Flow and Transport for CO2 Storage - UKCCSRC · 2016-02-02 · Flow and Transport for CO 2 Storage...

Flow and Transport for CO2 Storage

Thursday 29th October – Friday 30th October 2015, London

www.ukccsrc.ac.uk

Agenda.................................................................................................................................................................................................... 2Speakers and Chairs.................................................................................................................................................................................................... 3Delegate List.................................................................................................................................................................................................... 6Martin Blunt - Pore-scale processes....................................................................................................................................................................................................... 8Marc Hesse - Long-term safety of geological CO2 storage....................................................................................................................................................................................................... 50Jeroen Snippe - Multiphase flow modelling of calcite dissolution patterns....................................................................................................................................................................................................... 129

AGENDA 29th October 2015 12:00 - 13:00 Arrival & registration 13:00 - 13:15 Introduction to meeting – Catriona Reynolds (Imperial College London) 13:15 - 14:00 Pore-scale dynamics and the interpretation of flow processes - Martin Blunt (Imperial College

London) 14:00 - 14:45 TBC - Tony Espie (BP) 14:45 - 15:30 20 years and 20 million tonnes: Statoil storage experience - Andrew Cavanagh (Statoil) 15:30 - 16:00 Break Poster & Session 16:00 - 16:45 Characterising flow behaviour for gas injection: relative permeability of CO2-brine and N2-

water in heterogeneous rocks - Catriona Reynolds (Imperial College London) 16:45 - 17:30 Long-term safety of geological CO2 storage: Lessons from Bravo Dome Natural CO2 reservoir -

Marc Hesse (University of Texas at Austin) 17:30 - 18:30 Evening reception Poster Session 19:00 onwards Dinner (Med Kitchen, 25–35 Gloucester Road, London SW7 4PL, Tel: 020 7589 1383) 30th October 2015 08:30 - 09:00 Coffee Poster Session 09:00 - 09:45 Monitoring and modelling the flow and dissolution of geologically stored CO2 - Jerome Neufeld

(University of Cambridge) 09:45 - 10:30 Enhanced storage performance through CO2-Enhanced Oil Recovery - Stuart Haszeldine

(University of Edinburgh) 10:30 - 11:15 Migration of CO2 through layered sedimentary sequences - Chris MacMinn (University of

Oxford) 11:15 - 11:45 Break Poster Session 11:45 - 12:30 Multiphase Flow Modelling of Calcite Dissolution Patterns from Core Scale to Reservoir Scale -

Jeroen Snippe (Shell) 12:30 - 13:15 Musings on the properties of a mobile CO2 layer flowing in porous sand: integrating

monitoring and modelling - Andy Chadwick (BGS) 13:15 - 13:30 Meeting Close - Sam Krevor (Imperial College London)

SPEAKERS AND CHAIRS Martin J. Blunt Martin Blunt joined Imperial in June 1999 as a Professor of Petroleum Engineering. He served as Head of the Department of Earth Science and Engineering from 2006-2011. He Previous to this he was Associate Professor of Petroleum Engineering at Stanford University in California. Before joining Stanford in 1992, he was a research reservoir engineer with BP in Sunbury-on-Thames. He holds MA and PhD (1988) degrees in theoretical physics from Cambridge University. Professor Blunt's research interests are in multiphase flow in porous media with applications to oil and gas recovery, contaminant transport and clean-up in polluted aquifers and geological carbon storage. He performs experimental, theoretical and numerical research into many aspects of flow and transport in porous systems, including pore-scale modelling of displacement processes, and large-scale simulation using streamline-based methods. He has written over 200 scientific papers and is Editor of Transport in Porous Media. In 2011 he was awarded the Uren Award from the Society of Petroleum Engineers for outstanding contributions to the technology of petroleum engineering made before the age of 45. Andrew Cavanagh Dr Andrew Cavanagh - I hold a doctorate in petroleum system analysis from the University of Edinburgh (2003). I am a senior research scientist on CO2 Storage for Statoil, based in Trondheim since 2013. My focus is on subsurface flow modeling, CCS and CO2 EOR innovation. Before joining Statoil, I worked for Permedia and Landmark, a small research group in Ottawa, Canada, and a large service company in Denver, Colorado. Back then, I designed simulators for the oil and gas industry. I have a background in basin-to-reservoir scale fluid flow modeling and an interest in CO2 management, which was sparked by post-doctoral research at GFZ Potsdam on ice sheets and petroleum systems in the Arctic as geological climate change drivers. Andy Chadwick Andy Chadwick has over thirty years’ experience in most aspects of seismic geophysics, structural geology and basin analysis and is currently an Individual Merit Research Scientist at the British Geological Survey. Since 1998 he has become increasingly involved with underground CO2 storage, participating in many European CO2 research projects and a number of others funded by the UK and overseas governments, research councils and industry. Andy’s main interests lie in storage site performance prediction and monitoring. Current research directions include quantitative analysis of time-lapse seismic data to characterise CO2 plumes, and history-matched flow modelling to understand detailed modes of CO2 migration in reservoirs. Andy has advised a number of national and international regulatory bodies and is particularly interested in developing pragmatic integrated monitoring systems and strategies for industrial-scale storage sites that meet anticipated regulatory requirements. He has published over 150 scientific papers and books on a range of topics including more than sixty on CCS. Tony Espie Tony is an Advisor on CO2 Storage within BP Alternative Energy based at Sunbury in the UK where he manages a technology development programme on performance prediction for storage systems. He has been engaged in developing CO2 capture and storage technology since the mid 1990’s. This has included source-sink matching for capture and storage within BP, the evaluation of CO2 EOR options in Alaska, the North Sea, and the Middle

East and assessing saline aquifer options in Australia. He initially trained as a Chemical Engineer at the University of Canterbury in New Zealand where he completed a PhD in the field of liquid-liquid separations technology. His professional career started in the nuclear industry in the United Kingdom modelling coupled heat transfer and flow processes in the UK designed Advanced Gas Cooled Reactors. Tony joined BP as a reservoir engineer in 1985 where his responsibilities focused upon technology development to enhance and optimise oil and gas recovery processes. This included characterising the mechanisms controlling multiphase displacement in porous media and the design and monitoring of gas injection processes for Enhanced Oil recovery. Stuart Haszeldine Stuart has 25 years’ experience working with subsurface information from basin-scale to field-scale in hydrocarbon extraction and in waste disposal. He was awarded the Scottish Science Prize in 1999, and elected Fellow of the Royal Society of Edinburgh in 2003. Since 2005 he has created the UK's largest University group examining CO2 storage geology, with a particular focus on natural analogues and seepage processes through overburden. He is currently co-leader of the Scottish Centre for Carbon Storage, lead scientist on CO2 storage for the UK Energy Research Centre, and co-leader of the academic network UKCCSRC. He served as advisor to the 2005-6 UK Parliament Science and Technology Committee on CO2 capture and storage. Several pieces of evidence have been submitted to UK government consultations on CCS Marc Hesse Marc Hesse is a computational geoscientist interested in the dynamics of porous media in geological and environmental processes. Due to his broad training, his work bridges both the classical solid-earth sciences and the environmental sciences and energy geosciences. Marc believes that porous media provide a unifying theme across the geosciences and he is actively developing and teaching a range of new and innovative courses on porous media from a geoscience perspective. Marc initially studied Geology at the Technical University of Munich and the University of Edinburgh where he has developed an interest in a broad range of geological phenomena. Recognizing the importance of fluid dynamics and mathematical modeling in the study of porous media Marc shifted towards applied mathematics and its applications in the geosciences during his graduate education at the Massachusetts Institute of Technology, the University of Cambridge, and Stanford University and during his postdoc at Brown University. In 2009 Marc joined the Jackson School of Geosciences and the Institute of Computational Engineering and Sciences at the University of Texas. Chris MacMinn Chris earned his PhD from MIT, where he worked with Ruben Juanes on the fluid dynamics of geological CO2 storage. He was then a Postdoctoral Fellow of the Yale Climate & Energy Institute (Yale University) before joining the University of Oxford in October 2013. His research is currently focused on various aspects of the coupling between flow, transport, and deformation in the subsurface.

Jerome Neufeld Dr Jerome Neufeld is a University Lecturer and Royal Society University Research Fellow at the BP Institute, the Department of Earth Sciences, and the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge. His research focuses on the coupling of thermodynamics and fluid dynamics in multiphase systems found within and on the Earth using theoretical and experimental techniques closely linked with field observations. His research focuses on the flow of multiphase fluids with particular attention on the geological storage of carbon dioxide, and the solidification of multicomponent fluids such as the solidification of sea ice in the polar oceans, the solidification and texturing of crystals in magma chambers, the growth of the Earth’s inner core and the solidification of iron asteroids. More recently he has modelled the viscous deformation of continental collisions and the propagation of magma in the near subsurface. Dr Neufeld has a PhD in geophysics from Yale University and a B.A.Sc. in engineering science from the University of Toronto, and currently is an Official Fellow in Physics at St. Catharine's College, Cambridge. Cat Reynolds Cat is a final year PhD candidate at Imperial College London working in the Department of Earth Science and Engineering and the Qatar Carbonates and Carbon Storage Research Centre. Her research focuses on the multiphase flow behaviour and relative permeability characteristics of CO2 and brine at the core and pore scales, with particular application to geologic CO2 storage in sandstones. Cat holds an MSci in Natural Sciences from the University of Cambridge (2011), and an MSc in Petroleum Geoscience from Imperial College London (2012). Jeroen Snippe Jeroen Snippe is a senior Reservoir Engineer in Shell Global Solutions International B.V. (Rijswijk, Netherlands) and Shell Subject Matter Expert in Integrated Reservoir Modelling. He holds a PhD in theoretical physics (1997) from Leiden University (Netherlands). After his PhD he joined the Shell simulator development team, focusing on static-dynamic model integration, upscaling and gridding. From 2003 to 2009 he worked in Aberdeen on several North Sea oil and gas reservoirs (well & reservoir management and field redevelopment). In 2009 he moved into his current role - research and deployment of Reactive Transport Modelling technologies - and leads a small team on this topic. The team collaborates with several universities, defines and executes complementary internal research, and supports Shell projects on CCS and acid gas injection as well as water injection projects.

DELEGATE LIST

First Name Organisation Pedro Abrantes CO2track Simeon Agada Imperial College London Ali Al-Menhali Imperial College London Mohammed Dahiru Aminu Cranfield University Mike Bickle Cambridge Martin Blunt Imperial College London Maartje Boon Imperial College London Emilie Brady UKCCSRC Andrew Cavanagh Statoil Jiajun Cen Imperial College London Andy Chadwick British Geological Survey Florence Chow Imperial College London Laurence Cowton University of Cambridge John Crawshaw Imperial College London Diganta Das Loughborough University Yacine Debbabi Imperial College London Emmanuel Efika Imperial College London Tony Espie BP Group Technology Simon Franchini Imperial College London Davide Gamboa British Geological Survey Mojgan Hadi Mosleh Imperial College London Stuart Haszeldine SCCS/University of Edinburgh Marc Hesse University of Texas at Austin Vivek Jaiswal LR-Senergy Luke Jenkins University of Oxford Gareth Johnson University of Edinburgh/SCCS Mark Kelman Sam Kevor Imperial College London Rachel Kilgallon University of Edinburgh Peter Lai Imperial College London Qingyang Lin Imperial College London Fiona Llewellyn-Beard University of Cambridge Iain Macdonald QCCSRC, Imperial College London Chris MacMinn University of Oxford Geoff Maitland QCCSRC Hannah Menke Imperial College London Jerome Neufeld University of Cambridge Anh Thy Nguyen Ngo Petro-vision Vahid Niasar University of Manchester JP Nijjer University of Cambridge Ben Niu Imperial College London Thomas Oliveira Imperial College London Kumar Patchigolla Cranfield University Bhavna Patel Imperial College London Joao Paulo Pereira Nunes Imperial College London Ronny Pini Imperial College London

Kazeem Rabiu Loughborough University Cat Reynolds Imperial College London Tarik Saif Imperial College London Saeed Salimzadeh Imperial College London Yolanda Sanchez-Vicente Imperial College of London Seyed Shariatipour Coventry University Olivia Sloan Imperial College London Jeroen Snippe Shell Weparn Julian Tay Imperial College London Konstantina Vogiatzaki City University Hayley Vosper British Geological Survey

UK Carbon Capture and Storage Research Centre (UKCCSRC) The UKCCSRC brings together over 1000 members including over 200 of the UK’s world-class CCS academics to provide a national focal point for CCS research and development. The Centre is a virtual network where academics, industry, regulators and others in the sector collaborate to analyse problems devise and carry out world-leading research and share delivery, thus maximising impact. A key priority is supporting the UK economy by driving an integrated research programme and building research capacity that is focused on maximising the contribution of CCS to a low-carbon energy system for the UK. The UKCCSRC is supported by the Engineering and Physical Sciences Research Council (EPSRC) www.epsrc.ac.uk as part of the Research Councils UK Energy Programme, with additional funding from the Department of Energy and Climate Change (DECC) www.decc.gov.uk for the UKCCSRC PACT Facilities www.pact.ac.uk

www.ukccsrc.ac.uk

http://www.epsrc.ac.uk/

http://www.decc.gov.uk/

http://www.pact.ac.uk/

Pore-scale processes A revolution in describing multiphase flow

Martin Blunt, Matthew Andrew, Branko Bijeljic, Sam Krevor,

Catriona Reynolds, Ali Raeini, Hu Dong, João P. Nunes, Kamaljit Singh

and Hannah Menke

Department of Earth Science and Engineering

Imperial College London and

iRock Technologies, Beijing

Ten-year, $70 million programme: 2008 – 2018. To understand carbon dioxide storage in a Qatari context (carbonates). Major experimental and modelling activity. Based at Imperial College. Work all published in the public domain.

Multidisciplinary (Chem. Eng. / Earth Sci. & Eng.). Three major themes: rocks, fluids and rock-fluid interaction. Four dedicated lecturers, other faculty, post-docs and PhD students (some from Qatar): involves >70 researchers.

http://www.google.co.uk/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0CAcQjRxqFQoTCMn3luSS5sgCFUm-FAodsjwEdA&url=http://www.firstcovers.com/user/558902/qccsrc.html&psig=AFQjCNE4uahsBuRpU_nZJ4HZJhvuOgvCxA&ust=1446155388338774

3

Nat Geo Oct 2013

Status of Impact – Sea-level rise

4

Abu Dhabi Environment Agency

2009

Abu Dhabi Environment Agency

2009

5

Motivation

Historically high oil prices, even at $40/barrel – peak oil per person in

1979 and current discoveries running at half global production (30 billion

stb/year). Need to produce more of the oil in existing fields.

Exploitation of unconventional oil and gas.

Wise use of groundwater.

Global-scale CO2 storage.

All involve understanding of flow of fluids in porous rocks.

New tools

Multi-scale imaging – particularly ability to image the pore space of rock

and fluids at 10 nm to micron resolution.

Public-domain availability of good-quality software for scientific

computing – changes the way we develop computational models.

What is digital rock analysis? A physically-based model for flow,

based on pore-scale displacement. A nm – cm model (6 orders of

magnitude in scale). A necessary complement and input to a field-scale

geological/reservoir model (cm – km, or another 6 orders of magnitude).

What we can do Original work on 3D X-ray microtomography by Flannery et al. (1987)

states in conclusion: “we believe that it will be possible to study contained

systems under conditions of temperature, pressure, and environment

representative of process conditions.” Can now!

Will discuss imaging and flow simulation: transport, reaction and

multiphase flow.

Flow

Transport

Reaction

Structure

Imperial College multi-scale imaging lab

Start with the fundamentals – understand processes experimentally at the

pore scale. Micron-to-metre imaging with in situ displacement at reservoir

conditions.

Micro-CT – Flow loop

10

Imaging and computing

Bench-top micro-CT scanners are

convenient, no time limitations and

modern systems have optics.

Synchrotron sources. Bright, mono-

chromatic and fast.

Computationally, not interested in

GPU, parallel, but better algorithms.

Availability of excellent public-

domain solvers:

algebraic multigrid,

OpenFoam

Navier-Stokes solver.

Fluid mechanics:

unstructured

adaptive grids.

Blunt et al., Adv. Water Res. 2013

Images and networks for carbonates

Estaillades Ketton Mount Gambier

Represent the pore space topologically and compute displacement semi-

analytically through the network. Also accommodate micro-porosity.

Transport – rocks and people

13

How to get to Imperial from

Heathrow airport?

Direct simulation: use a shallow

seismic image of the subsurface

of London?!

London Underground map (the

macro-pores) plus a local map

(the micro-pores)

Waterflooding and wettability

Complex displacement sequences, shown here for a single idealized

pore. What are the contact angles? Can now measure them in situ.

Altered wettability surfaces after primary drainage:

mixed-wettability.

Relative permeability is

governed by the interplay

of displacement,

structure and wettability,

which can vary across the

field

Water-wet two-phase predictions

Experimental data from Berea sandstone cores (Oak, 1990)

– No tuning of network (Øren and Bakke, 2003) necessary

– The fluids are water and oil

– Water-wet data – predictions made with θa = [50°, 80°]

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

Water Saturation

Rela

tive P

erm

eabili

ty

Primary drainage

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

Water Saturation

Rela

tive P

erm

eabili

ty

ExperimentalPredicted

p

p

rp

p PKk

q

Secondary waterflooding

Valvatne and Blunt, Water Resources Research (2004)

The tyranny of scale

Typically have a million-fold variation in length scale, from 10 nm for

the smallest micro-pores to cms for whole cores.

Need to upscale.

No one method can capture complex displacement processes over

this range of scales.

Whole core – 1 cm Macro pore - 1 mm Micro pore - 10 m

Direct simulation and networks

• Cannot compute multiphase flow directly on images that can

resolve the smallest pores, and processes within them.

• Direct simulation would require of order 1021 grid blocks. No, not

even the fastest in-the-future computer will ever be able to do this.

• Need to combine methods: direct simulation for pore-scale events,

“simple” images; network modelling to upscale behaviour and

capture the correct displacement sequence.

Back to the science - dispersion Direct simulation on the pore-space images.

Stokes solver, streamline tracing, random motion for diffusion.

Sandpack Sandstone (Bentheimer) Carbonate (Portland)

Carbonate images and flow fields

5 mm

Ketton

Mt Gambier

Estaillades Indiana

ME1 Guiting

Particle trajectories in the pore space

Combine analytical

streamline tracing with

a random hop to

represent diffusion.

Solute particles travel

longer distances for

larger Pe number.

𝑃𝑒 =𝑣𝐿

𝐷𝑚= advection

diffusion

v = velocity;

L = grain/pore size;

Dm = molecular diffusion coefficient.

Include reaction by allowing particles within a diffusion distance to react,

including solid. Probability of reaction relates to reaction rate.

Concentration

profiles

Bentheimer

Sandstone

Bead pack Portland

Carbonate

Compare prediction of

concentration vs.

distance for different

times and rock types

against NMR

experiments.

Can make first

principles predictions

once the pore

geometry is imaged.

Bijeljic et al. PRL (2011); PRE

(2013); WRR (2013).

Time

Reaction with the solid: Dissolution regimes

22

Daccord et al.,

Chem. Eng. Sci, (1993)

Maheshwari et al.,

Chem. Eng. Sci, (2013) compact

uniform

wormhole

𝑃𝑒 =𝑣𝐿

𝐷𝑚= advection

diffusion

Da = reaction

advection

Compare pore-scale experiments and models. In the models if a particle

hits solid in the diffusive step, dissolve solid after a given number of hits:

determines reaction rate.

Pore-scale dissolution experiments Flow rate: 0.5 ml/min for 2.5 hrs [Pe ~103; Da ~10-4]

Brine composition: 1% KCl 5% NaCl brine saturated with CO2

at 10 MPa and 50oC [pH=3.1]

Ketton carbonate - homogeneous Portland carbonate - heterogeneous

Menke et al., EST (2015)

Sim

ula

tio

n

Exp

eri

men

tal

Model vs. experiment

Dissolution – parallel to flow direction

Ketton carbonate – chanelling Portland carbonate – compact dissolution

0.05 ml/min [Pe ~102; Da ~10-3]

Three-dimensional results (low flow rate)

1.3

mm

0.67 mm

Small Pe regime only “face dissolution” - Whole grains are being dissolved

No significant impact in permeability.

Simulations: Estaillades Pe, Péclet = 1, slow flow

1.3

mm

0.67 mm

Simulations: Estaillades Pe, Péclet = 50

Simulations: Estaillades Pe, Péclet = 280, fast flow

High Pe regime see more uniform dissolution, as the reactant can penetrate the

rock before reacting. As seen experimentally.

Trapped CO2 clusters – colour indicates size

Pentland et al., Geophysical Research Letters (2011)

How much is trapped and

how much can be stored?

Results in sandstones

(Doddington, Bentheimer

and Berea).

After drainage After waterflooding

20 mm

0.0

0.2

0.4

0.6

0.0 0.5 1.0

Sn

wr

Snwi

C. Pentland (2011)@ 70 C

Rehab results @ 70C

Can study many systems – Bentheimer and Doddington

Can study many systems – Estaillades and Ketton

Can study many systems – Portland

Andrew et al.,

Geophysical Research

Letters (2011); IJGGC (2014)

Curvature, contact angle and validation Can also use high-resolution images to

determine: curvature – capillary pressure,

and local pressure for each ganglion; and

surface contacts to determine contact

angles.

Andrew et al.,

AWR (2014)

Residual oil in a mixed-wet system

Direct simulation (volume of

fluid) of trapping

Measurement of contact angle

Dynamic Tomography at Synchrotron Sources

35

Synchrotron Experimental

Team:

Matthew Andrew

Hannah Menke

Cat Reynolds

Kamal Singh

Branko Bijeljic

Martin Blunt

Connected pathway and ganglia flow

Scan time ≈ 20 s, Time step = 43 s,

10 PV

Interfacial curvature

Equilibrium capillary pressure change

38

Distal (non-local) snap-off

39

3D X-ray Micro-CT imaging of a rock sample

Does it matter?

40

Enhanced Oil Recovery

Carbon Storage

http://energy.gov/

Contaminant Transport

http://www.euwfd.com/html/groundwater.html

Shale oil and gas

Conclusions

New tools – both experimentally and numerically allow us to

observe and model flow and transport in great detail from the pore

scale upwards.

Huge practical challenges also drive the science.

We are on the cusp of a revolution.

Acknowledgements

Qatar Petroleum, Shell and the Qatar Science and Technology Park

under the Qatar Carbonates and Carbon Storage Research Centre

http://www.google.co.uk/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&uact=8&docid=BXm07rJQMjIw9M&tbnid=n3b6fnY1ChoYJM:&ved=&url=http://www.estrategiaconcursos.com.br/blog/noticia/resultado-concurso-da-petrobras/&ei=WMyzU5jJMoO6OICwgMgE&bvm=bv.69837884,d.ZWU&psig=AFQjCNEY7QHu04W73d3b7SXR6FRiDGgpZA&ust=1404378585129390

Long-term safety of geological CO2 storage:Lessons from Bravo Dome Natural CO2 reservoir

Marc A. Hesse

Department of Geological SciencesInstitute for Computational Engineering & Sciences

October 29, 2015

Marc A. Hesse UKCCSRC Workshop October 29, 2015 1 / 40

Outline

1 IntroductionMotivationBravo Dome natural CO2 field in New Mexico

2 Dissolution trapping at Bravo DomeMagnitude of CO2 dissolutionMechanism of solubility trapping at Bravo DomeRate of CO2 dissolution

3 Pressures in natural CO2 reservoirsReservoir compartmentalizationOrigins of subhydrostatic pressures

4 Implications for geological CO2 storage


AcknowledgementsFunding:National Science Foundation - Hydrologic SciencesDepartment of Energy - Basic Energy Sciences

Energy Frontier Research Center:Center for Frontiers in Subsurface Energy Security

Bravo Dome collaborators:Kiran Sataye, Daria Ahkbari, Kimberly Lankford, MartinCassidy, Toti Larson, Dani Stoeckli, Changli Yuan, GaryPope, OXY Bravo Dome team

Papers:Sathaye, Hesse, Cassidy & Stockli (2014) PNASSathaye, Larson, & Hesse (201X) EPSL Ahkbari & Hesse(201X) in prep


Outline






Outline






Trapping contribution and time-scales

IPCC special report Reservoir simulation Theoretical analysis

100 101 102 103 104

100%

80%

60%

40%

20%

0%

Frac

tion

of C

O2 tr

appe

d

Time since injection [yrs]

free CO2

solubility trapping

residual trapping

mineral trapping

100%

80%

60%

40%

20%

0%101 102 103 104


Frac

tion

of C

O2 tr

appe

d free CO2

solubility trapping

residual trapping

mineral trapping

100%

80%

60%

40%

20%

0%1 3 9

Injection periods [-]

Frac

tion

of C

O2 tr

appe

d

solubility trapping

residual trapping

free CO2

Benson et al. (2005)IPCC Special Report

Kumar et al. (2005)SPE Journal, 10(3)

MacMinn et al. (2011)J. Fluid Mech., 688

Constrain trapping rates at Bravo Dome using field observations!


Trapping contribution and time-scales

IPCC special report Reservoir simulation Theoretical analysis

100 101 102 103 104

100%

80%

60%

40%

20%

0%

Frac

tion

of C

O2 tr

appe

d


free CO2

solubility trapping

residual trapping

mineral trapping

100%

80%

60%

40%

20%

0%101 102 103 104


Frac

tion

of C

O2 tr

appe

d free CO2

solubility trapping

residual trapping

mineral trapping

100%

80%

60%

40%

20%

0%1 3 9

Injection periods [-]

Frac

tion

of C

O2 tr

appe

d

solubility trapping

residual trapping

free CO2

Benson et al. (2005)IPCC Special Report

Kumar et al. (2005)SPE Journal, 10(3)

MacMinn et al. (2011)J. Fluid Mech., 688

Constrain trapping rates at Bravo Dome using field observations!


Outline






Bravo Dome natural gas field, New Mexico


Introduction Bravo Dome, NM

The numbers:Area: 3600 km2

Gas-water contact: 1700 km2

Reserves: 22 tcf (10 tcf)Largest CO2 field.Top 20 natural gas fields.Essentially pure CO2

Origin: volcanic gas(very high 3He/4He)







20 km











Reserves: 22 tcf (10 tcf)Largest CO2 field.Top 20 natural gas fields.

Essentially pure CO2















Data available at Bravo Dome, NM

788 wells150 wells with digitized logs42 cored wells10 wells with stratigraphic logs18 wells with noble gas/isotope data3645 permeability and porositymeasurements21 drainage capillary pressure curves40 2D seismic lines

Best data set to constrain the magnitude and rate of solubility trapping.


Outline






Outline






Estimating dissolution from gas composition

Convective dissolution of CO2

CO2/3He-ratio in the gas

CO2CO2

He HeCO2 CO2

Fraction dissolved: F = 1− [CO2/He]final[CO2/He]initial

≈ 1− 216 ≈ 0.9


Estimating dissolution from gas composition

Convective dissolution of CO2 CO2/3He-ratio in the gas

CO2CO2

He HeCO2 CO2

0 5 10 15 20 25 30 350

2

4

6

8

10

12

14

16

18

time [hrs]

CO

2/He

in g

as [m

ol/m

ol]

Fraction dissolved: F = 1− [CO2/He]final[CO2/He]initial

≈ 1− 216 ≈ 0.9


Mapping geochemistry into the reservoir

Gas geochemistry:

Compositional variation in the reservoir

Gilfillan et al. (2009) Nature, 458Lollar & Ballentine (2009) Nature Geosci, 2(8)Cassidy (2006) PhD Thesis U. Houston



Gas geochemistry: Compositional variation in the reservoir

2.5

3

3.5

4

4.5

5

0 10 20 30 40 50 60 70Easting (km)

0

10

20

30

40

50

60

70

Nor

thin

g (k

m)

CO2/3 H

109 [-

]

8 MPa




Gas geochemistry: Compositional variation in the reservoir

10%

20%

30%

40%

50%

0 10 20 30 40 50 60 70Easting (km)

0

10

20

30

40

50

60

70

Nor

thin

g (k

m)

60%

0%

loca

l fra

ctio

n of

gas

dis

solv

ed



Gas mass per area: m = φ̄S̄ρ(p̄)h

Thickness

Volume fraction Density Mass

0 10 20 30 40 50 60 70Easting (km)

0

10

20

30

40

50

60

70

Nor

thin

g (k

m)

20

40

60

80

100

120

thic

knes

s of

gas

col

umn:

h [m

]

Large spatial variations that need to be accounted for in mass balance.



Thickness Volume fraction

Density Mass

0 10 20 30 40 50 60 70Easting (km)

0

10

20

30

40

50

60

70

Nor

thin

g (k

m)

20

40

60

80

100

120

thic

knes

s of

gas

col

umn:

h [m

]

0

10

20

30

40

50

60

70

Nor

thin

g (k

m)

4%

6%

8%

10%

12%

14%

16%

0 10 20 30 40 50 60 70Easting (km)

gas

volu

me

frac

tion:

φS




Thickness Volume fraction Density

Mass

0 10 20 30 40 50 60 70Easting (km)

0

10

20

30

40

50

60

70

Nor

thin

g (k

m)

20

40

60

80

100

120

thic

knes

s of

gas

col

umn:

h [m

]

0

10

20

30

40

50

60

70

Nor

thin

g (k

m)

4%

6%

8%

10%

12%

14%

16%

0 10 20 30 40 50 60 70Easting (km)

gas

volu

me

frac

tion:

φS

0 10 20 30 40 50 60 700

10

20

30

40

50

60

70

Easting (km)

Nor

thin

g (k

m)

100

200

300

400

500

600

700

800

gas

dens

ity [k

g/m

3 ]




Thickness Volume fraction Density Mass

0 10 20 30 40 50 60 70Easting (km)

0

10

20

30

40

50

60

70

Nor

thin

g (k

m)

20

40

60

80

100

120

thic

knes

s of

gas

col

umn:

h [m

]

0

10

20

30

40

50

60

70

Nor

thin

g (k

m)

4%

6%

8%

10%

12%

14%

16%

0 10 20 30 40 50 60 70Easting (km)

gas

volu

me

frac

tion:

φS

0 10 20 30 40 50 60 700

10

20

30

40

50

60

70

Easting (km)

Nor

thin

g (k

m)

100

200

300

400

500

600

700

800

gas

dens

ity [k

g/m

3 ]

0 10 20 30 40 50 60 700

10

20

30

40

50

60

70

Easting (km)

Nor

thin

g (k

m)

100

200

300

400

500

600

700

800

900

1000

1100

0

gas

mas

s pe

r uni

t are

a [k

g/m

2 ]




Thickness Volume fraction Density Mass

0 10 20 30 40 50 60 70Easting (km)

0

10

20

30

40

50

60

70

Nor

thin

g (k

m)

20

40

60

80

100

120

thic

knes

s of

gas

col

umn:

h [m

]

0

10

20

30

40

50

60

70

Nor

thin

g (k

m)

4%

6%

8%

10%

12%

14%

16%

0 10 20 30 40 50 60 70Easting (km)

gas

volu

me

frac

tion:

φS

0 10 20 30 40 50 60 700

10

20

30

40

50

60

70

Easting (km)

Nor

thin

g (k

m)

100

200

300

400

500

600

700

800

gas

dens

ity [k

g/m

3 ]

0 10 20 30 40 50 60 700

10

20

30

40

50

60

70

Easting (km)

Nor

thin

g (k

m)

100

200

300

400

500

600

700

800

900

1000

1100

0

gas

mas

s pe

r uni

t are

a [k

g/m

2 ]



Estimate of the local change in mass: ∆m

∆M =

∫∫∆m dxdy ≈

∫∫(1/F − 1)mf dxdy.

Mass/area: mf Fraction dissolved: F

Change in mass, ∆m

0 10 20 30 40 50 60 700

10

20

30

40

50

60

70

Easting (km)

Nor

thin

g (k

m)

100

200

300

400

500

600

700

800

900

1000

1100

0

gas

mas

s pe

r uni

t are

a [k

g/m

2 ]

10%

20%

30%

40%

50%

0 10 20 30 40 50 60 70Easting (km)

0

10

20

30

40

50

60

70

Nor

thin

g (k

m)

60%

0%

loca

l fra

ctio

n of

gas

dis

solv

ed

As expected, mf is low where F is high → global fraction dissolved is less!



∆M =

∫∫∆m dxdy ≈

∫∫(1/F − 1)mf dxdy.

Mass/area: mf Fraction dissolved: F Change in mass, ∆m

0 10 20 30 40 50 60 700

10

20

30

40

50

60

70

Easting (km)

Nor

thin

g (k

m)

100

200

300

400

500

600

700

800

900

1000

1100

0

gas

mas

s pe

r uni

t are

a [k

g/m

2 ]

10%

20%

30%

40%

50%

0 10 20 30 40 50 60 70Easting (km)

0

10

20

30

40

50

60

70

Nor

thin

g (k

m)

60%

0%

loca

l fra

ctio

n of

gas

dis

solv

ed

50

100

150

200

250

300

350

400

450

00 10 20 30 40 50 60 70

Easting (km)

0

10

20

30

40

50

60

70

Nor

thin

g (k

m)

mas

s lo

ss p

er u

nit a

rea

[kg/

m2 ]




∆M =

∫∫∆m dxdy ≈

∫∫(1/F − 1)mf dxdy.

Mass/area: mf Fraction dissolved: F Change in mass, ∆m

0 10 20 30 40 50 60 700

10

20

30

40

50

60

70

Easting (km)

Nor

thin

g (k

m)

100

200

300

400

500

600

700

800

900

1000

1100

0

gas

mas

s pe

r uni

t are

a [k

g/m

2 ]

10%

20%

30%

40%

50%

0 10 20 30 40 50 60 70Easting (km)

0

10

20

30

40

50

60

70

Nor

thin

g (k

m)

60%

0%

loca

l fra

ctio

n of

gas

dis

solv

ed

50

100

150

200

250

300

350

400

450

00 10 20 30 40 50 60 70

Easting (km)

0

10

20

30

40

50

60

70

Nor

thin

g (k

m)

mas

s lo

ss p

er u

nit a

rea

[kg/

m2 ]



Magnitude of CO2 dissolution at Bravo Dome

1 Mass of gas dissolved at Bravo Dome:∆M = 366± 122 MtCO2.Equivalent to 65 years of emissionsfrom US coal power plant.

2 Total mass of CO2 emplaced at BravoDome is Mt = 1.6± 0.7GtCO2.Equivalent to annual global volcanicCO2 emissions.

3 At Bravo Dome only 22%±7% ofthe emplaced CO2 have dissolved.Much less than the maximum localdissolution in NE.

free CO2 77%

dissolved CO2 23%

Uncertainty is mainly due to variations in height of gas column!


Magnitude of CO2 dissolution at Bravo Dome

1 Mass of gas dissolved at Bravo Dome:∆M = 366± 122 MtCO2.Equivalent to 65 years of emissionsfrom US coal power plant.

2 Total mass of CO2 emplaced at BravoDome is Mt = 1.6± 0.7GtCO2.Equivalent to annual global volcanicCO2 emissions.

3 At Bravo Dome only 22%±7% ofthe emplaced CO2 have dissolved.Much less than the maximum localdissolution in NE.

free CO2 77%

dissolved CO2 23%

Uncertainty is mainly due to variations in height of gas column!


Outline






Stratigraphic architecture of reservoir

Porosity and permeability

Capillary entry pressure

10-2 10-1 100 101 102 1030

100

200

[mD]

n = 35460 0.1 0.2 0.30

100

200

[-]

High capillary entry pressure prevents CO2 entry into the siltstone.





10-2 10-1 100 101 102 1030

100

200

[mD]

n = 35460 0.1 0.2 0.30

100

200

[-]

sandsilt






10-2 10-1 100 101 102 1030

100

200

[mD]

n = 35460 0.1 0.2 0.30

100

200

[-]42 mD




Porosity and permeability Capillary entry pressure

10-2 10-1 100 101 102 1030

100

200

[mD]

n = 35460 0.1 0.2 0.30

100

200

[-]42 mD

0.0 0.2 0.4 0.6 0.8 1.00.0

0.5

1.0

1.5

2.0

2.5

[MPa

]

[-]

siltstone

sandstone




Porosity and permeability Capillary entry pressure

10-2 10-1 100 101 102 1030

100

200

[mD]

n = 35460 0.1 0.2 0.30

100

200

[-]42 mD

0.0 0.2 0.4 0.6 0.8 1.00.0

0.5

1.0

1.5

2.0

2.5

[MPa

]

[-]

siltstone

sandstone



Dissolution into residual brine during emplacement

0 0.1 0.25695

705

715

725

735

745

φ, φg [-]

silt}} sand

B 5 15 25 35 45 55 65 B’500

600

700

800

900

granitic basementbrine

elev

atio

n [m

]

source

distance along cross-section [km]

anhydrite

0 20 40 60 80 100 1200

0.2

0.4

0.6

0.8

1

1.2

Pressure [bar]

CO

2so

lubility[m

ol

kg]

Bravo Dome MeasurmentsDuan et al. (2003): pure waterDuan et al. (2003): 2 molal NaCl

Easting (km)

No

rth

ing

(km

)

0 25 50 750

25

50

75

0

50

100

150CO

2(aq)

[kg/m2]



0 0.1 0.25695

705

715

725

735

745

φ, φg [-]

silt}} sand

B 5 15 25 35 45 55 65 B’500

600

700

800

900


elev

atio

n [m

]

source


anhydrite

0 20 40 60 80 100 1200

0.2

0.4

0.6

0.8

1

1.2

Pressure [bar]

CO

2so

lubility[m

ol

kg]


Easting (km)

No

rth

ing

(km

)

0 25 50 750

25

50

75

0

50

100

150CO

2(aq)

[kg/m2]



0 0.1 0.25695

705

715

725

735

745

φ, φg [-]

silt}} sand

B 5 15 25 35 45 55 65 B’500

600

700

800

900


elev

atio

n [m

]

source


anhydrite

0 20 40 60 80 100 1200

0.2

0.4

0.6

0.8

1

1.2

Pressure [bar]

CO

2so

lubility[m

ol

kg]


Easting (km)

No

rth

ing

(km

)

0 25 50 750

25

50

75

0

50

100

150CO

2(aq)

[kg/m2]



0 0.1 0.25695

705

715

725

735

745

φ, φg [-]

silt}} sand

B 5 15 25 35 45 55 65 B’500

600

700

800

900


elev

atio

n [m

]

source


anhydrite

0 20 40 60 80 100 1200

0.2

0.4

0.6

0.8

1

1.2

Pressure [bar]

CO

2so

lubility[m

ol

kg]


Easting (km)

No

rth

ing

(km

)

0 25 50 750

25

50

75

0

50

100

150CO

2(aq)

[kg/m2]



0 0.1 0.25695

705

715

725

735

745

φ, φg [-]

silt}} sand

B 5 15 25 35 45 55 65 B’500

600

700

800

900


elev

atio

n [m

]

source


anhydrite

0 20 40 60 80 100 1200

0.2

0.4

0.6

0.8

1

1.2

Pressure [bar]

CO

2so

lubility[m

ol

kg]


Easting (km)

No

rth

ing

(km

)

0 25 50 750

25

50

75

0

50

100

150CO

2(aq)

[kg/m2]



0 0.1 0.25695

705

715

725

735

745

φ, φg [-]

silt}} sand

B 5 15 25 35 45 55 65 B’500

600

700

800

900


elev

atio

n [m

]

source


anhydrite

0 20 40 60 80 100 1200

0.2

0.4

0.6

0.8

1

1.2

Pressure [bar]

CO

2so

lubility[m

ol

kg]


Easting (km)

No

rth

ing

(km

)

0 25 50 750

25

50

75

0

50

100

150CO

2(aq)

[kg/m2]


How much dissolved during emplacementMain reservoir segment Map of Bravo Dome: NE reservoir segment:

residual brine 53%

aquifer 47%

10%

20%

30%

40%

50%

0 10 20 30 40 50 60 70Easting (km)

0

10

20

30

40

50

60

70

Nor

thin

g (k

m)

60%

0%

loca

l fra

ctio

n of

gas

dis

solv

ed

residualbrine 14%

aquifer 86%

1 Significant amounts dissolved into ’residual brine’ during emplacement.Highlights positive effect of heterogeneity on dissolution!

2 Significant amounts dissolved into underlying aquifer after emplacement.Provides field evidence for enhanced dissolution due to brine flow.


Outline






CO2 emplacement and regional volcanismDistribution of regional volcanism Age of regional volcanism

[MPa]2 4 6 8 10 12 14

−60 −40 −20 0 20 40 60 80

20

40

60

80

100

120

140

160

1.7Ma−56ka9Ma−2.2Ma

easting [km]

north

ing

[km

]

TexasO

klahoma

Colorado

T1

095

Folsom SiteFolsom SiteCapulin volcanoCapulin volcano

New Mexico

volcanic ages:

T2

Assumed age of Bravo Dome is 10ka.

Three major volcanic phases:1 Raton phase: 9.0 - 3.5 Ma2 Clayton phase: 3.0 -2.25 Ma3 Capulin phase: 1.7 - 0.04 Ma

Independent estimate of CO2 age!

Stroud (1996) M.S. Thesis, NM Tech


Dating CO2 emplacment with thermochronologyCore sample with Apatite crystal

(U-Th)/He thermochronology

Apatite accumulates He from radioactive decay below T = 75◦C.Current reservoir conditions T = 35◦C → heating by ∆T ≈ 40◦C

Hot volcanic CO2 entered Bravo Dome 1.2-1.5 Ma ago.


Dating CO2 emplacment with thermochronologyCore sample with Apatite crystal (U-Th)/He thermochronology




Dating CO2 emplacment with thermochronologyCore sample with Apatite crystal (U-Th)/He thermochronology




Estimate IPCC–diagram for Bravo Dome

100 101 102 103 104

100%

80%

60%

40%

20%

0%

Frac

tion

of C

O2 tr

appe

d


free CO2

solubility trapping

residual trapping

mineral trapping


Estimate IPCC–diagram for Bravo Dome

Bravo Dome

100%

80%

60%

40%

20%

0%

Frac

tion

of C

O2 tr

appe

d

100 101 102 103 104

Time since emplacement [yrs]105 106 107

free CO2

solubility trapping


Outline






Outline






Pressures gradients at Bravo Dome

[MPa]2 4 6 8 10 12 14

−60 −40 −20 0 20 40 60 80

20

40

60

80

100

1.7Ma−56ka9Ma−2.2Ma

easting [km]

north

ing

[km

]

Texas

095

volcanic ages:

?

?

??

?

?

Is the reservoir still filling?If not, why didn’t the pressure gradient relax?


Sub-hydrostatic gas pressures at Bravo Dome

Bravo Dome gas pressure

Pressure compartments

0 2 4 6 8 10

600

650

700

750

800

850

900

Gas Pressure (MPa)

Dep

th (m

)

AB

C

D

EF

ρwg

ρgg

pe



Bravo Dome gas pressure Pressure compartments

0 2 4 6 8 10

600

650

700

750

800

850

900

Gas Pressure (MPa)

Dep

th (m

)

AB

C

D

EF

ρwg

ρgg

pe

103103.2103.4103.6103.835.6

35.8

36

36.2

36.4

Longitude (°W)La

titud

e (°

N)

A

BC

DE

F

S

T


Stratigraphic controls on compartmentalization

Pressure compartments

Gas volume fraction s̃and fraction

103103.2103.4103.6103.835.6

35.8

36

36.2

36.4

Longitude (°W)

Latit

ude

(°N

)

A

BC

DE

F

S

T



Pressure compartments Gas volume fraction s̃and fraction

103103.2103.4103.6103.835.6

35.8

36

36.2

36.4

Longitude (°W)

Latit

ude

(°N

)

A

BC

DE

F

S

T

0

10

20

30

40

50

60

70

Nor

thin

g (k

m)

4%

6%

8%

10%

12%

14%

16%

0 10 20 30 40 50 60 70Easting (km)

gas

volu

me

frac

tion:

φS



B 5 15 25 35 45 55 65 B’500

600

700

800

900


elev

atio

n [m

]

source


compartment 1

compartment 2

compartment 3

CO2 is stored in a number of closed compartments?



B 5 15 25 35 45 55 65 B’500

600

700

800

900


elev

atio

n [m

]

source


compartment 1

compartment 2

compartment 3

CO2 is stored in a number of closed compartments?


Outline







14 ± 3%

5%

16%

Total Subhydrostatic Pressure = 6.3 MPa

Regional Subhydrostatic

Ogallala Depletion

Erosional Unloading

Cooling of Volcanic CO2

Dissolution of CO2into Brine

Un-Explained

41 ± 30%

10 ± 5%

14 ± 4%



14 ± 3%

5%

16%

Total Subhydrostatic Pressure = 6.3 MPa

Regional Subhydrostatic

Ogallala Depletion

Erosional Unloading

Cooling of Volcanic CO2

Dissolution of CO2into Brine

Un-Explained

41 ± 30%

10 ± 5%

14 ± 4%

250 300 350 400 450 5000

2

4

6

8

10

12

Temperature (K)P

ressu

re (

Mp

a)

CO2 Isodensity Diagram

Dissolution Effect

∆P = 0.7 - 1.1 MPa


Regional underpressure

33˚ N

34˚ N

35˚ N

36˚ N

37˚ N

38˚ N

Latit

ude

Langitude100˚ W101˚ W102˚ W103˚ W104˚ W105˚ W106˚ W

A A’


Regional underpressure

33˚ N

34˚ N

35˚ N

36˚ N

37˚ N

38˚ N

Latit

ude

Langitude100˚ W101˚ W102˚ W103˚ W104˚ W105˚ W106˚ W

A A’

Elev

atio

n (ft

)

-6000

-4000

-2000

0

2000

4000

6000

100˚ W101˚ W102˚ W103˚ W104˚ W

Langitude

A’A

Precambrian Basement

TXNMAnadarko

DalhartBasin

Wolfcampion


Underpressure due to regional evaporite


Underpressure due to regional evaporite

Permian Evaporite


Underpressure is normal in natural CO2 reservoirs

Gas Pressure (MPa)

De

pth

(m

)

0

4000

3000

2000

1000

0 302010

SJE

DMMD

KD

GC

MC

MD

L

GC

KD

MC

GCL

MD

SJ E

DM

B1

B5

B4

B2

B3

BD

DM: Des Moines

E: Estancia

GC: Gordon Creek

KD: Kevin Dome

L: Lisbon

MC: Mc Callum

MD: McElmo Dome

SJ: St. Johns

B1: Denver Basin

B2: Anadarko Basin

B3: Arkoma Basin

B4 Palo Duro Basin

B5: San Juan Basin

ρw g


Outline






Summary and conclusionNatural analogs for geological CO2 storage

1 Large amounts of data are freely available.2 Provide constraints in long-term fate of geological CO2 storage

Dissolution at Bravo Dome1 Estimate that 366 MtCO2 have dissolved.2 50% dissolves into residual brine during emplacement.3 50% dissolves after emplacement into aquifer.4 Emplacement of CO2 1.2-1.4 Ma ago.

Pressures at Bravo Dome1 Pressure is significantly below hydrostatic (common).2 CO2 dissolution can reduce pressure in compartmentalized reservoir.3 Permian evaporites are associated with large regional underpressure.












Implications for CO2 storageTrapping and safety

1 Structural trapping contained large volume over millenial timescales.2 Dissolution trapping is slower then expected.3 Dissolution trapping is nice, but not strictly necessary.

Geological CO2 storage on a scale large enough to matter?1 Sleipner is an example of successful storage in an optimal formation.2 Bravo Dome is an example if successful storage in a formation

that would not be considered optimal.

Low-pem & low-pressure formations as CO2 storage targets1 Previously considered for hazardous waste injection.2 CO2 is less mobile than in high perm formations.3 Inject large amounts before reaching hydrostatic pressure.4 CO2 dissolution reduces pressure over time.




















Thank you for your attention.


Copyright of Shell Global Solutions International B.V.

MULTIPHASE FLOW MODELLING OF CALCITE DISSOLUTION PATTERNS FROM CORE SCALE TO RESERVOIR SCALE

Jeroen Snippe, Holger Ott Shell Global Solutions International B.V.

1 October 2015

Presentation for UKCCSRC Specialist Meeting on Flow and Transport for CO2 Storage

Imperial College London,

30th October 2015


DEFINITIONS & CAUTIONARY NOTE

Reserves: Our use of the term “reserves” in this presentation means SEC proved oil and gas reserves.

Resources: Our use of the term “resources” in this presentation includes quantities of oil and gas not yet classified as SEC proved oil and gas reserves. Resources are consistent with the Society of Petroleum Engineers 2P and 2C definitions.

Organic: Our use of the term Organic includes SEC proved oil and gas reserves excluding changes resulting from acquisitions, divestments and year-average pricing impact.

Resources plays: Our use of the term ‘resources plays’ refers to tight, shale and coal bed methane oil and gas acreage.

The companies in which Royal Dutch Shell plc directly and indirectly owns investments are separate entities. In this presentation “Shell”, “Shell group” and “Royal Dutch Shell” are sometimes used for convenience where references are made to Royal Dutch Shell plc and its subsidiaries in general. Likewise, the words “we”, “us” and “our” are also used to refer to subsidiaries in general or to those who work for them. These expressions are also used where no useful purpose is served by identifying the particular company or companies. ‘‘Subsidiaries’’, “Shell subsidiaries” and “Shell companies” as used in this presentation refer to companies in which Royal Dutch Shell either directly or indirectly has control. Companies over which Shell has joint control are generally referred to as “joint ventures” and companies over which Shell has significant influence but neither control nor joint control are referred to as “associates”. The term “Shell interest” is used for convenience to indicate the direct and/or indirect ownership interest held by Shell in a venture, partnership or company, after exclusion of all third-party interest.

This presentation contains forward-looking statements concerning the financial condition, results of operations and businesses of Royal Dutch Shell. All statements other than statements of historical fact are, or may be deemed to be, forward-looking statements. Forward-looking statements are statements of future expectations that are based on management’s current expectations and assumptions and involve known and unknown risks and uncertainties that could cause actual results, performance or events to differ materially from those expressed or implied in these statements. Forward-looking statements include, among other things, statements concerning the potential exposure of Royal Dutch Shell to market risks and statements expressing management’s expectations, beliefs, estimates, forecasts, projections and assumptions. These forward-looking statements are identified by their use of terms and phrases such as ‘‘anticipate’’, ‘‘believe’’, ‘‘could’’, ‘‘estimate’’, ‘‘expect’’, ‘‘intend’’, ‘‘may’’, ‘‘plan’’, ‘‘objectives’’, ‘‘outlook’’, ‘‘probably’’, ‘‘project’’, ‘‘will’’, ‘‘seek’’, ‘‘target’’, ‘‘risks’’, ‘‘goals’’, ‘‘should’’ and similar terms and phrases. There are a number of factors that could affect the future operations of Royal Dutch Shell and could cause those results to differ materially from those expressed in the forward-looking statements included in this presentation, including (without limitation): (a) price fluctuations in crude oil and natural gas; (b) changes in demand for Shell’s products; (c) currency fluctuations; (d) drilling and production results; (e) reserves estimates; (f) loss of market share and industry competition; (g) environmental and physical risks; (h) risks associated with the identification of suitable potential acquisition properties and targets, and successful negotiation and completion of such transactions; (i) the risk of doing business in developing countries and countries subject to international sanctions; (j) legislative, fiscal and regulatory developments including potential litigation and regulatory measures as a result of climate changes; (k) economic and financial market conditions in various countries and regions; (l) political risks, including the risks of expropriation and renegotiation of the terms of contracts with governmental entities, delays or advancements in the approval of projects and delays in the reimbursement for shared costs; and (m) changes in trading conditions. All forward-looking statements contained in this presentation are expressly qualified in their entirety by the cautionary statements contained or referred to in this section. Readers should not place undue reliance on forward-looking statements. Additional factors that may affect future results are contained in Royal Dutch Shell’s 20-F for the year ended 31 December, 2014 (available at www.shell.com/investor and www.sec.gov ). These factors also should be considered by the reader. Each forward-looking statement speaks only as of the date of this presentation, 2 October, 2015. Neither Royal Dutch Shell nor any of its subsidiaries undertake any obligation to publicly update or revise any forward-looking statement as a result of new information, future events or other information. In light of these risks, results could differ materially from those stated, implied or inferred from the forward-looking statements contained in this presentation. There can be no assurance that dividend payments will match or exceed those set out in this presentation in the future, or that they will be made at all.

We use certain terms in this presentation, such as discovery potential, that the United States Securities and Exchange Commission (SEC) guidelines strictly prohibit us from including in filings with the SEC. U.S. Investors are urged to consider closely the disclosure in our Form 20-F, File No 1-32575, available on the SEC website www.sec.gov. You can also obtain this form from the SEC by calling 1-800-SEC-0330.

October 2015


INTRO: CALCITE DISSOLUTION DURING CO2 INJECTION

Context: CO2 storage/EOR

CO2 injection → acidification →

carbonate dissolution

3 October 2015

Fred and Fogler (1999), SPE 56995

Experiments show ‘wormholing’ for CO2 -saturated brine injection

Similar to patterns in extensive acid stimulation literature

Very limited experimental work done with gas/SC CO2 injection

Model investigation

Impact of gas phase

Upscaling to field scale


MODELLING APPROACH

Using in-house dynamic multiphase reservoir flow simulator (MoReS) coupled to open-source geochemical package (PHREEQC v3)

4 October 2015

Detailed model (core scale)

Explicit representation of WH patterns

Grid resolution << WH diameter

Chemistry including kinetics (phreeqc.dat, Palandri & Kharaka)

2-phase flow description including capillary effects and diffusion

Permeability, capillary pressure, relperms modified during dissolution

Continuum scale (Darcy) model → flow within WH approximate

Effective model (core scale to well/reservoir scale) [2nd part of presentation]

Implicit representation of WH patterns

Generalised to 2-phase case with CO2

Parameters tuned to detailed model and experiments


DETAILED MODEL

5 October 2015


3D

SOME MODEL RESULTS (SINGLE PHASE)

6 October 2015

WH competition 5mm…

Most of fine-scale simulations done in 2D

Compact dissolution Conical dissolution Conical wormhole

Ramified wormholes Homogeneous dissol. Dominant wormhole

2D

WH width 2 mm


MODEL VALIDATION (SINGLE PHASE)

7 October 2015

Ramified wormholes

Uniform dissolution

Dominant wormhole

Con

ical

w

orm

hole

Com

pact

di

ssol

utio

n

Dah

mko

hler

num

ber

(rea

ctio

n ra

te/c

onve

ctio

n ra

te)

Peclet number (convection rate/diffusion rate)

MoReS results (colour) plotted on domain boundaries from Golfier et al., J. Fluid Mech. (2002), vol. 457, pp. 213-254 with experimental patterns from Fred and Fogler (1999), SPE 56995


TWO-PHASE EXPERIMENT/MODEL RATIONALE

Experiment

Two experiments were done at Shell with CO2 + brine co-injection

This is ~representative for the conditions somewhat behind the CO2 plume front in CCS

Pure CO2 injection WH experiment would be more challenging

longer core to resolve profiles (gas saturation, calcite dissolution)

high CT signal:noise to resolve subtle calcite dissolution patterns

Model:

Model experiment with CO2 + brine co-injection and compare results

Derive upscaled (effective) model description

Apply effective model to pure CO2 injection (larger model dimensions)

8 October 2015


2-PHASE RELPERM AND CAPILLARY PRESSURE

9 October 2015

During dissolution

Interpolation between curves (linear in porosity)

Power law scaling of permeability with porosity

0.0

1.5

3.0

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Cap

illar

y p

ress

ure

(G

as-W

ate

r) [

bar

]

Re

lati

ve p

erm

eab

ility

Gas saturation

krw matrix

krg matrix

krw cavity

krg cavity

Pc matrix

Pc cavity


TWO-PHASE MODEL RESULTS (CO-INJECTION)

10 October 2015

The gas phase slightly suppresses WH velocity

260 PV

single-phase two-phase co-injection (same rate)

720 PV

560 PV

760 PV

760 PV

2000 PV

260 PV

880 PV

760 PV

880 PV

760 PV

2000 PV


1

10

100

1000

10000

0.001 0.010 0.100 1.000 10.000 100.000

Po

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Interstitial Water Velocity (cm/min)

Brine + gas

Observed

TWO-PHASE MODEL RESULTS (CO-INJECTION)

11 October 2015

Most suppression around optimal flow rates (~dominant WH regime)


TWO-PHASE MODEL RESULTS: ANALYSIS/COMPARISON

12 October 2015

2-phase, 1+1 ml/min co-inj. 1-phase, 1ml/min

Porosity

Experiment (Shell) (Porosity)

Ott et al. (2013) SCA2013-029

Gas saturation

Water flux (log scale)

760 PV 880 PV


Ott, H., and S. Oedai (2015) Geophys. Res. Lett., 42, 2270–2276 doi:10.1002/2015GL063582

2ND SHELL EXPERIMENT: WH SUPPRESSION

In this experiment gas co-injection seems to trigger transition from dominant WH into conical WH/compact dissolution

Slumping reproduced in model runs with gravity (only investigated on small diameter core)

13 October 2015


EFFECTIVE MODEL APPROACH

14 October 2015


LEARNINGS FROM ACID STIMULATION LITERATURE

15 August 2015

Acid stimulation literature (single phase): Universally shaped curve #PVBT vs vi (or vWH vs vi)

Location of curve depends on phi, perm, aspect ratio, HCl strength, …

‘Global Wormholing Model’ (GWM), Talbot&Gdanski (2008), SPE 113042, offers ~universal parameterisation

~predictive vWH vs vi for given phi, perm, HCl strength, etc.

Buijse & Glasbergen (2005), SPE 96892


GWM CHARACTERISTICS

16 October 2015

Talbot&Gdanski (2008), SPE 113042


1

10

100

1000

10000

0.001 0.010 0.100 1.000 10.000 100.000 1000.000

Po

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s to

Bre

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gh

Interstitial Water Velocity (cm/min)

Brine + gas Observed

compact dissolution limit Model (fit)

Model (solubility-equivalent HCl) Model (pH-equivalent HCl)

GWM APPLICATION TO CO2-BRINE

17 August 2015

Deviation in single WH regime because Poiseuille flow profile

in model poorly resolved or grid

resolution too coarse

Model was run in 2D, for which GWM

model is overshooting in face dissolution

regime

GWM model fitted by tuning HCl strength

Resulting GWM model also fits available experimental data well (next slides)

GWM model applied to dynamic flow simulations by locally accounting for calcite saturation index through HCl strength parameter

For 2-phase use same GWM parameters – use the water vi as input velocity


1

10

100

1000

10000

0.001 0.01 0.1 1 10 100 1000

Po

re V

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s to

Bre

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Interstitial Fluid Velocity (cm/min)

Model Observed

FITTED MODEL COMPARISON TO EXPERIMENTS OTT ET AL. (SHELL) 2013 – SCA 2013-029

18 October 2015

L=5.91”, d=2.95”

q=1 mL/min

Estaillades limestone

φ=0.278, k=270 mD

T=50 °C, p=100 bar


1

10

100

1000

10000

0.001 0.01 0.1 1 10 100 1000

Po

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Model Observed

FITTED MODEL COMPARISON TO EXPERIMENTS CAROLL ET AL. (LLNL) 2013 - IJGHGC 16S (2013) S185–S193

19 October 2015

L=1.18”, d=0.59”

q=0.05 mL/min

Calculated HCl equivalent based on undersaturated CO2 molality

Weyburn limestone (59% calcite)

φ=0.15, k=0.032 mD

T=60 °C, p=248 bar, p_CO2 = 30 bar


1

10

100

1000

10000

0.001 0.01 0.1 1 10 100 1000

Po

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s to

Bre

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Model Observed

FITTED MODEL COMPARISON TO EXPERIMENTS VIALLE ET AL. 2014 - J. GEOPHYS. RES. SOLID EARTH, 119, 2828–2847

20 October 2015

L=0.13.8”, d=3.94”

q=5 mL/min

Salinity = 25000 ppm


Estaillades limestone

φ=0.286, k=120 mD

T=20 °C, p_CO2 = 1 bar


1

10

100

1000

10000

0.001 0.01 0.1 1 10 100 1000

Po

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s to

Bre

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Model Observed

FITTED MODEL COMPARISON TO EXPERIMENTS LUQUOT ET AL. 2011 - TRANSP POROUS MED (2014) 101:507–532

21 October 2015

L=0.71”, d=0.35”

q=0.08 mL/min


Alcobaa limestone

φ=0.15, k=0.24 mD

T=100 °C, p=120 bar, p_CO2 = 34 bar


1

10

100

1000

10000

0.001 0.01 0.1 1 10 100 1000

Po

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Bre

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Model Observed

FITTED MODEL COMPARISON TO EXPERIMENTS SVEC & GRIGG 2001 - SPE 71496

22 October 2015

L=20.3”, d=1.98”

q=17 mL/min

Indiana limestone

φ=0.123, k=35.7 mD

T=38 °C, p=138 bar

Salinity=86950 ppm


1

10

100

1000

10000

0.001 0.01 0.1 1 10 100 1000

Po

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s to

Bre

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Model Observed

FITTED MODEL COMPARISON TO EXPERIMENTS LUQUOT & GOUZE 2009 - CHEMICAL GEOLOGY 265 (2009) 148–159

23 October 2015

L=0.71”, d=0.35”

q=1.14 mL/min

Mondeville limestone

φ=0.075, k=35.7 mD

T=100 °C, p=120 bar, p_CO2 = 100 bar


1

10

100

1000

10000

0.001 0.01 0.1 1 10 100 1000

Po

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Bre

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Model Observed

FITTED MODEL COMPARISON TO EXPERIMENTS MENKE 2015 - IMPERIAL COLLEGE LONDON – PRIVATE COMM

24 October 2015

L=0.47”, d=0.16”

q=0.5 mL/min

Salinity = 60000 ppm

Portland limestone

φ=0.045, k=0.096 mD

T=50 °C, p=100 bar


∆ Pressure

Reference case: no WH’s

EFFECTIVE MODEL RESULTS (LINEAR MODEL, 1METER)

CO2-saturated brine injection: Potential for large injectivity increase

Pure CO2 injection: Short/no wormholes. Negligible impact on injectivity 25 October 2015

Pure CO2 injection (1cm/min) CO2-sat brine injection (1cm/min)

WH velocity

Gas saturation

WH length


EFFECTIVE MODEL RESULTS (RADIAL MODEL, R=50 METER)

26 October 2015

Pure CO2 injection (0.5 MT/year) CO2-sat brine injection (0.5 MT/year)

Gas saturation

Injection pressure

Reference case: no WH’s

WH length

Same conclusions as for linear model Note for pure CO2 injection: WH length decreases with distance (cf. linear: ~constant)


ANALYSIS OF RESULTS (RADIAL MODEL)

27 October 2015

0.000001

0.000010

0.000100

0.001000

0.010000

0.100000

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 100 200 300 400 500 600

WH

ve

loci

ty (

cm/m

in),

WH

len

gth

(cm

),

po

rosi

ty c

han

ge (

m3

/m3

), p

erm

mu

lt -

1

gas

satu

rati

on

(m

3/m

3)

Radial distance (cm)

SAT_GAS

Vwh

Lwh

DPHI

PERMX_MULT -1

0.000001

0.000010

0.000100

0.001000

0.010000

0.100000

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

300 320 340 360 380 400

WH

ve

loci

ty (

cm/m

in),

WH

len

gth

(cm

),

po

rosi

ty c

han

ge (

m3

/m3

), p

erm

mu

lt -

1

gas

satu

rati

on

(m

3/m

3)

Radial distance (cm)

SAT_GAS

Vwh

Lwh

DPHI

PERMX_MULT -1

Only thin region in which conditions are favourable for WH growth

Far ahead of gas front gradual increase in acidity → always close to calcite equillibrium → outside WH regime (too low Da#)

Note: calcite solubility in CO2-saturated brine controls final porosity change


1

10

100

1000

10000

0.001 0.01 0.1 1 10 100 1000

Po

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Bre

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Base caseVi -> 0 (ideal compact dissol)L/A=2L/A = .67T=-65T=30T=50HCl=.002617HCl=.05HCl=.238Estaillades exp (L/A = .34)Model 2D (L/A=15)best fit to 9.8 cm2/g MoReS

SENSITIVITY TO GWM PARAMETER UNCERTAINTY RANGE

28 October 2015

Parameter ranges based on (wide) envelope around experimental and model results

For radial application, base case L/A ≈1cm-1 based on acid stimulation radial corefloods and field application experience


SENSITIVITY RESULTS: IMPACT ON WH LENGTH (1-PHASE)

29 October 2015

0

100

200

300

400

500

600

700

800

900

1,000

0 20 40 60 80 100

Wo

rmh

ole

len

gth

(cm

)

Distance from sandface (cm)

ref case HCld238 HCld005 LdAd67

LdA2 Tm30 T50

Strong sensitivity, especially to acid strength parameter

In all cases strong wormhole growth initiating at sandface

Hypothetical WH’s initiating ahead of sandface overtaken (shock front)

After several months of injection


Weaker sensitivities than in 1-phase case

Conclusions from reference case run appear robust, i.e.: short/no wormholes (LWH < 0.05 cm)

perm multiplier < 1.01 for LWH < 5cm (2D) or 2cm (3D) [next slides]

SENSITIVITY RESULTS: IMPACT ON WH LENGTH (2-PHASE)

30 October 2015

0.000

0.010

0.020

0.030

0.040

0.050

0.060

0 100 200 300 400 500

Wo

rmh

ole

len

gth

(cm

)

Distance from sandface (cm)

ref case HCld238 HCld005 LdAd67

LdA2 Tm30 T50


REMARK ON EFFECTIVE PERMEABILITY MULTIPLIER

In pure CO2 injection case WH’s would initiate away from sandface

Q: how assign effective perm?

Matrix background and high perm WH channels

Assume random WH initiation pattern

Assume idealised dominant WH’s

Straight channel WH = 2mm

From Poiseuille flow, kWH ≈ 105 D

Control parameters ∆φ and LWH

Considered both 2D and 3D

Considered enhanced connectivity case (~ WH angle distribution/bifurcations)

31 October 2015


Numerical upscaling

Simple formula gives good fit, for all ∆φ , all LWH, all perm contrasts

𝑘𝑒𝑓𝑓

𝑘𝑚− 1 =

𝑘𝑔𝑒𝑜𝑚(∆φ)

𝑘𝑚− 1

𝐿𝑊𝐻

𝑐1

𝑐2 (+bounded by harm and arithm)

REMARK ON EFFECTIVE PERMEABILITY MULTIPLIER

32 October 2015

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E+06

1.E-06 1.E-05 1.E-04 1.E-03 1.E-02 1.E-01 1.E+00

k_mult_harm - 1

k_mult_geom - 1

k_mult_arithm - 1

WH_kmult1Min1

WH_kmult2Min1

Fit

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E+06

1.E-06 1.E-05 1.E-04 1.E-03 1.E-02 1.E-01 1.E+00

k_mult_harm - 1

k_mult_geom - 1

k_mult_arithm - 1

WH_kmult1Min1

WH_kmult2Min1

Fit

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E+06

1.E-06 1.E-05 1.E-04 1.E-03 1.E-02 1.E-01 1.E+00

k_mult_harm - 1

k_mult_geom - 1

k_mult_arithm - 1

WH_kmult1Min1

WH_kmult2Min1

Series7

Example - perm contrast 𝑘𝑊𝐻

𝑘𝑚= 400, LWH =100mm

log(∆φ)

log

𝐤𝐖

𝐇

𝐤𝐦

−𝟏

10-6 1 10-6

10+6

Note: for pure CO2 injection: ∆φ≈10-4


CONCLUSIONS

At fixed brine rate, gas co-injection causes some suppression of calcite dissolution patterns.

Modelling indicates limited suppression for any flow rate

Experiment: limited to strong suppression in dominant/conical WH regime

33 October 2015

Successfully applied effective GWM model (from acid stimulation literature) to CO2-brine system (matches fine-scale model and experiments)

Effective model predicts WH can be significant in carbonate reservoirs on operational timescale (days-years) for CO2 & water co-injection

Good for injectivity

Potentially problematic for well/rock stability (depending on WH pattern)

Effective model predicts negligible wormhole formation for pure CO2 injection (at any scale from core scale to reservoir scale)

WH formation irrelevant for pure CO2 injection projects (‘standard’ CCS)


REMARK ON REACTION KINETICS VS GWM PARAMETERS

35 August 2015

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