storage 6 -modeling for co storage - ieaghg · integrated data for reservoir characterisation &...

IEAGHG CCS Summer SchoolRegina, Sask., Canada, 17-22 July, 2016

Storage 6 - Modeling for CO2 Storage

Professor John KaldiChief Scientist, CO2CRCAustralian School of Petroleum, University of Adelaide, Australia


Modeling

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Data Analysis

What & Why: Modeling in Context of CCS Business Decision Making

Static Reservoir

Model

Dynamic Reservoir

Model

Surface Facility Model

Economics Model

Capacity, Injectivity, ContainmentMonitoring Planning

Data QC

Injection ForecastingWell/facilities PlanningReservoir ManagementStored CO2 Volumes

Value

Decisions

Conceptual Geological

Model

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On Models .…

• There is no substitute for: • Critical independent evaluation on the part of

the geoscientists and engineers to assure the success of a modeling project.

Most failures occur because a basic assumption was found to be wrong.

“All models are wrong…. some are useful” George Box

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Probabilistic Modeling: (Dealing with Uncertainty)

MultipleInterpretations

DataScarcity

ReservoirHeterogeneity

Data“Fuzziness”

Stochastic Model

NeedProbabilistic Approach

+

Uncertainty


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Uncertainty, Accuracy & Precision

Which is better?

Inaccurate & Imprecise

Reality

Accurate & Imprecise

Reality

Inaccurate & Precise

Reality

Accurate & Precise

Reality

At what cost?Is it feasible?Is it worth it?

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Modeling for CO2 Storage• Modeling is used to:– Design injection (location and number of wells, – Forecast the migration of injected carbon dioxide– Simulate fluid flow – Estimate storage capacity– Predict reservoir response – Know when and where to monitor

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• Modeling can include:– coupled geochemistry;– coupled geomechanics;– tracer migration.

• Other benefits of modeling– Ascertain uncertainty– Impress stakeholders: communication tool

• Most modelling uses computer models– Although analytical models are being developed

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Modeling for CO2 Storage


Computer models for CO2 Storage

• Computer models usually:– solve the multiphase equations for fluid flow in porous

media;– use finite-difference techniques for solving flow

equations;– require the simulated region to be broken up into grid

blocks;– are based on techniques and code developed in the

petroleum industry over the past 4 decades.

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Simulation grid

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Static & dynamic models comprised of variable numbers & sizes of Grid Blocks


Discretisation & parameterisation

Each grid block only has one value for porosity, permeability, saturation, composition etc. This has two important consequences:

• We cannot resolve anything below the size of a grid block, i.e. may need to refine grid in areas of interest.

• Geological data measured on different scales e.g. core data, has to be “upscaled” or averaged for application to field or basin scale.

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Upscaling

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Static (Geological) ModelingAim: Capture effects of structure, stratigraphy, sedimentary architecture

– Reservoirs and seals– Lateral and vertical scale & heterogeneity– Faults & fractures– petrophysical properties (porosity, permeability, seismic)– Fluid properties

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3D representation(s) of the subsurfaceEach cell (“grid block” ) contains values for geographic position, depth, volume, rock type, poro/perm, and other “static” properties.Size and complexity importantGrid resolution a key decision: trade-off between detail vs. computational limits.

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Static (Geological) Modeling


3D Model of XYZ Oil Field, NW Shelf, Australia using Petrel

Static (Geological) Reservoir Models

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Input data typesHard Data:Direct measurement from the subsurface:

• Cores (metres), • cuttings (a few mms), • Plugs (10s cms)• fluid samples…

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Integrated data for reservoir characterisation & modeling

-Dep. Env.- Poro/Perm- Stratigraphy

Core data

OutcropAnalogs- Stratigraphy- Geometry

Seismic

Static model

Wireline log data (correlate between wells)

Dynamic (Res Sim) model

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Reservoir Simulation: Dynamic Models

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Simulation input and output

• Input:– Static model (permeability, porosity, fault boundaries…)– Dynamic properties (relative permeability, capillary

pressure)– Initial conditions (pressure, temperature,…)– Boundary conditions (aquifer drive, …)– Flow rates at wells

• Output:– Maps of pressure, fluid saturation, ….– Tracer concentration (if implemented)– Dissolved components (if implemented)– Chemical reaction products (if implemented)– Stress and strain (if implemented in a geomechanical

model)

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Models should be fit for purpose

1. To address scientific questions in a generic context e.g.:

– the effect of shale barriers on vertical migration of CO2

– the effect of a hydrodynamic gradient on CO2 migration

– Dissolution of CO2

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Image Reference: Cardoso& Andres, 2014, Nature Communications Volume: 5

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900 yr

1300 yr

2400yrFrom: J. Ennis-King

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Modeling the dissolution of injected CO2


Models should be fit for purpose

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2. To make technical predictions in a site-specific context to support decisions e.g.: • What is the breakthrough

(arrival) time of CO2 in an EOR project?

• What is the effect of well-spacing on the maximum injectivity?

• What is the predicted seismic response?

Image source: http://www.iogsolutions.com

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Models can be simple...

hkqtk

trrwc

wrcc

)(max,

rc,max

h

h(r,t)

q

Nordbotten et al. (2005)

e.g. Analytical models

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…or more complex

Reference: Flett, M. A., et al. (2008) SPEdoi:10.2118/116372-MS

Example from the Gorgon Project:•Plan to inject and store 3-4 MT PA•Long-term modelling and monitoring required

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Basin-scale modeling

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• Solve a large set of linear equations at multiple time-steps for a large number of cells in a broad geographic area− Can be coupled (eg geochemical / geomechanical

• Computationally demanding

• Make technical predictions in a site-specific context to aid decision-making

• Computationally manageable

Small-scale (pilot projects) modeling


CO2injection

well

Image from: C. Gibson-Poole

Basin-scale modeling: injection and migration of CO2Example Central Gippsland Basin, Australia

Lakes Entrance Formation

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Small-scale (Pilot project) modeling:Example CO2CRC Otway Project

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• Geological model: incorporates structure (faults) & fluid contacts

• Static model- based on: facies (rock-

type) grid parameterization

- Stochastic: multiple realisations of properties (eg porosity, permeability)

• Dynamic model: upscaled; simulates various injection / migration scenarios

500m


CRC-1 InjectorNaylor-1Monitor well

Naylor South-1

0 300m

Pre-production gas spill point

-2140-2120-2100-2080-2060-2040-2020-2000-1980

Depth mSS

T.Dance

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T.Dance

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Screen grab of 3D model looking West.

Naylor South FaultInjection zone in contact with Timboon Aquifer

Geo-cellular model Details:

•10x10m lateral cell size•Layers ~1m

•Total cells: 132,396

•Layers follow top

•5 Realisations of sands and shale•Poro/perm conditioned to facies

CRC-2Naylor-1

CRC-1

PorosityT.Dance

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Screen grab of 3D model looking West.

Naylor South FaultInjection zone in contact with Timboon Aquifer

Geo-cellular model Details:•10x10m lateral cell size•Layers ~1m

•Total cells: 132,396

•Layers follow top

•5 Realisations of sands and shale•Poro/perm conditioned to facies

CRC-2Naylor-1

CRC-1

PermeabilityT.Dance

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CO2 injection well

Monitoring well

CO2 accumulation

Upscaled Reservoir Model

Y.Cinar

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CO2CRC Otway project: CO2 mass fraction

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Carbon dioxide mass fraction: 18 Sept Carbon dioxide mass fraction: 31 Dec


CO2CRC Otway project: pressure difference

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Pressure difference: 18 Sept – 31 Dec Pressure difference: 18 Sept – 31 Dec(NW – SE slice)


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Modeling Time and effort

(from Tyson, 2008)


Modeling Conclusions

• Modeling is a useful tool in the design of carbon dioxide storage projects.

• Modeling depends on the quality of the data and the skill of the user (old saying: garbage in, garbage out).

• Most effort and time in modeling is in data gathering and grid parameterization

• Models will always be just models…uncertainty is inherent! But, in the right hands with the right questions models can provide powerful answers.

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storage 6 -modeling for co storage - ieaghg · integrated data for reservoir characterisation &...

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