Deformation modeling over geologic time : from constructing pressure evolution to predicting strike-slip fault deformation

Forces from small earthquakes, moving cars, or even footsteps during a short period can cause deformation of the earth's crust to varying degrees, but these actions do not cause large-scale permanent damage to the crust. In contrast, forces from geological processes over millions of years cause rocks to bend and break permanently. The work presented in this dissertation presents two geomechanical modeling techniques, finite element method, and machine learning, to improve how we model the second kind of deformation: large-scale deformation. Specifically, the proposed modeling techniques enhance our understanding of the evolution of pore pressure and stress (part one of the dissertation) and the kinematic efficiency of the strike-slip fault system (part two of the dissertation). In terms of the evolution of pore pressure, predictions based on basin and petroleum system modeling (BPSM) still have limitations, especially those models with underlying assumptions that exclude inelasticity and non-vertical deformation. Hence, we offer an alternative modeling approach by incorporating a fully-coupled hydromechanical simulator that includes inelasticity, enabling us to track the dynamic properties of rock over time. Regarding the strike-slip fault system, the current understanding of off-fault deformation behavior is limited because quantifying individual control (i.e., fault geometry, roughness, and connectivity) neglects the effects of the inter-relationship of these three controls on fault behaviors. Hence, we offer an alternative solution by harnessing a machine learning algorithm that can relate all relevant parameters in higher dimensions to estimate off-fault deformation. The first part of this dissertation explains how this work improves predictions of the evolutionary pore pressure and stress. It consists of two chapters and addresses the following research objectives: (1) integrating advanced geomechanical concepts into basin and petroleum system modeling by incorporating more realistic assumptions, e.g., inelastic constitutive relation and non-vertical stress; (2) constructing a model that captures evolving properties of shale rocks as a function of stress and pore pressure for different levels of model complexity, such as fracturing, geometry, and boundary conditions; and (3) investigating stress, pore pressure generation, and pore pressure dissipation in a tectonically complex region by using a nonlinear stress-induced upscaled permeability. The second part of this dissertation explains the improvement in predicting the kinematic efficiency of strike-slip fault systems. It consists of one chapter and addresses the following research objectives: (1) utilizing a comprehensive labeled dataset under various loading conditions of simulated strike-slip faults to build a predictive model of off-fault deformation; (2) searching for the most appropriate architecture, loss functions, and hyperparameters that maximize the performance of the convolutional neural network (CNN) model on an unseen fault dataset; and (3) testing the hypothesis that the trained CNN model can estimate off-fault deformation that is consistent with geologic observations

Keywords: Geological and Environmental Sciences.
Advisor: Graham S. A. (Stephan Alan)
Scheirer Allegra Hosford
Mukerji Tapan
Zoback Mark D.