As microseismic monitoring expands, a wide variety of monitoring configurations have evolved including vertical, horizontal and deviated observation wells as well as surface and near-surface monitoring. All monitoring configurations have a common data quality indicator: signal-to-noise ratio (SNR) such that the higher the SNR the more accurate and confident the results. The key criteria for a successful microseismic project therefore primarily involve maximizing SNR. Data acquisition can be designed to optimize SNR by using low-noise equipment designed to record appropriate data quality, deployed as close as possible to the target zone. Sample rate should be tailored to the signal bandwidth, and the equipment should also have optimal directional response although for individual microseismic events both will be controlled by the data SNR. Finally, the position of the sensor array will control the fundamental location accuracy, although this will be commonly be a trade-off with SNR depending on logistical constraints of monitoring wellbore access. Recent processing techniques based on seismic migration methods, offer automated and repeatable processing with inherent signal conditioning which provided SNR improvement. Associated with the automated processing is the ability of realtime delivery of all the microseismic events for realtime stimulation decisions. A demonstration with an automatically processed dataset, illustrated the importance of filtering the events based on SNR. Low SNR events had higher location uncertainty, such that both the volume of the microseismic cloud and its aspect ratio were made anomalously large. An accurate microseismic image was produced by filtering out the low confidence, low SNR events. Beyond the geophysical processing aspects, it is equally important that the microseismic project be designed so that engineering value can be extracted by determining valuable fracture details or answering key questions.

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