Can continuous recorded data be improved with signal processing? The application of deconvolution to microseismic data

Ronald M Weir, Laurence R. Lines, Don C. Lawton and David W. Eaton

ABSTRACT

Passive seismic recording is increasingly being used to record seismic events associated with hydraulic fracture stimulation. The recorded amplitudes of these induced seismic events are relatively small and may be undetectable given the noisy environment in which they are recorded. Here we describe a method using reflection seismic processing techniques applied to continuously recorded passive (microseismic) data. Signal processing has been used for many years in reflection seismic processing to enhance signal quality. Algorithms such as deconvolution, scaling, and various types of filtering have been routinely applied to raw recorded data to enhance the processing and interpretability of the recorded data. Induced seismic events, such as perforation shots, can provide a time to depth relationship, although they may be difficult to detect. Induced seismic events caused by hydraulic fracturing events can indicate the depth and direction of the fracture stimulation, and induced seismicity may identify geohazards. In this study we apply a combination of the more commonly used algorithms used in reflection data processing to continuously recorded microseismic data and demonstrate how signal quality can be improved. These results demonstrate how signal processing can lead to more reliable detection of induced seismic events, and significantly improve the overall signal quality.

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