Spring-slider and finite element modeling of microseismic events and fault slip during hydraulic fracturing

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Hydraulic fracturing increases reservoir permeability by opening fractures and triggering slip on natural fractures and faults. While seismic slip of small faults or fault patches is detectable as microseismic events, the role of aseismic slip is poorly understood. From a modeling standpoint, geomechanical analysis using the Coulomb criterion can determine if faults slip but not whether slip is seismic or aseismic. Here we propose a computational methodology to predict fault slip, and whether slip is seismic or aseismic, using rate-and-state friction. To avoid computational costs associated with resolving small faults, we use the spring-slider idealization that treats faults as points. Interaction between faults is neglected. The method is applied to study fault slip from a hydraulic fracture that grows past a fault, without intersecting it. We represent the hydraulic fracture stressing using an asymptotic expansion of stresses around the tip of a tensile crack. We investigate the effect of fault length, orientation, and distance from the hydraulic fracture. For velocity-weakening faults with stiffness smaller than a critical stiffness, slip is seismic, whereas faults with stiffness greater than the critical stiffness slip aseismically. Furthermore, we compare the spring-slider idealization with a finite element analysis that resolves spatially variable slip. The spring-slider idealization provides reasonably accurate predictions of moment and even moment-rate history, especially for faults having stiffness close to or larger than the critical stiffness. Differences appear for large faults where rupture propagation is important, though differences might still be negligible for many applications. The spring-slider methodology could be applied to model statistics of microseismicity and aseismic slip on a population of small faults in a reservoir by populating a stochastic fracture network with spring sliders having frictional properties drawn from a statistical characterization based on well logs and experimental correlations between friction and rock properties


Type of resource text
Form electronic resource; remote; computer; online resource
Extent 1 online resource
Place California
Place [Stanford, California]
Publisher [Stanford University]
Copyright date 2020; ©2020
Publication date 2020; 2020
Issuance monographic
Language English


Author Kashefi, Ali
Degree committee member Dunham, Eric
Thesis advisor Dunham, Eric
Associated with Stanford University, Department of Mechanical Engineering.


Genre Theses
Genre Text

Bibliographic information

Statement of responsibility Ali Kashefi
Note Submitted to the Department of Mechanical Engineering
Thesis Thesis Engineering Stanford University 2020
Location electronic resource

Access conditions

© 2020 by Ali Kashefi
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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