PoroTomo: Poroelastic Tomography by Adjoint Inverse Modeling of Data from Seismology, Geodesy, and Hydrology

Quantifying reservoir complexity is a grand challenge in characterizing and managing Enhanced Geothermal Systems (EGS). The complexity of a fracture-dominated reservoir is evident at Brady Hot Springs, Nevada, where highly permeable conduits along faults appear to channel fluids from shallow aquifers to the deep geothermal reservoir tapped by the production wells, as revealed by poro-elastic modeling of deformation mapped by satellite geodesy. The hypothesis of such structurally controlled conduits of high permeability is also consistent with the fault scarps aligned with hydrothermal features. Characterizing such structures in terms of their “rock mechanical properties” is essential to successful EGS operations.

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. Estimating these parameters and their uncertainties will contribute to the overarching objective of characterizing the reservoir in terms of its effective permeability and/or fracture transmissivity. By performing inverse modeling with a Bayesian, adjoint-based approach, the integrated tech­nology is analyzing three data sets: (1) seismic waveforms; (2) ground deformation measured by the Global Positioning System (GPS) and Interferometric Synthetic Aperture Radar (InSAR); and (3) time series of hydraulic pressure, flow, and temperature measured in wells. The proposed project is driving the methods for analyzing each of these data sets individually from Technology Readiness Level (TRL) 2 or 3 to TRL 4 or 5 and the integrated technology to TRL 3.

Phase I focused on validating innovative computational analysis techniques by adapting and applying them to existing data sets. To design a test of a prototype of the integrated technology, the team calculated the expected values of the technology performance metric — resolution — that was successfully evaluated in a Stage Gate Review.

Phase II demonstrated a prototype in a ~1500-by-500-by-400-meter natural laboratory at Brady, where the material properties respond to field operations. The prototype deployment included four distinct time intervals, separated by changes in hydrological conditions caused by intentionally manipulating the pumping rates in injection and production wells. The technology is quantifying the resulting temporal changes in material properties and assess their statistical uncertainty and resolution. The deployment included: active seismic sources, temperature-resistant fiber-optic cables for Distributed Acoustic Sensing (DAS) and Distributed Temperature Sensing (DTS) to ~400 m depth, ~9000 m of DAS cable and ~240 seismometers on the surface, 5 pressure sensors in observation wells, continuous geodetic measurements at 3 GPS stations, and at least one InSAR acquisition every ~11 days.

To account for the mechanical behavior of both the rock and the fluids, the project is using physical principles, including the Biot theory of poroelasticity [e.g., Biot 1949; Wang, 2000; Morency et al. 2009], and fracture mechanics, to constrain numerical models for the 3-D distribution of the material properties.

Data generated during the project have been submitted to the DOE Geothermal Data Registry (GDR) and the National Geothermal Data System (NGDS). Data sets are also available here.

The expected outcome of the project is a validated small-scale prototype that will provide the technical specifications required to deploy the technology in a deeper, full-scale EGS field or in the proposed Frontier Observatory for Research in Geothermal Energy (FORGE). The project is addressing the need for “an ‘integrated’ approach to the complexity of the geothermal phenomena [that] is still lacking,” according to Franco and Vaccaro (2014).

The PoroTomo project is funded by a grant from The Office of Energy Efficiency and Renewable Energy (EERE) of the U.S. Department of Energy. The PoroTomo team includes scientists and engineers from: University of Wisconsin-Madison Department of Geoscience (1), Ormat Technologies, Inc. (2), Silixa Ltd. (3), University of Nevada-Reno (4), Temple University (5), Lawrence Livermore National Laboratory (6), and Lawrence Berkeley National Laboratory (7).

PoroTomo team on a hill overlooking the natural laboratory. [Photo by Dan Koetke using Neal Lord’s camera 2014/10/16]
PoroTomo team members, including (from left to right), Dante Fratta1, David Lim1, Neal Lord1, Kurt Feigl1, Janice Lopeman2, Joe Greer3, Thomas Coleman3, Mike Cardiff1, Christina Morency6, Michelle Robertson7, John Akerly2, Eric Matzel6, Bill Foxall7, Bret Pecorora4, Chelsea Lancelle1, Corné Kreemer4, Martin Schoenball5, Paul Spielman2.

References:

Ali, S. T., J. Akerley, E. C. Baluyut, M. Cardiff, N. C. Davatzes, K. L. Feigl, W. Foxall, D. Fratta, R. J. Mellors, P. Spielman, H. F. Wang, and E. Zemach (2016), Time-series analysis of surface deformation at Brady Hot Springs geothermal field (Nevada) using interferometric synthetic aperture radar, Geothermics, 61, 114-120.
http://dx.doi.org/10.1016/j.geothermics.2016.01.008

Biot, M. A. (1941), General theory of three-dimensional consolidation, Journal of Applied Physics, 12, 155-164.

Feigl, K. L., and PoroTomo_Team (Year), 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, paper presented at Stanford Geothermal Workshop, Stanford University.
https://pangea.stanford.edu/ERE/db/GeoConf/papers/SGW/2017/Feigl.pdf

Franco, A., and M. Vaccaro (2014), Numerical simulation of geothermal reservoirs for the sustainable design of energy plants: A review, Renewable and Sustainable Energy Reviews, 30, 987-1002.
http://www.sciencedirect.com/science/article/pii/S1364032113007909

Gordon, S. (2015), Better data tools for a bigger geothermal future.
https://www.engr.wisc.edu/better-data-tools-for-a-bigger-geothermal-future/

Lord, N., H. Wang, and D. Fratta (2016), A source-synchronous filter for uncorrelated receiver traces from a swept-frequency seismic source, Geophysics, 81, P47-P55.
http://dx.doi.org/10.1190/geo2015-0324.1

Morency, C., Y. Luo, and J. Tromp (2009), Finite-frequency kernels for wave propagation in porous media based upon adjoint methods, Geophys. J. Int., 179, 1148-1168.
http://gji.oxfordjournals.org/content/179/2/1148.

Wang, H. F. (2000), Theory of poroelasticity with applications to geomechanics and hydrology, Princeton University Press.

Zeng, X., C. Lancelle, C. Thurber, D. Fratta, H. Wang, A. Chalari, and A. Clarke (2017), Properties of Noise Cross Correlation Functions Obtained from a Distributed Acoustic Sensing (DAS) Array at Garner Valley, California, Bull. Seismol. Soc. Am., 107. http://dx.doi.org/10.1785/0120160168