Browsing by Author "Eaton, David WS"

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    Geological Susceptibility to Hydraulic Fracturing-Induced Seismicity in the Montney Formation
    (2022-05) Wozniakowska, Paulina Gabriela; Eaton, David WS; Gilbert, Hersh Joseph; Trad, Daniel Osvaldo; Pedersen, Per Kent; Chen, Zhangxing; Eberhardt, Erik
    This thesis focuses on induced (anthropogenic) seismicity related to hydraulic fracturing operations in the Montney Formation - a geological unit of Triassic age located in the Western Canada Sedimentary Basin. Originally a conventional oil and gas play, the Montney Formation is currently one of the most prolific unconventional resource plays worldwide. Documented cases of induced seismicity in the Montney play occur in distinct clusters, indicative of local variability of factors influencing the seismic activation potential (SAP). Notably, virtually all induced seismicity related to hydraulic fracturing, to date, has occurred in British Columbia despite similar levels of industrial activity in Alberta. This implies that geological trends may have a more significant impact on SAP than operational factors. This thesis presents several new methodologies for investigating the complex interplay between subsurface conditions and induced seismicity distribution. Three independent workflows, based on machine learning-based analysis, structural interpretation, and statistical inference, respectively, were developed to evaluate hypotheses regarding the influence of geological, geomechanical and structural controls of hydraulic fracturing-induced seismicity in the Montney Formation. First, a machine learning model was used to identify areas within the Montney that are characterized by the highest geological susceptibility to induced seismicity. The results suggest that distance to the Cordilleran deformation front and injection depth are the most important factors influencing the observed seismicity trends. Next, a multi-step workflow based on trend-surface analysis combined with geophysical data interpretation allowed major structural trends (structural corridors) to be delineated throughout the Montney play. The results of machine learning and structural interpretation were used to formulate hypotheses regarding geological factors influencing observed cluster characteristics of seismicity in the Montney. These hypotheses were independently tested using SimSeis – a newly developed tool for statistical inference based on a stochastic simulation approach. Using this tool, sets of synthetic catalogs are generated according to assumed spatial relationship(s) between geological susceptibility and/or mapped structural corridors and further compared against a Null hypothesis, corresponding to a random spatial association of induced seismicity with hydraulically fractured wells. While each of the alternative models performed significantly better than the Null hypothesis, a machine-learning model based on geological susceptibility achieved the best results. SimSeis is customizable and can be applied to investigate mechanisms that influence the distribution of induced seismicity distribution in other unconventional plays and thus enhance currently existing seismic-risk mitigation strategies.
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    The Stochastic Characterization of Natural and Hydraulic Fractures in Unconventional Reservoirs
    (2023-01-13) McKean, Scott Harold; Dettmer, Jan; Priest, Jeffrey Alan; Eaton, David WS; Wan, Richard G; Davidsen, Joern; Dusseault, Maurice Bernard
    An informed understanding of the subsurface is critical for mining, tunnelling, wastewater injection, carbon sequestration, and hydraulic fracturing (HF). Unfortunately, subsurface characterization is full of uncertainty. This is especially true when trying to understand or mitigate induced seismicity (IS), or the triggering of earthquakes by anthropogenic processes. This research focuses on hydraulic fracturing caused IS in unconventional reservoirs. The interaction between HF and IS is complicated by geomechanical variability and the presence of natural fractures. Our research accomplishes three objectives. We study natural fractures through outcrop analogues, discrete fracture network modelling, and induced seismicity. We characterise geomechanical rock properties along with their uncertainty. Finally, we develop a repeatable and scalable workflow to separate HF microseismicity from IS in order to characterise hydraulic and natural fractures. The research focuses on the Duvernay Formation in the Western Canadian Sedimentary Basin. An alpine outcrop equivalent of the Duvernay is characterized to quantify small- and large-scale fractures. This study reveals irreducible small-scale heterogeneity, as well as discernable patterns in large-scale fractures. Statistics and geostatistics are used to investigate elastic moduli and brittleness. The work shows how measurement and modelling uncertainity can propogate from laboratory to basin-scale. It reveals fundamental differences between elastic moduli and brittleness and shows why holistic modelling and uncertainty quantification approaches are essential to understanding and modelling the subsurface. We then introduce methods for the separation of HF microseismicity from IS. Physics-based clustering and Bayesian inference of diffusivity are used for the separation. This permits HF characterization which highlights the large variability of diffusivity and HF dimensions. We show why physics-based constraints are essential for microseismic analysis. The separated IS allows us to infer information about the natural fractures linked with induced seismicity. Application of the methods to the Duvernay shows HFs propogating directly into natural fractures and rotating away from the maximum principal stress direction towards natural fractures. Discrete fracture network modelling and parameter estimation is able to constrain the architecture of multiple fracture sets. We demonstrate that aseismic fracture sets are essential for establishing pressure connectivity and displaying IS.