Monitoring with Time-lapse 3D VSPs at the Illinois Basin Decatur Project

Transcription

1 Monitoring with Time-lapse 3D VSPs at the Illinois Basin Decatur Project Marcia L. Couëslan Senior Geophysicist 18 September

2 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

3 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.

4 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

5 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

6 Fluid Substitution Modeling: CO 2 Saturations Depth Sco2 at 60.. Sco2 at Sco2 at Sco2 at Sco2 at v/v v/v v/v v/v v/v

7 Fluid Substitution Modeling: Before CO 2 Time (ms) Distance from CCS#1 60 ft 250 ft 540 ft 740 ft 1000 ft Depth (ft) Granite Wash Granite Wash Granite Wash Granite Wash Granite Wash

8 Fluid Substitution Modeling: After CO 2 Time (ms) Distance from CCS#1 60 ft 250 ft 540 ft 740 ft 1000 ft Depth (ft) Granite Wash Granite Wash Granite Wash Granite Wash Granite Wash

9 Fluid Substitution Modeling: Difference Time (ms) Distance from CCS#1 60 ft 250 ft 540 ft 740 ft 1000 ft Depth (ft)

10 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

11 Acquisition Challenges: Acquisition Footprint Source Y Walkaway Source Locations 3D VSP Source Locations Pre-plot Positions GW#1 Well CCS#1 Well VW#1 Well Source X

12 Acquisition Challenges: Acquisition Footprint Depth (ft)

13 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

14 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 Hz Baseline 2 Apr 12-14, 2011 Dry Hz 385 Monitor 1 Feb 11-12, 2012 Frozen dry Hz

15 Acquisition Parameters: Baseline 2 and Monitor 1 Map of Co-located Shot Points Source Y Baseline 2 Monitor 1 GW#1 Well CCS#1 Well VW#1 Well Source X 15

16 Acquisition Parameters: Distance Histogram of Co-located Shot Points Number of Co-located Shots Distance Between Sources for Both Surveys 16

17 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

18 Processing and Data Comparisons: NRMS of Shots Before and After Cross-Equalization Source Y Before Cross-Equalization GW#1 Well CCS#1 Well VW#1 Well Source X NRMS After Cross-Equalization Source X 18

19 Processing and Data Comparisons: Final Migrated Image with NRM Baseline 2 Image with NRM Monitor 1 Image with NRM Difference Image Depth (ft) Offset (ft) 19

20 Processing and Data Comparisons: NRMS Maps NRMS computed between ft depth NRMS NRMS computed between ft depth NRMS GW#1 Well CCS#1 Well VW#1 Well 20

21 Processing and Data Comparisons: NRMS Maps NRMS computed between ft depth NRMS NRMS computed between ft depth Hit Count GW#1 Well CCS#1 Well VW#1 Well 21

22 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