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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
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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.

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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.

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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.