Lawrence Livermore National Laboratory (LLNL), Livermore, California, USA March 23, 2011

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1 Xianjin Yang, Robert Mellors, Abe Ramirez, Steve Hunter, Jeff Wagoner, David Camp, S. Julio Friedmann (LLNL), Livermore, California, USA March 23, 2011 Seismic reflection images and ERT synthetic model are courtesy of Dr. Feng Chen of ENN Sci. & Tech Co. Ltd, China This work was sponsored by the Institutional Science and Technology Office of LLNL-PRES This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA Lawrence Livermore National Security, LLC

2 Outline LLNL UCG Program Why Monitor? Process Monitoring Groundwater Monitoring Technologies being Developed at LLNL Subsidence monitoring Cavity geometry and deformation monitoring Conclusions 2

3 is a U.S. Department of Energy site applying science and technology to missions of national and global importance Approximately 6,800 employees with over 2000 technical staff and over 1000 Ph.D. s ~$1.5 billion annual budget World class experimental and computational facilities and capabilities Multi-disciplinary approach 60 years of government investment Basic Science National Ignition Facility Livermore Main Site 3 km 2 Energy and Environment Terascale Computing Facility Geothermal 3

4 LLNL has been active in UCG for decades Past Tests (1970 s and 1980 s) 16 field tests at Hoe Creek, Centralia, Rocky Mountain Invented CRIP process method Extensive instruments and monitoring Cavity excavation UCG models and simulations Current Activities Development of next generation UCG Simulator Development of next generation UCG monitoring capabilities UCG program planning and site selection Site characterization and conceptual design Multi-discipline UCG team of 12 scientists and engineers Silane igniter, Rocky Mountain Hoe Creek II Well Layout 4

5 Our UCG simulator will interface rigorous models of all near-field and far-field domains to predict UCG behavior Geomechanical Model Roof collapse Subsidence Stress changes & fracturing Wall Zone Model Reactions & drying Heat & mass transport Spallation Gas & solid compositions Thermal-Hydrological Model Groundwater flow & influx Pore pressure field Thermal response Simulator Manager Execution control Information exchange Data conversions & merging Boundary Evolution Model Cavity growth from wall reactions & structural failure Rubble zone geometry Roof movement due to structural failure Cavity Gas Model Reactions & gas composition Heat & mass transport Turbulent mixing Rubble Zone Model Reactions & drying Heat & mass transport Gas & solid compositions Far-field models Near-field models 5

6 Why Monitor? Operate more efficiently Informed process management High quality of syngas Stable syngas production Cost reduction Operate more responsibly Accelerate permitting Shield against liabilities Assure stakeholders Meet regulatory requirements o No groundwater contamination o No gas leakage and air pollution o No excessive subsidence o No health and safety incidents Air Syngas UCG Black Box? 6

7 UCG Process Monitoring Injection gas content (air, O 2, steam, ) Temperature in injection and production wells and in the reactor (max temperature ~ 1,100 C in the cavity) Pressure in injection and production wells and in the reactor (cavity pressure < hydrostatic, so water flows into the cavity) Flow rates of injection and product gases Syngas content (H 2, CO, CH 4, CO 2, ) Syngas heating value (syngas quality) 7

8 Rocky Mountain 1 (1986) A success story of groundwater monitoring program Outer ring - 9 monitoring wells Inner ring - 15 wells 19 wells ended in the coal seam 5 wells completed in the overburden and underburden. Groundwater pressure used for process control Groundwater chemistry Baseline - The full parameter suite (39 parameters from Alkalinity to Zinc) During the burn - compliance parameter suite (6 parameters) The post-burn monitoring and groundwater cleanup (the clean cavern concept) Compliance Suite Ammonia Boron Phenols Cyanide TDS TOC 8

9 Surface Subsidence Monitoring InSAR Tilt GPS Kelly et al.,

10 Cost-Effective Subsidence Monitoring by InSAR InSAR - Interferometric synthetic aperture radar, a satellite based technology for deformation monitoring Millimeter level of deformation accuracy Low-cost large-area coverage (~10,000 km 2 /scene, 8m 8m pixel) Coal mining collapse observed with InSAR Candall Canyon, Utah Contour Interval: 5 cm ALOS L-band PALSAR 6/ / InSAR image showing surface deformation over an on-going UCG project 10

11 Tilt Reliable real-time subsidence monitoring tool High data sampling rate Less interference from surface and atmospheric disturbances compared with InSAR Installation near surface or in the borehole LLNL was the original developer of this technology Tilt monitoring of enhance oil recovery (EOR) LLNL,

12 Cavity Geometry and Subsurface Deformation Radon measurements Seismic reflection Passive microseismic ERT Cross-borehole electromagnetic tomography Cross borehole seismic tomography Tracer tests 12

13 Seismic Reflection detect cavity, affected coal and coal residual at Wulanchabu UCG site, China Coal residual affected coal Coal residual affected coal cavity cavity cavity cavity Data courtesy of ENN/Xinao Group, China Expensive monitoring tool 13

14 Emanation (%) Monitoring by Radon Measurements Rn emanation factor increases dramatically when temperature is greater than 700 C. Radon solubility decreases as the temperature increases. Detect the burn-front and estimate its migration speed Used by ENN at Wulanchabu, China (Dr. Feng Chen) Rn Emanation Factor vs. Temperature Temperature C Chen, Q.F., Liang, J., Yu, L., and Guo, Q.Y., 2008 Rn detector (Xue and Cui, 2004) 14

15 Passive Microseismic Monitoring Locate fracturing and spalling above the burn front Proven in micro-earthquake detection in an enhanced geothermal system. Locate the roof collapse Processing algorithms leverage extensive LLNL expertise in seismic monitoring Advanced signal processing for improved event detection with lower threshold and sophisticated location algorithm Salton Sea Geothermal Field, Jingbo Wang et al, (LLNL, 2011) 15

16 Electrical Resistivity Tomography (ERT) can be the least expensive UCG cavity monitoring tool VEA4 VEA3 Resistivity is a function of temperature, air/fluid saturation, salinity, porosity, Fully autonomous 3D data collection VEA1 VEA2 Fully autonomous data processing Inexpensive sensors (metal stakes) V V ERT arrays can be collocated with thermocouple or groundwater monitoring wells I V V 3D ERT Layout UCG Monitoring Objectives by ERT Locate burn front Delineate cavity boundary Resolve temperature distribution VEA Vertical Electrode Array I V Transmitter injects current into the earth Receiver measures voltage 16

17 ERT is a proven technology for monitoring of subsurface processes Depth (m) ERT Monitoring of steam enhanced remediation 2/18/99 2/23/99 3/5/99 3/25/ m Temperature changes Labrecque and X. Yang, 2001 Air saturation changes ERT Monitoring of in-situ air-sparging X. Yang, 2001 ERT monitoring of CO 2 sequestration CO 2 saturation changes ERT array at 3,000m depth ERT Monitoring of water infiltration Moisture content changes C. Carrigan et al, 2010, LLNL Day 9 Day 33 X. Yang, 1999, Ph.D. thesis 17

18 UCG Cavity Delineation w/ ERT Synthetic Data Test Synthetic resistivity model was based on the UCG site geology of Wulanchabu (Dr. Feng Chen, ENN) Coal seams Synthetic Model A cavity is embedded in the lower coal seams Our time lapse inversion algorithm resolves changes in the ground. ERT reveals the cavity clearly 18

19 Stochastic inversion of multi-physics data provides better resolution of a UCG cavity On-going stochastic inversion projects for oil companies Integrate flow and reactive transport models, seismic and water chemistry data to resolve CO 2 saturation and reservoir porosity and permeability Joint inversion of InSAR, tilt, and injection volume for reservoir performance monitoring UCG monitoring with stochastic inversion Joint inversion of ERT, temperature and cavity volume and other prior data for better resolution Reduce and quantify uncertainty of the solution 19

20 Conclusions Goals of UCG Monitoring Operate more efficiently Operate more responsibly Monitoring strategy Spatial coverage: o Laterally - near, medium and far monitoring wells o Vertically in, above and below coal seam Temporal coverage: baseline, during burn and post-burn New monitoring methods being developed UCG Simulator ERT Data integration with stochastic inversion Passive microseismic InSAR and tilt 20

21 Thank you! Rocky Mountain I cavity by post-burn drilling (Oliver, Lingdblom and Covell, 1991) 21

22 What can we control? Before operations Choice of site (coal, rock, depth, dip, hydrology, ) Module design, mine plan, dewatering well locations Construction details Startup details Operations Injection rate, composition, temperature, pressure Liquid pumping locations and rates Where to inject and produce When to stop Shutdown When and how to clean and manage the cavities and site Monitoring informs these decisions 22