Introduction The Sleipner gas field in the North Sea is a pioneer in commercial scale CCS with over 10 million tonnes of CO2 stripped from produced natural gas and injected (1 Mt CO2 per year) into a layer of sand called the Utsira Formation since 1996 to date (Fig1)

Methodology 4D monitoring is generally based on poststack data sets and as such the data can only be manipulated to enhance visualization resolution and contrast to distinguish changes in wavelength that varies in a highly nonlinear way across the spectrum and comparing with the extracted geobodies (constrained opacity parameters) of the far and full poststacked data. Two attributes are applied to both the far offset and the full offset these include: A. Variance: A seismic attribute used for imaging discontinuities in horizontal Continuity of amplitude and isolating edges. B. RMS Amplitude: This is the square root of the sum of the squared amplitudes divided by the number of live samples.

References

The injected CO2 has been monitored with Time lapse monitoring which reveals stratigraphic and residual trapping of the CO2

taking place at different sub-horizontal horizons within the Utsira formation viewed as a multi-tier feature of high reflectivity Fig 2, 3.

Geology of the Utsira Sand The Utsira Formation is a shallow marine sandstone formation (saline reservoir) with thinner intercalated claystones. The sandstone-dominated Utsira sequence is of Miocene age and represents the lowermost member of the shale-dominated Nordland Group, where the Nordland shale sequence has proven to be an effective seal for the CO2 storage site (6).

Why the use of poststack seismic attributes?

Attribute Analysis of the Far Offset and Full Offset

Discussion

Conclusion Due to the strong contrasted physical properties of supercritical CO2 compared to brine, a high reflective AVO anomaly is usually expected, and the use of attribute analysis will have an impact on the AVO responses.

Questions however still exist : • What is the sensitivity of the data sets to lithological change in comparison to

an signal vs noise ratio within the data while applying attribute analysis? Work is still ongoing to identify the spatial effects of the attribute analysis on the AVO anomalies and discriminate between a CO2 saturated related changes and also a pore pressure related change within the Data sets.

Visual Enhancement of Injected CO2 into the Utsira Formation using Poststack Seismic Attributes

Since injection the spread (lateral and vertical extent) of the CO2 plume has been modelled (4,7), the datasets imaged seismically and the growth of the CO2 plume quantified (2) where the CO2 is partitioned between high saturation and low saturation component trapped between layers .

Attributes are increasingly becoming popular as a valid analytical tool for predicting lithology and reservoir characterisation, as they provide qualitative information of the geometry and physical parameters of the CO2 in the Utsira formation. Fig 4 was generated using the Shuey Equation, : (𝑅𝑅 𝜃𝜃 = 𝑅𝑅 0 + 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺2𝜃𝜃 + F tan2θ − 𝐺𝐺𝐺𝐺𝐺𝐺2𝜃𝜃 . This equation assumes a boundary horizon of a single reflection (insert square in fig 3 Utsira top from shale to sandstone) showing a curve and the different offset ranges which can impact on the AVO responses. Vp, Vs and Density values used from well logs and (3).

Fig 2 Residually trapped CO2 in reservoir fluid Source: (CO2CRC)

This research is partly aimed at applying seismic attributes to post stack data of different offsets (near, far, full) in a bid to identify spatial geometrical features of the survey related to the CO2 plume which can be sensitive to possible AVO anomalies only detectable through a proper offset analysis related to CDP in the seismic data.

Fig4: A hypothetical AVO for a specific reflection using the Shuey Eqn. Fig 5: Seismic acquisition showing offset positions (Source PGS)

Figure 3: Gamma ray log and Density log showing the boundary of the Utsira formation, inset 3D 2008 Survey and the high amplitude reflection of the CO2 plume (Inline/Xline) Intersection

Figure 6a: RMS Attribute time slice of a far Offset 2008 data against an extracted geobody of the same far Offset data, b: Attribute time slice of a full Offset 2008 data against an extracted geobody of the same full Offset data.

Acknowledgement

Fig8: Wiggle trace over Far Offset data

a c

The resulting effect of displaying a wiggle trace (same parameters and inline) over the far data and the full data shown in fig 8 & 9.

Fig9: Wiggle trace over Full Offset data

1. Arts, R., Eiken, O., & Chadwick, A. (2004). Monitoring of CO 2 injected at Sleipner using time-lapse seismic data. Energy, I, 347–352. Retrieved from http://www.sciencedirect.COm/science/article/pii/S0360544204001550 2. Chadwick, R. a., Arts, R., Eiken, O., Kirby, G. a., Lindeberg, E., & Zweigel, P. (2004). 4D Seismic Imaging of an Injected C02 Plume at the Sleipner Field, Central North Sea. Geological Society, London, Memoirs, 29(1), 311–320. doi:10.1144/GSL.MEM.2004.029.01.29 3. Chadwick, R. a., Williams, G. a., Williams, J. D. O., & Noy, D. J. (2012). Measuring pressure performance of a large saline aquifer during industrial-scale CO2 injection: The Utsira Sand, Norwegian North Sea. International Journal of Greenhouse Gas Control, 10, 374–388. doi:10.1016/j.ijggc.2012.06.022 4. Chadwick, R. a., Arts, R., Eiken, O., Kirby, G. a., Lindeberg, E., & Zweigel, P. (2004). 4D Seismic Imaging of an Injected C02 Plume at the Sleipner Field, Central North Sea. Geological Society, London, Memoirs, 29(1), 311–320. 5. Gerlitz, K. (2006). Deriving the Poisson Impedance in Hampson-Russell Software, (April). 6. Head, M. J., Riding, J. B., Eidvin, T., & Chadwick, R. A. (2004). Palynological and foraminiferal biostratigraphy of (Upper Pliocene) Nordland Group mudstones at Sleipner, northern North Sea. Marine and Petroleum Geology, 21(3), 277–297. 7. Singh, V., Asa, S., Cavanagh, A., Hansen, H., Nazarian, B., Iding, M., & Ringrose, P. (2010). SPE 134891 Reservoir Modeling of CO 2 Plume Behavior Calibrated Against Monitoring Data From Sleipner , Norway. 8. Yang, C., Juhlin, C., Ivanova, A., & Fan, W. (2011). AVO attributes analysis of time-lapse 3D surface seismic data from CO2SINK project site , Ketzin , Germany, 13, 2011.

Ponfa Roy Bitrus, Randell Stephenson, Clare Bond, David Iacopini University of Aberdeen, School of Geosciences, Geology and Petroleum Geology Department

Email: prbitrus@abdn.ac.uk LinkedIn: Ponfa Roy Bitrus

My gratitude goes to NDDC for the financial sponsorship. And also Statoil for the data provided.

Figure 7a: Attribute analysis of the far and 7b: full data set. In descending order. Original Amplitude –Variance (Edge) – RMS attribute (Time Slice Z -924m)

Figure 7c: Attribute analysis of the far and 7d: Full data set. In descending order. Original Amplitude –Variance (Edge) – RMS attribute (Time Slice Z -891m)

Observe in the full data, no visible wiggle trace below the plume, distorted amplitude signal (attenuation of frequency), however the far shows a more visible trace, which could also be a mirror image of the CO2 plume above it.

Figure 1: Sleipner Field (Statoil)

The extracted geobody of the CO2 plume on the full offset reveals a connecting throat 6b which in 6a appears as a discontinuity. Time slice of the same horizon 7b doesn’t reveal a connecting throat on the full datasets but high amplitude revealed on the far offset. Analysis is still ongoing to compare the near and mid offset data.

A notable difference exist in the Far offset fig 6a; arrow indicates a probable dimmed or no reflectivity compared to 6b where area becomes less apparent with attributes.

b d

Shuey Equation Expansion 𝑅𝑅 0 = 1

2𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥

+ 𝛥𝛥𝛥𝛥𝛥𝛥

, 𝐺𝐺 = 12𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥

− 2 𝛥𝛥𝛥𝛥𝐺𝐺𝛥𝛥𝛥𝛥𝛥𝛥

𝛥𝛥𝛥𝛥𝛥𝛥

+ 2 𝛥𝛥𝛥𝛥𝐺𝐺𝛥𝛥𝐺𝐺

, 𝐹𝐹 = 12𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥

.

R(0) - Reflection coefficient at normal incidence G- AVO Gradient F- Describes the behaviour at large angles/far offsets close to the critical angle.