Modelling - IEA Greenhouse Gas R&D Programmeieaghg.org/.../3__MODELLING_Hagen_SmlSEC.pdf ·...

Modelling IEAGHG Summer School – Austin 2014

“The purpose of modelling is to capture knowledge of the subsurface in a quantitative form in order to make engineering decisions”

Sveinung Hagen

With large input from colleagues, in particular Jan Rivenæs and Philip Ringrose

Overview this presentation

• Background – with rational and motivation

• Workflow

• Input / Interpretation – data availability

• Geological modelling

− Model framework

− Gridding

− Properties

• Dynamic modelling

• Uncertainties

• Examples

• Concluding words

Capacity

Injectivity

Containment

Background Workflow Input/Interpretation Geological Modelling Dynamic Mod. Uncertainties Examples

Geological Storage of CO2

• We want to store captured CO2 underground in porous rocks that otherwise would contain water, oil or gas

• We need to be at sufficient depths that ensure CO2 is in a dense form

• These are also the depths where we are confident that natural gas has been trapped for millions of years

• But the big questions are:

− Where do we store?

− How much CO2 can be injected?

− Can we store it safely?

− Can we store it cost-effectively?

Reservoir modelling strive to quantify answers to these engineering questions

Background Workflow Input/Interpretation Geological Modelling Dynamic Mod. Uncertainties Examples Background

• Representing the subsurface reservoir (and overburden - seal) mathematically

• Essential within the oil & gas industry

• CO2-storage modelling builds on experience (workflow and tools) from oil & gas industry

• We cannot really “predict,” but make good estimates of the likely outcomes;

− modelling is a form of forecasting

A – Seismic Data B – Wells C – Static grid with properties D – Dynamic grid with properties

Workflow Input/Interpretation Geological Modelling Dynamic Mod. Uncertainties Examples Background

Comprises static, dynamic and coupled models

Reservoir modelling – Rationale

Courtesy Bjørlykke

Overall take away

Concept - make sure you have a reasonable understanding of what (geology) you are modelling

Integration – assure a reasonable consistency between the geo-model and your flow simulation

Uncertainties - models are important and a very useful, but be aware (and understand) the shortcomings - models are merely simplified reflections of reality…

Purpose - clarify/agree prior to modelling what the usage will be (capacity, flow, rock mechanics…)


Why making a model?

• Answer some of the questions posted earlier (Where? How much? Safe?)

− Capacity estimations (theoretical) and storage capabilities – (via numerical simulations)

− Well planning (injectors and/or water producers) and infrastructure requirements

− Injection/production strategies through time

• Collaboration

− Visualisation in 3D

− Utilise and integrate the data

− Improved reservoir and seal understanding

− Integrate subsurface disciplines

• Regulators (and public)

− Storage capabilities and performance


For whom do we make models?

• Decision makers – base the investment decisions on “good estimates of the likely outcome”

• Regulators – support the process when issuing storage permits and subsequent following up the storage performance

• Public/NGO’s – visualise facts and project content in a comprehensible manner


http://www.google.com/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&uact=8&docid=tBAulfDEH0R0BM&tbnid=4onNguqqIITkJM:&ved=0CAUQjRw&url=http://english99.ru/there-is-a-lot-vs-there-are-a-lot/&ei=vXSZU76QBIv9ywO4uIHABA&bvm=bv.68911936,d.bGQ&psig=AFQjCNHCY1f758za_nWEzghne9a4RaiXRw&ust=1402652176452076

What do we model?

Courtesy Roxar

• Geometries and architecture of the reservoir/seal

• Faults and barriers (“visible” and “invisible”)

• CO2 phase behaviour

• Rock properties

• Dissolution

• Geochemistry

• Rock-mechanics

• Multi-phase flow

• Well design and engineering

• Numerical representation of flow processes


• Effects of reservoir geology on CO2 injection and storage are critical (but difficult to quantify)

• Good appreciation of geology is essential

Tidal Delta Sedimentary Architecture (Niell Klinter Formation, Greenland)

Normal fault gouge with clay smear (Sinai, Egypt)

Lamina-scale permeability variations (Tilje Fm., Norway)


«Geology controls»

Consequences (site development,

new well, etc.) Courtesy Harold Levin

Background Input/Interpretation Geological Modelling Dynamic Mod. Uncertainties Examples Workflow

Overall work flow

“Workflow illustrated”

Seismic interpretation Seismic framework Zonation - Isochores Geological framework

3D Geo- and Simgrid Facies modelling Property modelling/ Flow simulation

Background Input/Interpretation Geological Modelling Dynamic Mod. Uncertainties Examples Workflow

Utilise all data (in CO2 normally less available…)

Core data

Outcrop analogues

Seismic data

Thin sections Biostratigraphy

Well logs

Concepts Production? Earlier models

Courtesy In Salah JIP

Background Workflow Geological Modelling Dynamic Mod. Uncertainties Examples Input/Interpretation

Looking for data…?

• We spend to much time looking for data!

• Efficient data management is a key

• Modelling plan must dedicate sufficient time for data management

Background Workflow Geological Modelling Dynamic Mod. Uncertainties Examples Input/Interpretation

Seismic framework – The model based on seismic interpretation (interpreted horizons and faults)

Geological framework – The model based on combining seismic framework and zonation, where zonation is expressed as isochore maps

3D grid – The model based on making 3D grid cell from a geological framework

Background Workflow Input/Interpretation Dynamic Mod. Uncertainties Examples Geological Modelling

Framework modelling blocks

Depth Conversion

• Seismic time topography is not equal to depth topography

• Using acoustic velocity of the subsurface (and travel time) the depth is calculated

• 3D modelling is usually done in the depth domain (but different practices may occur)


Basic Geological Elements in Modelling

Well Described by a trajectory (path) and associated logs, in addition to a well history. The intersection between wells and horizons (etc) are called markers or picks. There are also thickness picks. Property Surfaces, horizons and zones may have multiple associated properties, e.g. a porosity or a fault multiplier

Zone Geological unit bounded by horizons Horizon Surface bounding a reservoir zone - Interpreted Horizons – seismic interpretation - Calculated Horizons – zonation and stratigraphic modelling computed using thickness information Surface A more or less loose term than horizon – i.e. it may be an erosion surface, a fault surface, fluid contact etc. Surfaces are 2D


Geological framework – Two concerns

Wells

What do wells measure?

Isochores

Mostly based on TVT points, well correlation and the conceptual model

Largely dependent of algorithm used…

Measured depth thickness (MDT) True vertical thickness (TST) True stratigraphic thickness (TVT)


Gridding

• The reservoir volume represented by “rectangular” (actually octahedron) cells

• A 3D grid model represents a significant simplification of the real geometries and properties

• We distinguish between a Geogrid (static) and a SimGrid (dynamic)


Gridding challenge (example)

How can this be represented by a grid? Pillars must be straight and not cross… Staircased fault to maintain cell regularity


Petrophysical modelling

• Data analysis

− Data analysis is difficult, almost an art

− Trends must be consistent with you concept! But also consider that your concept model may be wrong...

− Remember that well data may be biased!

• Porosity

− Smoothed (prediction) versus Stochastic

• Permeability

− Permeability is a direction dependent property

− Often simplified to Kxx, Kyy and Kzz (Kh & Kv)


The scale challenge

We are measuring at different scales. How can these be compared and applied correctly?


Facies modelling

Channel sandstone bodies embraced by mudstone and heterolithics in outcrop, Utah. Nature is abrupt, hence modelling a “smooth" field will often fail!


• A “Facies” in reservoir modelling is a discrete body applied for improving petrophysical classification and spatial shape

• In siliciclastic systems, modelling facies often derive from depositional facies.

• In carbonates, modelling facies may be highly related to diagenesis (e.g. dolomitisation)

We model channel deposits, not water-channels. Hence a sinuous river may produce a rather straight channel-belt deposit.

Channel bodies in Escanilla Fm, Spain Euphrates, where you see that the actual channel belt (green) is much wider than the water channel.


What is facies?

Geogrid and Simgrid

• A geogrid is aimed for static property modelling

• A simgrid is aimed for flow simulation

• These grids may be the same, but are normally separate, due to different requirements and maximum numbers of cell possible

Background Workflow Input/Interpretation Geological Modelling Uncertainties Examples Dynamic Mod.

Maths and Computation

Important to:

1. Appreciate what the software package is trying to do

2. Read the manual…, which is usually out of date so ask someone who has most recently used the functions!

3. Learn to differentiate between “good enough” and “wildly inaccurate”

Defining an appropriate grid for simulation is a difficult task requiring care and experience


Flow simulator in a “nut shell”

• The simulator starts with initial fluid saturations, pressures, and initial wells

• It then solves a large set of linear equation - Darcy's law combined with a mass conservation relation (and/or other codes dependent on objectives)

• At given time-steps, the equations are re-computed

• Hence, since a simulator is quite CPU demanding, the maximum number of cells in practice (today) is around 1-2 million

• If the geomodel has more cells (normally does), rescaling is needed


Rescaling

Representing data on another scale, to conserve both volumes and fluid flow behaviour

Fine Grid Coarse Grid

Coarse cell based on sampled Grid


Overall aim CO2 flow simulator

• We model to better understand trapping efficiencies (and hence final immobilisation of injected CO2) caused by general mechanisms like:

− Structural entrapping

− Capillary retention

− Dissolution

− Mineralisation

IPCC – WG3


• Two or more fluids flowing → Have to share permeability

• Relative permeability: The ratio of effective to absolute permeability

Water Saturation

Rel

ativ

e P

erm

eabi

lity

1 0

krg

krw

Sleipner Ref. Model 2010 SPE 134891

1

• Both capacity, injectivity and containment relates to the relative permeability (Relperm) • Keep injection pressure below fracturing pressure

• Areal spread & migration

• RelPerm functions for CO2-brine systems are much

debated

• Stanford Rel. perm explore is a useful took: http://pangea.stanford.edu/research/bensonlab/relperm/index.html


Relative permeability

• We distinguish between uncertainties and risks

Uncertainty

• The lack of certainty, a state of having limited knowledge where it is impossible to exactly describe existing state or future outcome, more then one possible outcome

• Uncertainty analysis ( for example using Monte Carlo simulations) gives you a range, statistically determined within where you expect your outcome (P10, P50, P90)

Risk

• Risk can be describe as a state of uncertainty where some possible outcomes have an undesired effect of significant loss

• Risk can be reduced and/or eliminate (plan for mitigation)

Background Workflow Input/Interpretation Geological Modelling Dynamic Mod. Examples Uncertainties

Uncertainty & Risk

“There are known knowns. There are things we know that we know.

There are known unknowns. That is to say, there are things that we now know we don’t know.

But there are also unknown unknowns. There are things we do not know we don’t know”

- Donald Rumsfeld

Our models are heavily based on interpretation, - and the uncertainty is dominated by that!


Uncertainties & Risks

• Log Resolution versus Plug Scale – Outcrop Example


Examples of data related uncertainties (issue of scale)

Examples of uncertainties

• “Nightmare cores” alsmost visable on logs…


• CO2 from the Sleipner field is stored in the Utsira Formation, North Sea

• Water depth is 80 meter, reservoir unit at 700-1100 m depth

• One CO2 injector - 38 meter perforation at ~1012 meter (TVD)

• Injected gas is ~98% CO2

• Wellhead pressure stable at ~65 bar

• Wellhead temperature held at 25°C

• More than 14 Mt CO2 have been injected (as of July 2014, ~0,9M per annum)

CO2 Plume outline


Sleipner CO2 injection site - Location

• Initial models built from pre-injection seismic:

− Coarse grid simulations which indicated a circular, dispersed plume.

• 4D monitoring data indicates a northerly extension to the plume propagation.

• IP (Invasion Percolation) modeling (Permedia Migration tool) gave closer matches to the seismic, indicating a dominance of gravity/buoyancy forces over viscous forces.

• Adjusted inputs to conventional reservoir simulations in order to capture enhanced gravity segregation and understand physio-chemical prosesses:

− Gives better matches to seismic

− Shows importance of Vertical Equilibrium (VE) assumption

− Suggests dissolution was previously overestimated

• Results presented in SPE Paper 134891, Singh et al, 2010.


Sleipner Modelling Insights

1999 2001 2002 2004 2006 2008

Seismic response map

IP simulator

FD simulator

N

Injection point


Overall take away - repeated

Concept - make sure you have a reasonable understanding of what (geology) you are modelling

Integration – assure a reasonable consistency between the geo-model and your flow simulation

Uncertainties - models are important and a very useful, but be aware (and understand) the shortcomings - models are merely simplified reflections of reality…

Purpose - clarify/agree prior to modelling what the usage will be (capacity, flow, rock mechanics…)

Presentation title

Presenters name Presenters title E-mail address ……@statoil.com Tel: +4700000000 www.statoil.com

References • Bjørlykke, Knut (in print) Petroleum Geology • Bond, C. E., Wightman, R., Ringrose, P. S., 2013. The influence of fracture anisotropy on CO2 flow. Geophysical Research

Letters, 40, 1284-1289. DOI: 10.1002/grl.50313 • Carroll, S., Walt McNab, Sharon Torres, Mike Singleton, Pihong Zhao, Wellbore integrity in carbon sequestration

environments: 1. Experimental study of Cement–Sandstone/Shale–Brine–CO2, Energy Procedia, 4, 5186-5194 (GHGT-10).

• Cavanagh, A., 2013. Calibration and prediction of the Sleipner CO2 plume from 2006 to 2012. 11th Int. Conference on Greenhouse Gas Technology (GHGT11), 18th-22nd November 2012, Kyoto, Japan. www.sciencedirect.com

• Chadwick, A., Clochard, V., Delepine, N., and others, 2010. Quantitative analysis of time-lapse seismic monitoring at the Sleipner CO2 storage operation. The Leading Edge, 29 (2). 170-177.

• Gemmer, L., Hansen, O., Iding, M. Leary, S. and Ringrose, P., 2012. Geomechanical Response to CO2 injection at Krechba, In Salah, Algeria. First Break, 30, 79-84.

• Hansen, H., Eiken, O. and Aasum, T.O. 2005. Tracing the Path of Carbon Dioxide from a Gas/Condensate Reservoir, Through an Amine Plant and Back into a Subsurface Aquifer - Case Study: The Sleipner Area, Norwegian North Sea. Paper SPE 96742 presented at Offshore Europe, 6-9 September 2005, Aberdeen, United Kingdom.

• Iding, M. & Ringrose, P., 2010. Evaluating the impact of fractures on the performance of the In Salah CO2 storage site. International Journal of Greenhouse Gas Control, 4, 242–248.

• Lopez, O., Idowa, N., Störer, S., Rueslatten, H., Boassen, T., Leary, S. & Ringrose, P., 2011. Pore-scale modelling of CO2-brine Flow Properties at In Salah, Algeria. Energy Procedia, Volume 4, 3762-3769.

• Mathieson, A., Midgley, J., Dodds, K., Wright, I., Ringrose, P. and Saoula, N., 2010. CO2 sequestration monitoring and verification technologies applied at Krechba, Algeria. The Leading Edge (February 2010), 216-221.

http://www.sciencedirect.com/

References

• Pau, G.S.H., Bell, J. B., Pruess, K., Almgren, A. S. Lijewski, M. J. and Zhang, K., 2010. High-resolution simulation and characterization of density-driven flow in CO2 storage in saline aquifers. Advances in Water Resources, 33 (4), 443-455.

• Riaz, A., Hesse, M. Tchelepi, H. A. & Orr, F. M., 2006. Onset of convection in a gravitationally unstable diffusive boundary layer in porous media. Journal of Fluid Mechanics, 548, 87-111.

• Ringrose, P., Atbi, M., Mason, D., Espinassous, M., Myhrer, Ø., Iding, M., Mathieson, A. & Wright, I., 2009. Plume development around well KB-502 at the In Salah CO2 Storage Site. First Break, 27, 81-85.

• Ringrose, P. S., Mathieson, A. S., Wright, I. W., Selama, F., Hansen, O., Bissell, R., Saoula, N. & Midgley, J. 2013. The In Salah CO2 storage project: lessons learned and knowledge transfer. 11th Int. Conference on Greenhouse Gas Technology (GHGT11), 18th-22nd November 2012, Kyoto, Japan. www.sciencedirect.com

• Rivenæs, Jan C. (2012), Applied Reservoir Modelling GEOV366, Course Material University of Bergen • Singh, V., Cavanagh, A., Hansen, H., Nazarian, B. Iding, M. and Ringrose, P., 2010. Reservoir modeling of CO2 plume

behavior calibrated against monitoring data from Sleipner, Norway. SPE paper 134891 presented at the SPE Annual Technical Conference and Exhibition held in Florence, Italy, 19–22 September 2010.

• Vasco D. W., Ferretti A., Novali F. 2008. Reservoir monitoring and characterization using satellite geodetic data: Interferometric synthetic radar observations from the Krechba field, Algeria, Geophysics, 73 (6), WA113–WA122

• Vasco, D. W., Rucci, A., Ferretti, A., Novali, F., Bissell, R. C., Ringrose, P. S. Mathieson, A. S. and Wright, I. W., 2010. Satellite-based measurements of surface deformation reveal fluid flow associated with the geological storage of carbon dioxide. Geophysical Research Letters, Vol. 37, L03303.

http://www.sciencedirect.com/

Modelling - IEA Greenhouse Gas R&D Programmeieaghg.org/.../3__MODELLING_Hagen_SmlSEC.pdf ·...

Documents

Transcript of Modelling - IEA Greenhouse Gas R&D Programmeieaghg.org/.../3__MODELLING_Hagen_SmlSEC.pdf ·...