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科学领域:

  • 地质技术工程 地质技术工程
  • 数据科学数据科学数据科学
  • 地理基础设施 地理基础设施

背景情况:

  • 大规模的基于传感器的测量-在-钻井 (MWD) 数据对于评估道工程项目中的岩石工程条件至关重要.
  • 处理和处理大型MWD数据,通常受多种堆叠和噪声的影响,存在重大挑战.
  • 准确的地质工程解释需要将领域专业知识与数据科学技能相结合.

研究的目的:

  • 开发一种自动化方法来规范和过噪音测量-在-钻井 (MWD) 数据.
  • 引入一个新的规范化指数来对大型地球工程数据集进行分类.
  • 建立一个有效的数据管理系统,用于MWD和接数据.

主要方法:

  • 开发了一种自动化处理方法,整合了逐步技术,模式和百分比门带,用于数据规范化和过.
  • 提出了一种新的数学规范化指数,用于分类大型数据集.
  • 创建了一个关系统一的PostgreSQL数据库,用于存储和管理原始和处理的MWD数据以及实时接信息.

主要成果:

  • 开发的自动化方法有效地将测量在钻井 (MWD) 数据集中的噪音数据正常化和过.
  • 可视化结果表明,基于单个洞的规范化有效地消除了异常值和杂数据.
  • 为数据提取和管理建立了一个具有成本效益和效率的PostgreSQL数据库.

结论:

  • 自动化处理方法显著提高了在地理基础设施项目中处理大型MWD数据集的效率.
  • 新的规范化索引和数据库促进了深入的调查和AI技术的应用.
  • 这种方法预计将改善岩石质量的预测,并为适当的支持系统的设计提供信息.