Dettmer, JanPriest, Jeffrey AlanMcKean, Scott Harold2023-02-162023-02-162023-01-13McKean, S. H. (2023). The stochastic characterization of natural and hydraulic fractures in unconventional reservoirs (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.http://hdl.handle.net/1880/115865https://dx.doi.org/10.11575/PRISM/40756An 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.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.natural fracturesmachine learninginduced seismicitygeostatisticsmicroseismicBayesian statisticsGeologyStatisticsEnergyEngineeringdoctoral thesis