Integration of DAS (distributed acoustic sensing) vertical seismic profile and geostatistically modeled lithology data to characterize an enhanced geothermal system.

In March 2016, a 4-week field data acquisition took place at Brady's Natural Lab (BNL), an enhanced geothermal system (EGS) in Fallan, NV. During these 4 weeks, a vibe truck executed 6,633 sweeps, recorded by nodal seismometers, horizontal distributed acoustic sensing (DAS) cable, and 400 meters of vertical DAS cable. DAS provides lower signal to noise ratio than traditional geophones but better spatial resolution. The analysis of DAS VSP included Fourier transform, and filtering to remove all up-going energy. Thus, allowing for accurate first arrival picking. We present an example of the Gradual Deformation Method (GDM) using DAS VSP and lithological data to produce a distribution of valid velocity models of BNL. GDM generates continuous perturbations of prior model realizations seeking the best match to the data (i.e. minimize the misfit). Prior model realizations honoring the lithological data were created using sequential Gaussian simulation, a commonly used noniterative geostatistical method. Unlike least-squares-based methods of inversion, GDM readily incorporates a priori information, such as a variogram calculated from well-based lithology information. Additionally, by producing a distribution of models, as opposed to one optimal model, GDM allows for uncertainty quantification. This project aims at assessing the integrated technologies ability to monitor changes in the water table (possibly to one meter resolution) by exploiting the dependence of seismic wave velocities on water saturation of the subsurface. This project, which was funded in part by the National Science Foundation, is a part of the PoroTomo project, funded by a grant from the U.S. Department of Energy.