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Ekofisk Revisited G. A. Jones 1, D. G. Raymer 2, K. Chambers 1 and J-M. Kendall 1 1. University of Bristol; 2. Schlumberger Cambridge Research Reservoir.

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Presentation on theme: "Ekofisk Revisited G. A. Jones 1, D. G. Raymer 2, K. Chambers 1 and J-M. Kendall 1 1. University of Bristol; 2. Schlumberger Cambridge Research Reservoir."— Presentation transcript:

1 Ekofisk Revisited G. A. Jones 1, D. G. Raymer 2, K. Chambers 1 and J-M. Kendall 1 1. University of Bristol; 2. Schlumberger Cambridge Research Reservoir monitoring seismic experiment Hypocentre determination using grid search methods Monte Carlo hypocentre error analysis Multiplet relocation Fault reactivation and production induced deformation

2 Challenges in microseismicity Anisotropy and shear wave splitting Focal mechanism Fault/fracture identification Repeating earthquakes

3 The Ekofisk reservoir Located in the central Graben of the Norwegian North Sea Field discovered in 1969 and was the first economically viable chalk reservoir Sea-floor subsidence ~30cm/year The challenge: to monitor subsidence, compaction and its effects on reservoir permeability

4 The Ekofisk microseismic experiment One of the 1 st microseismic monitoring experiments experiments in oil industry Vertical downhole geophone array of 6, 3 component receivers spaced 20 meters Geophones located in producing part of reservoir 4490 events triggered over the 18 day experiment in April 1997

5 Signal characteristics

6 Event evolution with time

7

8 Velocity model construction

9 Arrival time picking and polarisations Velocity (µm) P S

10 Polarisation analysis – refining position and azimuth Jones et al. in press

11 Use the array-velocity model symmetry to reduce problem from 3D to 2D Simplification of the problem allows for a dense grid search procedure to be implemented

12 Which hypocentre method? Which minimisation function to use? –P- and S-times individually? –Differential S-P? –All possible combinations of differential arrival times? Or use EDT surfaces?

13 EDT tolerance selection

14 Arrival Time Monte Carlo Test S-P All pairs EDT

15 Velocity Model Monte Carlo Test S-P All pairs EDT

16 Summary of Monte Carlo Analysis r tt (m)z tt (m)r vel (m)z vel (m) S-P 0.13 ± ± ± ± 29.6 All pairs 0.04 ± ± ± ± 6.7 EDT ± ± ± ± 12.0

17 Hypocentre Locations

18 Multiplet Identification

19 Arrival time re-picking Before After

20 Multiplet polarisation analysis Modified polarisation analysis of de Meersman et al. 2006

21

22 Location of 5 largest multiplet clusters identified with cluster analysis

23 Cluster 1

24 Cluster 2

25 Cluster 3

26 Cluster 4

27 Cluster 5

28 Results Different mechanism of failure seen based on waveform characteristics and location. Mechanisms include stress triggering - cluster 2, pore pressure diffusion cluster 4, and fault re- activation - clusters 1,3 and 5. Clusters dip away from monitoring well

29 Conclusions Use of all available arrival time pairs result in most robust hypocentres at Ekofisk Errors in velocity model x2 those of arrival times Numerous possible mechanisms of microseismic activity present at Ekofisk: –Fault re-activation –Pore pressure diffusion –Stress triggering –Production induced activity around wells


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