Microseismic mapping of hydraulically-induced fracture networks has become a standard industry technique to monitor stimulation effectiveness. When it comes to the microseismic processing workflow, there is still much to learn from the interaction between sensor selection, survey design, operations, etc. This work focuses on surface microseismic and compares results following recent internal improvement in processing to the results provided initially by a third-party company. The dataset being analyzed is of a 7-stage vertical treatment monitored by a surface array. The goal is to evaluate the impacts of key aspects of surface processing workflow including detection criteria and focal-mechanism handling.


Surface mapping of microseismic events induced by hydraulic fracturing provides information as to the mapped network’s length, height, orientation, and focal mechanism. This information is essential for monitoring effective fluid flow and stimulated reservoir volume, as well as for decision-making on fracture operations and well completions (Fisher et al., 2002; Le Calvez et al., 2005). Reliable detection and the precise location of microseismic events are therefore the primary goal of microseismic processing and the key to a successful monitoring exercise.

In this work, we focus on surface microseismic processing. Thanks to modern computing capabilities, most surface microseismic mapping techniques apply non-pick-based imaging methods to detect microseismic events and identify their locations where the model-driven energy stacks show distinct signal-to noise ratios (SNR).

Although the imaging location methods allow automatic detection of microseismic events, the criteria and SNR threshold value for triggering event candidates is arbitrary. For example, results may be contaminated by small uncertain event candidates if the SNR threshold is too low.

In addition, the dominant source focal mechanism of microseismic events is double-couple. As a result we observe changing polarity and amplitude of event signals across the surface array. Incorporating assumed focal mechanisms or more scientifically correct, appropriately inverted focal mechanisms, or removing polarity via non-linear stacking, results in different SNR of the stacks, which may lead to biased detection and location.

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