Monitoring with Time-lapse 3D VSPs at the Illinois Basin Decatur Project Marcia L. Couëslan Senior Geophysicist 18 September 2012 www.slb.com/carbonservices
Acknowledgements Rob Finley, Sallie Greenberg, and Hannes E. Leetaru Illinois State Geological Survey US Department of Energy (DOE) National Energy Technology Laboratory (NETL) Scott Totten, Billy Hancock, and Jitendra Gulati, Schlumberger WesternGeco Kevin Fisher, Schlumberger DCS Valerie Smith and Ozgur Senel, Schlumberger Carbon Services 2
2012 Schlumberger. All rights reserved. An asterisk is used throughout this presentation to denote a mark of Schlumberger. Other company, product, and service names are the properties of their respective owners.
Outline 1. MVA and Time-lapse 3D VSP Objectives 2. Fluid Substitution Modeling 3. Acquisition Challenges 4. Acquisition Parameters 5. Processing and Data Comparisons 6. Conclusions 7. Future Plans 4
MVA and Time-lapse 3D VSP Data Objectives A Monitoring, Verification, and Accounting (MVA) program should: Fulfill existing regulatory requirements Provide information on CO 2 plume development over time Demonstrate containment of CO 2 within the storage formation Provide data to verify and update models/simulations Time-lapse 3D vertical seismic profile (VSP) data to monitor CO 2 plume development around the Injector well 5
Fluid Substitution Modeling: CO 2 Saturations Depth Sco2 at 60.. Sco2 at 250.. Sco2 at 540.. Sco2 at 740.. Sco2 at 1000.... v/v v/v v/v v/v v/v 6969.50 0.00 0.00 0.00 0.00 0.00 6970.00 0.29 0.27 0.01 0.02 0.00 6973.54 0.31 0.20 0.08 0.05 0.03 6977.08 0.16 0.10 0.08 0.05 0.03 6980.63 0.19 0.16 0.15 0.06 0.04 6984.17 0.30 0.15 0.10 0.07 0.04 6987.71 0.31 0.22 0.17 0.12 0.04 6991.25 0.33 0.30 0.26 0.15 0.03 6994.79 0.35 0.28 0.26 0.18 0.01 6998.33 0.34 0.28 0.24 0.18 0.00 7001.88 0.34 0.29 0.07 0.01 0.00 7005.42 0.36 0.29 0.09 0.02 0.00 7008.96 0.36 0.23 0.10 0.02 0.00 7012.50 0.35 0.16 0.00 0.00 0.00 7016.04 0.36 0.23 0.00 0.00 0.00 7019.58 0.36 0.21 0.01 0.00 0.00 7023.13 0.20 0.19 0.15 0.00 0.00 7026.67 0.25 0.20 0.15 0.00 0.00 7030.21 0.31 0.24 0.17 0.00 0.00 7033.75 0.33 0.25 0.07 0.00 0.00 7037.29 0.33 0.25 0.01 0.00 0.00 7040.83 0.34 0.26 0.00 0.00 0.00 7044.38 0.35 0.19 0.00 0.00 0.00 7047.92 0.36 0.04 0.00 0.00 0.00 7051.46 0.26 0.00 0.00 0.00 0.00 7052.00 0.00 0.00 0.00 0.00 0.00 6
Fluid Substitution Modeling: Before CO 2 Time (ms) Distance from CCS#1 60 ft 250 ft 540 ft 740 ft 1000 ft Depth (ft) 930 6800 940 950 6900 960 7000 970 980 990 Granite Wash Granite Wash Granite Wash Granite Wash Granite Wash 7100 7200 7
Fluid Substitution Modeling: After CO 2 Time (ms) Distance from CCS#1 60 ft 250 ft 540 ft 740 ft 1000 ft Depth (ft) 930 6800 940 950 6900 960 7000 970 980 990 Granite Wash Granite Wash Granite Wash Granite Wash Granite Wash 7100 7200 8
Fluid Substitution Modeling: Difference Time (ms) Distance from CCS#1 60 ft 250 ft 540 ft 740 ft 1000 ft Depth (ft) 930 6800 940 950 6900 960 7000 970 980 990 7100 7200 9
Acquisition Challenges Site access and industrial infrastructure can result in: Holes in the acquisition footprint that cause artifacts in baseline datasets Noise contamination in the dataset Data repeatability is essential to the success of time-lapse seismic surveys Factors affecting data repeatability from survey to survey include: Ground conditions Source and receiver locations Receiver response Non-repeatable noise A permanent 31-level geophone array was installed in Geophysical Well #1 to eliminate receiver positioning errors 10
Acquisition Challenges: Acquisition Footprint 1174000 1173000 1172000 Source Y 1171000 1170000 1169000 1168000 1167000 Walkaway Source Locations 3D VSP Source Locations Pre-plot Positions GW#1 Well CCS#1 Well VW#1 Well 1166000 1165000 336000 338000 340000 342000 344000 346000 Source X 348000 11
Acquisition Challenges: Acquisition Footprint -3000-2000 -1000 0 1000 2000 3000 3000 3500 4000 Depth (ft) 4500 5000 5500 6000 6500 7000 12
Acquisition Challenges: Signal Interference Thick concrete surface creating source generated noise. Caterpillar Caterpillar CCS#1 Geophysical Well Electrical noise from power lines and 60Hz transformer plant ADM Road traffic noise due to tractor trailers at ADM. Related noise from ADM plant 13
Acquisition Parameters ~74,000 tonnes of CO 2 had been injected at the time of Monitor 1 Small amount to detect seismically Monitor 1 timed to coincide with the first round of fluid sampling and time-lapse RST* reservoir saturation tool logging 3D VSP Survey Name Survey Date Ground Conditions Vibrator Sweep Repeated Shots Baseline 1 Jan 27-30, 2010 Wet 2 100 Hz Baseline 2 Apr 12-14, 2011 Dry 8 120 Hz 385 Monitor 1 Feb 11-12, 2012 Frozen dry 8 120 Hz 467 14
Acquisition Parameters: Baseline 2 and Monitor 1 Map of Co-located Shot Points 1173000 1172000 Source Y 1171000 1170000 1169000 Baseline 2 Monitor 1 GW#1 Well CCS#1 Well VW#1 Well 1168000 340000 342000 344000 346000 Source X 15
Acquisition Parameters: Distance Histogram of Co-located Shot Points 140 120 Number of Co-located Shots 100 80 60 40 20 0 0 5 10 15 20 25 30 35 40 45 50 55 Distance Between Sources for Both Surveys 16
Processing and Data Comparisons: Processing Highlights Baseline 2 and Monitor 1 co-processed after co-located shots selected Data cross-equalized to remove small amplitude/phase variations and time shifts between the two datasets Non-rigid matching (NRM) applied after migration to reduce time-lapse noise Normalized Root Mean Square (NRMS) repeatability metric used during processing to calculate data repeatability at several points Tends to be sensitive to differences in amplitude, phase, and time shifts 17
Processing and Data Comparisons: NRMS of Shots Before and After Cross-Equalization Source Y 1172000 1171000 1170000 1169000 1168000 Before Cross-Equalization 340000 342000 344000 346000 GW#1 Well CCS#1 Well VW#1 Well Source X NRMS 0 5 9 14 19 23 28 33 38 42 47 52 56 61 66 70 75 80 84 89 94 98 103 108 113 117 122 127 131 136 140 145 150 340000 After Cross-Equalization 342000 344000 346000 Source X 18
Processing and Data Comparisons: Final Migrated Image with NRM Baseline 2 Image with NRM Monitor 1 Image with NRM Difference Image 2000 2000 3000 3000 4000 4000 5000 5000 6000 6000 7000 7000 Depth (ft) -1000-500 0 500 1000-1000 -500 0 500 1000-1000 -500 0 500 1000 Offset (ft) 19
Processing and Data Comparisons: NRMS Maps NRMS computed between 5000 5500 ft depth NRMS NRMS computed between 6950 7100 ft depth NRMS 800 0 12.5 800 0 12.5 600 25 600 25 400 37.5 400 37.5 200 0 50 62.5 75 200 0 50 62.5 75-200 87.5-200 87.5 100-800 -400 0 400 800-800 -400 0 400 800 100 GW#1 Well CCS#1 Well VW#1 Well 20
Processing and Data Comparisons: NRMS Maps NRMS computed between 5000 5500 ft depth NRMS NRMS computed between 6950 7100 ft depth Hit Count 800 0 12.5 800 1 600 25 600 5 400 200 37.5 50 62.5 400 200 10 0 75 0 15-200 87.5-200 100-800 -400 0 400 800-800 -400 0 400 800 GW#1 Well CCS#1 Well VW#1 Well 21
Conclusions Baseline 2 and Monitor 1 had the most similar acquisition parameters and ground conditions and were used for time-lapse analysis Differences directly attributable to CO 2 injection difficult to identify on the difference displays Only ~70,000 of tonnes had been injected at the time of Monitor 1 The associated time-lapse signal may be below the noise levels in the data NRMS repeatability metrics show that: Baseline 2 and Monitor 1 datasets are very repeatable above the Mount Simon formation Higher NRMS values through the injection zone may be suggestive of CO 2 plume development Caution about inferring too much from these results without further interpretation and correlation to other data types 22