Third EAGE CO2 Geological Storage Workshop, Edinburgh, March

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1 Third EAGE CO2 Geological Storage Workshop, Edinburgh, March Tveranger, J. (Uni Research) Dahle, P. (Norwegian Computing Center) Nilsen, H.M. (SINTEF) Syversveen A.R. (Norwegian Computing Center) Nordbotten, J.M. (Dept. of Math, Univ. of Bergen) Abrahamsen, P. (Norwegian Computing Center) Lie, K.A. (SINTEF)

2 Forecasting of large scale/long-term sequestration of CO 2 relies on simulation models CPU-cost considerations drive simplification of these models Widely held but largely unproven assumption that geological detail does not matter on relevant temporal and spatial scales Is this assumption valid or are we overlooking the elephant in the room?

3 Generic, quantified understanding of the impact of specific geological parameters can Raise awareness of the influence of reservoir heterogeneity on CO 2 sequestration Ease site evaluation Focus data collection for modelling prioritise collection of data related to high-impact features Simplify modelling process ignore or simplify inclusion of low/no-impact parameters

4 Focus on properties influencing storage capacity, plume movement and containment Build and screen a large number of synthetic models on a relevant scale Employ geological realistic generic model scenarios Scale Stratigraphy Geological features and properties Initial tests should involve geologic features with already proven influence on CO 2 flow Systematic varying geological parameters in order to identify their impact

5 Shallowest point of model at 1000 m depth 30 km Slight curvature along long axis to avoid lateral leakage 0.5 o Grid size: Cell size: 300 x 600 x 20 cells 100 m x 100 m x 5 m

6 Large number of possible geological parameters to investigate Focus on a limited number of features Top-reservoir morphology Known, but so far not quantified impact Straddling seismic resolution Topography of reservoir/top seal interface originates from Buried topography Faults Folding Tectonic tiliting Seal bypass features (breccia pipes, sand-dykes, salt intrusions etc.)

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8 Grid resolution gives a minimum scale of features that can be included Only features > 100 m (XY) affecting top-seal morphology will be included Effect of smaller, high-frequency perturbations (XY) can only be captured by increasing grid resolution Vertical variations can be captured in detail Structural scenarios will necessitate adaptation of grid to match fault geometries Geological homogeneity on the scale of the model (30*60 km) is not realistic; the isotropic models should be considered conceptual rather than fully realistic Realistic cases should include lateral variation in top-seal morphology and/or employ observed XY ranges of features included in the scenarios.

9 Stratigraphic features Flat Buried offshore sand ridges (OSS) Flooded marginal marine (FMM) The scenarios are chosen based on the most likely situations where depositional/ erosional topography would be preserved on top of an extensive sandstone reservoir under a regional seal (here marine shale) Flat and no faults act as a references Structural scenarios No faults UP1: Uniform faults all parallel NP1: Non-uniform faults all parallel UP2: Uniform faults two intersecting sets NP2: Non-uniform faults two intersecting sets

10 UP1 NP1 In Nature fault patterns highly case specific To avoid bias we utilise generic, repetitive fault patterns throught the model Stochastic modelling using Havana All faults are considered sealing for simulation purposes UP2 NP2

11 Formed on continental shelf and coastal areas Require sand supply and currents > 0.5 m/s Open shelf ridges may have regional distribution Become moribund and encased in shale during transgression Well documented ancient and modern examples Park et al. (2003)

12 Buried sand ridge, Arafura basin Earl et al. (2006)

13 Beach ridges are relict semiparallel wave or wind generated ridges forming strandplains Relief may be preserved during transgression Can cover extensive areas

14 Top Etive beach ridges Oseberg Øst Jackson et al. (2010)

15 OSS FMM Scales based on ancient and modern examples Stochastic modelling of features Formation porosity uniform for all models : 25%

16 100 realizations created for each model Simulate surface Create grid Add faults

17 Single injector at position (15,15) km from Sequential filling of traps to spill-point along flow path Fast calculation of storage volume fed by well Fast calculation of column heights Flat UP1 OSS UP2 FMM NP2

18 Geometric analysis of max theoretical capacity Identify cascade of traps in each realization Identify pathways Bracket potential for structural trapping Cascade of traps for specific realizations of six different scenarios

19 Single injector Constant rate of 10 7 m 3 /year for 50 years Total of 500 Million m 3 injected Monitor plume movement for 5000 years CO 2 : Supercritical; constant density of 686 kg/m 3 Viscosity cp Quadratic rel. perm. with residual saturation of 0.2 and end-point scaling factor of Water Constant density: 975 kg/m 3 Viscosity: 0.31 cp Residual saturation of 0.1 and end-point scaling factor of 0.85 Hydrostatic boundary conditions during injection No-flow boundary conditions during post injection period Focus on structural trapping and long term migration of CO 2 plume

20 One realization of each model 15 different injection points Largest variation in OSS Variation smaller than stochastic variation of realizations Choice of injection point not considered critical

21 Total available trap volume Trap volume filled by single injector Based on 100 realizations. Porosity = 25% Values in million cubic metres

22 Using the VE assumption (Nordbotten & Celia 2012) approximates the flow of a thin plume in terms of its thickness to obtain a 2D simulation model Cuts CPU cost Errors arising from the dimensional simplification are in many cases significantly smaller than errors arising from the required coarse resolution of a 3D simulation model In the VE simulation the plume spreads laterally, but does not reach the top of the structure after 5000 years

23 Column height, spill point analysis Column height after 5000 yers simulation time, VE simulation

24 Free volume is define as the volume not residually trapped and includes volumes confined in traps Residual trapping largest for the flat cases for which the plume has reached the top of the structure by 5000 years no relief retards plume migration which will sweep a large area For OSS and FMM the plume is retarded and shows lower residual trapping Structural configuration (i.e. fault pattern) appear to have very limited influence on results. Largest differences caused by the relief formed by the depostional features

25 Beach ridges and offshore sand ridges common display coarser grain sizes and higher permeability towards their tops For flat and FMM scenarios plume will move fast to the top thereby increasing residual trapping and decreasing free volume For OSS a high perm top will retard plume migration. This is an effect of feature ampllitude

26 At 1425 y At 4525y

27 Top surface morphology significantly affects trapping of CO 2 by Increasing structural trapping Retarding plume migration Simple geometric spill point analysis can bound structural trapping capacity in large model ensembles Spread of the plume is inhibited if the height of the plume is smaller than the amplitude of the relief Slower spread increases residual trapping In out chosen model setup it appers that faulting has less influence on the free and residual volumes than expected

28 Earl, K., Logan, G., Struckmeyer, H. and Totterdell, J. 2006: The northern Arafura Basin a shallow water forntier. AusGeo News 81, March Jackson, C. A.-L., Grünhagen, H., Howell, J. A., Larsen, A. L., Andersson, A., Boen, F., Groth, A., D seismic imaging of lower delta-plain beach ridges: lower Brent Group, northern North Sea. Journal of the Geological Society of London 167, Park, S.C. Han, H.S., Yoo, D.G. 2003: Transgressive sand ridges on the mid-shelf of the southern sea of Koera (Korea Strait): formation and development in high-energy environments. Marine Geology 193, All data and models used in this study, as well as animations, are available for download at Acknowledgement The work supported by the Norwegian Research Council through the CLIMIT programme