Inferring Fault Frictional and Reservoir Hydraulic Properties From Injection‐Induced Seismicity
DOI: 10.1002/2017gl075925
Abstract
Abstract Characterizing the rheological properties of faults and the evolution of fault friction during seismic slip are fundamental problems in geology and seismology. Recent increases in the frequency of induced earthquakes have intensified the need for robust methods to estimate fault properties. Here we present a novel approach for estimation of aquifer and fault properties, which combines coupled multiphysics simulation of injection‐induced seismicity with adaptive surrogate‐based Bayesian inversion. In a synthetic 2‐D model, we use aquifer pressure, ground displacements, and fault slip measurements during fluid injection to estimate the dynamic fault friction, the critical slip distance, and the aquifer permeability. Our forward model allows us to observe nonmonotonic evolutions of shear traction and slip on the fault resulting from the interplay of several physical mechanisms, including injection‐induced aquifer expansion, stress transfer along the fault, and slip‐induced stress relaxation. This interplay provides the basis for a successful joint inversion of induced seismicity, yielding well‐informed Bayesian posterior distributions of dynamic friction and critical slip. We uncover an inverse relationship between dynamic friction and critical slip distance, which is in agreement with the small dynamic friction and large critical slip reported during seismicity on mature faults.
Key Points We use a coupled flow‐geomechanics model of injection‐induced seismicity in a Bayesian inversion framework We perform probabilistic inference of dynamic friction properties of faults We identify stress transfer mechanisms from the interplay between poroelastic expansion and fault slip.
The authors are grateful for support from the Eni‐MIT Alliance research program. Additionally, J. Jagalur Mohan and Y. Marzouk acknowledge support for software infrastructure on Bayesian inversion from the National Science Foundation SI2‐SSI program, under award 1550487. The data used in this work are synthetic, produced by simulations that are fully described in the paper. Computations pertinent to the inversion process were performed using the open‐source uncertainty quantification software MUQ (http://muq.mit.edu). Any other details can be obtained from the corresponding author.