首页> 外国专利> INTEGRATED MACHINE LEARNING FRAMEWORK FOR OPTIMIZING UNCONVENTIONAL RESOURCE DEVELOPMENT

INTEGRATED MACHINE LEARNING FRAMEWORK FOR OPTIMIZING UNCONVENTIONAL RESOURCE DEVELOPMENT

机译:集成机器学习框架,用于优化非传统资源开发

摘要

Implementations described and claimed herein provide systems and methods for developing resources from an unconventional reservoir. In one implementation, raw reservoir data for the unconventional reservoir is obtained. The raw reservoir data includes geology data, completion data, development data, and production data. The raw reservoir data is transformed to transformed data. The raw reservoir data is transformed to the transformed data based on a transformation from a set of one or more raw variable to a set of one or more transformed variables. The set of one or more transformed variables is statistically uncorrelated. Resource development data is extracted from the transformed data. Performance analytics are generated for the unconventional reservoir using the resource development data. The performance analytics are generated through ensemble machine learning. The unconventional reservoir is developed based on the performance analytics.
机译:这里描述和要求保护的实施方式提供来自非传统水库的资源的系统和方法。在一个实施方式中,获得了非传统储层的原始储层数据。原始水库数据包括地质数据,完成数据,开发数据和生产数据。 RAW COLUCTOIR数据转换为转换数据。基于从一组一个或多个RAW变量的转换到一组一个或多个变换变量的变换,原始储库数据转换为变换数据。一个或多个转换变量的集是统计上不相关的。资源开发数据从转换数据中提取。使用资源开发数据为非传统水库生成性能分析。性能分析是通过集合机器学习生成的。非传统水库是根据性能分析开发的。

著录项

  • 公开/公告号WO2021086915A1

    专利类型

  • 公开/公告日2021-05-06

    原文格式PDF

  • 申请/专利权人 CONOCOPHILLIPS COMPANY;

    申请/专利号WO2020US57661

  • 发明设计人 ZHOU HUI;LASCAUD BENJAMIN;

    申请日2020-10-28

  • 分类号E21B49;E21B43/26;E21B43/16;E21B44;G01V1/44;

  • 国家 US

  • 入库时间 2022-08-24 18:36:29

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