Overview and Preliminary Results from the PoroTomo project at Brady Hot Springs, Nevada: Poroelastic Tomography by Adjoint Inverse Modeling of Data from Seismology, Geodesy, and Hydrology

摘要

In the geothermal field at Brady Hot Springs, Nevada, subsidence occurs over an elliptical area that is ～4 km by ～1.5 km. Highly permeable conduits along faults appear to channel fluids from shallow aquifers to the deep geothermal reservoir tapped by the production wells. Results from inverse modeling suggest that the deformation is a result of volumetric contraction in units with depth less than 600 m [Ali et al., 2016]. Characterizing such structures in terms of their rock mechanical properties is essential to successful operations of Enhanced Geothermal Systems (EGS). The goal of the PoroTomo project is to assess an integrated technology for characterizing and monitoring changes in the rock mechanical properties of an EGS reservoir in three dimensions with a spatial resolution better than 50 meters. The targeted rock mechanical properties include: saturation, porosity, Young's modulus, Poisson's ratio, and density, all of which are critically important characteristics of a viable EGS reservoir. In March 2016, we deployed the integrated technology in a 1500-by-500-by-400-meter volume at Brady Hot Springs. The 15-day deployment included four distinct time intervals with intentional manipulations of the pumping rates in injection and production wells. The data set includes: active seismic sources, fiber-optic cables for Distributed Acoustic Sensing (DAS) and Distributed Temperature Sensing (DTS) arranged vertically in a borehole to ～400 m depth and horizontally in a trench 8700 m in length and 0.5 m in depth; 244 seismometers on the surface, three pressure sensors in observation wells, continuous geodetic measurements at three GPS stations, and seven images for interferometric synthetic aperture radar (InSAR). To account for the mechanical behavior of both the rock and the fluids, we are developing numerical models for the 3-dimensional distribution of the material properties. The work presented herein was funded in part by the Office of Energy Efficiency and Renewable Energy (EERE), U.S. Department of Energy, under Award Number DE-EE0006760.