据,但自动化和减少劳动力的好处是巨大的.
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