Key Criteria for a Successful Microseismic Project

Abstract

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.

Microseismic Imaging of Hydraulically Induced-Fractures in Gas Reservoirs: A Case Study in Barnett Shale Gas Reservoir, Texas, USA

“…SRV is usually calculated using the dimensions of the 3D seismic cloud that is commonly associated with both measurement uncertainty and tendency towards over SRV estimations [37]. Through early evaluations of the relationship between the measurement uncertainty and the volume of microseismic cloud, Reference [38] indicated that larger source location uncertainty is associated with the larger the microseismic cloud volume with a tendency to overestimate the underlying deforming volume. The tendency towards SRV overestimated develops due to the microseismicity induced by stresses of hydraulic treatment acting on pre-existing structures, which is usually not represented in geomechanical models [6].…”

Improvements on particle swarm optimization algorithm for velocity calibration in microseismic monitoring

“…Velocity calibration is an indispensable data processing step in microseismic monitoring, which obtains reliable initial models for microseismic data analysis (Cipolla et al 2012;Maxwell et al 2010). Various methods have been applied to this problem (Pei et al 2009;Warpinski et al 2005;Warpinski and Du 2013).…”

Comparative Microseismic Interpretation of Hydraulic Fractures

“…The ability of this SNR filter to high grade the event data set has been described previously (e.g. Maxwell et al, 2010). An SNR filter can be extended to consider additional quality control attributes (Maxwell et al, 2007) such as confidence level defined as a score between 0 (min) and 5 (max) for contributing factors of p-and s-wave SNR, arrival time residuals and hodogram consistency.…”