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    • 10. 发明授权
    • Processing time series data embedded in high noise
    • 处理时间序列数据嵌入高噪声
    • US09334718B2
    • 2016-05-10
    • US12652405
    • 2010-01-05
    • Henri-Pierre ValeroSandip BoseQiuhua LiuRamachandra ShenoyAbderrhamane Ounadjela
    • Henri-Pierre ValeroSandip BoseQiuhua LiuRamachandra ShenoyAbderrhamane Ounadjela
    • G01V1/40E21B43/26G01V1/28
    • E21B43/26G01V1/288G01V2210/123
    • Automatic detection and accurate time picking of weak events embedded in strong noise such as microseismicity induced by hydraulic fracturing is accomplished by: a noise reduction step to separate out the noise and estimate its spectrum; an events detection and confidence indicator step, in which a new statistical test is applied to detect which time windows contain coherent arrivals across components and sensors in the multicomponent array and to indicate the confidence in this detection; and a time-picking step to accurately estimate the time of onset of the arrivals detected above and measure the time delay across the array using a hybrid beamforming method incorporating the use of higher order statistics. In the context of hydraulic fracturing, this could enhance the coverage and mapping of the fractures while also enabling monitoring from the treatment well itself where there is usually much higher and spatially correlated noise.
    • 自动检测和精确时间采集嵌入强噪声(如水力压裂引起的微震)的弱事件是通过以下方式实现的:通过降噪步骤分离噪声并估计其频谱; 事件检测和置信指标步骤,其中应用新的统计测试以检测哪个时间窗口包含多组件阵列中的组件和传感器之间的相干到达,并指示该检测的置信度; 以及精确估计上述检测到达时间的时间选择步骤,并且使用结合使用更高阶统计量的混合波束成形方法来测量阵列上的时间延迟。 在水力压裂的背景下,这可以增强裂缝的覆盖和映射,同时还能够从处理井本身进行监测,其中通常有更高的空间相关噪声。