基于机器学习的四相流体在水库中的定性识别
1College of Geophysics, China University of Petroleum (Beijing), Beijing 102249, China.
ACS omega
|January 15, 2024
概括
精确识别含有二氧化碳 (CO2) 的储中的流体类型对于石油和天然气勘探至关重要. 本研究引入了一种机器学习方法,使用核日志记录数据来提高流体识别精度.
科学领域:
- 石油地质科学 石油地质科学
- 储水库工程 储水库工程
- 地理学井的记录记录时间
背景情况:
- 精确的液体识别在石油和天然气储备中对于勘探和开发至关重要.
- 储库中二氧化碳 (CO2) 的存在使储库评估和流体类型的确定变得复杂.
- 现有的流体识别日志记录方法通常受到形成变化,概括性差,以及对多个仪器的依赖的限制.
研究的目的:
- 开发一种可靠的方法,用于准确识别含有二氧化碳的储中的流体类型.
- 在复杂的水库条件下克服传统伐木技术的局限性.
- 通过机器学习利用多参数核日志记录数据来增强水库特征.
主要方法:
- 利用来自核记录的中子玛技术获得多参数数据.
- 应用机器学习算法,特别是支持矢量机 (SVM) 作为元模型,用于数据分析.
- 采用二分法分类策略来识别在采伐过程中的流体.
主要成果:
- 通过机器学习方法成功识别了含有二氧化碳的储中的液体.
- 证明了将核日志的多参数信息与机器学习相结合的有效性.
- 与传统技术相比,开发的方法提供了更好的准确性和概括性.
结论:
- 该研究提供了一种基于机器学习的新型解决方案,用于在含有二氧化碳的储中识别流体.
- 这种方法为设计先进的伐木仪器提供了新的见解.
- 这些发现对石油和天然气勘探,二氧化碳增强石油回收 (CO2-EOR) 和碳捕获,利用和储存 (CCUS) 有重大影响.
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