Third EAGE CO2 Geological Storage Workshop, Edinburgh ... · •Forecasting of large...

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)

Third EAGE CO2 Geological Storage Workshop, Edinburgh, March 26-27 2012

• Forecasting of large scale/long-term sequestration of CO2

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?

Third EAGE CO2 Geological Storage Workshop, Edinburgh, March 26-27, 2012

• Generic, quantified understanding of the impact of specific geological parameters can

• Raise awareness of the influence of reservoir heterogeneity on CO2 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


• 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 CO2 flow

• Systematic varying geological parameters in order to identify their impact


• Grid size: 300 x 600 x 20 cells

• Cell size: 100 m x 100 m x 5 m

0.5o

30 km Slight curvature along long

axis to avoid lateral leakage

Shallowest point of model at 1000 m depth


• 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.)


• 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.


• 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


• 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

UP1

UP2

NP1

NP2


• 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)


• Buried sand ridge, Arafura basin

Earl et al. (2006)


• Beach ridges are relict semiparallel wave or wind generated ridges forming

strandplains

• Relief may be preserved during transgression

• Can cover extensive areas


Jackson et al. (2010)

Top Etive beach ridges Oseberg Øst


• Scales based on ancient and modern examples

• Stochastic modelling of features

• Formation porosity uniform for all models : 25%

OSS

FMM


• 100 realizations created for each model

Simulate surface Create grid Add faults


• 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


• 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


• Single injector

• Constant rate of 107 m3/year for 50 years

• Total of 500 Million m3 injected

• Monitor plume movement for 5000 years

• CO2: • Supercritical; constant density of 686 kg/m3

• Viscosity 0.057 cP

• Quadratic rel. perm. with residual saturation of 0.2 and end-point scaling factor of 0.2142

• Water • Constant density: 975 kg/m3

• 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 CO2 plume


• 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


Based on 100 realizations. Porosity = 25%

Values in million cubic metres

• Trap volume filled by single injector • Total available trap volume


• 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


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


• 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


• 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

At 1425 y

At 4525y


• Top surface morphology significantly affects trapping of CO2 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


• Earl, K., Logan, G., Struckmeyer, H. and Totterdell, J. 2006: The northern Arafura Basin – a shallow water forntier. AusGeo News 81, March 2006.

• Jackson, C. A.-L., Grünhagen, H., Howell, J. A., Larsen, A. L., Andersson, A., Boen, F., Groth, A., 2010. 3D seismic imaging of lower delta-plain beach ridges: lower Brent Group, northern North Sea. Journal of the Geological Society of London 167, 1225-1236.

• 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, 1-18.

• All data and models used in this study, as well as animations, are available for download at http://igems.nr.no

• Acknowledgement

• The work supported by the Norwegian Research Council through the CLIMIT programme


http://igems.nr.no/

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