Integrated analysis of anomalous microseismic behavior in a Montney treatment: Engineering parameters, locations, moment tensors, and geomechanics

Microseismic monitoring is crucial for evaluating hydraulic fracturing operations and understanding the subsurface. Processing and analyzing microseismic signals induced by fracturing fluid injection provides insights into pore pressure and in-situ stress changes. However, the large volume of recorded data and the variability in microseismic signals present significant challenges in the efficient and accurate processing and analysis of microseismic data. For example, an automated energy-based detector, the short-time average over the long-time average, can result in many false alarms, making event detection in large data sets time-consuming. Determining event locations also faces challenges due to velocity model errors, uncertainties in arrival time picking, or lack of geophone coverage. Large data sets demand location algorithms to provide hypocentral estimation with high accuracy and at a preferably low computational cost. Accelerating location algorithms to resolve the efficiency challenge is thus crucial. Furthermore, hydraulic fracture networks in the subsurface are complex, contributing to highly variable recorded microseismic waveforms. Understanding the geomechanical context is also essential for interpreting microseismic behavior. This thesis studies an extensive microseismic data set induced from 78 hydraulic fracturing treatment stages across four horizontal wells in the Montney reservoir in northeastern British Columbia, Canada. The microseismic activity exhibits substantial variations between treatment stages, with most events concentrated near the heel of the wells. Different hypotheses have been proposed for the leading cause of anomalous microseismic behavior. It could be operational issues, changes in treatment parameters, errors in microseismic data processing, pre-existing faults, and changes in the geological and geomechanical properties of the medium. First, I examine operational problems by scrutinizing fracturing treatment records for each stage, studying issues like screen-out conditions that may cause cessation of the fracturing process, and determining their correlation with the microseismicity. Second, changes in treatment parameters are considered, specifically breakdown pressure, injection rate, and treatment duration, to understand their impact on microseismic activity. Third, I investigate whether anomalies result from inefficient detection algorithms, using different automated detection methods to determine any related processing errors. Fourth, I perform an integrated analysis to study the impacts of geological and geomechanical changes on microseismicity. The treatment wells could travel in and out of zones with lateral variation in lithology or pre-existing fractures/faults in the medium can lead to the event anomaly. Major findings indicate that operational issues, treatment parameter changes, and data processing are not the primary causes of the microseismic anomaly. Evidence from the evolution of the microseismic cloud distance over time, moment tensor characteristics, landing heights of the treatment wells, variations in lithology, and high shear-wave velocity anisotropy strongly suggest that geological and geomechanical changes are most likely linked with anomalous microseismic behavior. The integrated analysis of treatment parameters, event locations, moment tensor, and geomechanics provides a comprehensive understanding of microseismic behavior in the Montney reservoir, presenting an interesting case study for microseismic analysis. Beyond investigating the cause of the event anomaly, this thesis contributes to the data processing field by improving automated processing algorithms for large, noisy microseismic data sets. The proposed fast matched filter workflow effectively detects potential microseismic events, outperforming traditional triggering-based detectors. Two time-frequency methods, the sparse Gabor transform and neighboring block thresholding, are investigated for signal enhancement and automated event detection. The sparse Gabor transform is more promising, effectively reducing noise while preserving signal characteristics. Furthermore, a quadratic interpolation algorithm is introduced to accelerate grid searches for event localization, providing a more efficient alternative to estimate event hypocenters. In conclusion, this thesis unravels the leading cause of abnormal microseismic behavior in the Montney treatment and contributes to the microseismic data processing field by improving automated event detection and location algorithms. The results have implications for optimizing hydraulic fracturing operations and enhancing the efficiency of automated processing algorithms for large data sets.

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