Modelling IEAGHG Summer School Austin 2014

Transcription

1 The purpose of modelling is to capture knowledge of the subsurface in a quantitative form in order to make engineering decisions Modelling IEAGHG Summer School Austin 2014 Sveinung Hagen With large input from colleagues, in particular Jan Rivenæs and Philip Ringrose

2 Overview this presentation Capacity Background with rational and motivation Workflow Input / Interpretation data availability Geological modelling Model framework Gridding Properties Dynamic modelling Uncertainties Examples Concluding words Injectivity Containment

3 Geological Storage of CO 2 We want to store captured CO 2 underground in porous rocks that otherwise would contain water, oil or gas We need to be at sufficient depths that ensure CO 2 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 CO 2 can be injected? Can we store it safely? Can we store it cost-effectively? Reservoir modelling strive to quantify answers to these engineering questions

4 Reservoir modelling Rationale Comprises static, dynamic and coupled models Representing the subsurface reservoir (and overburden - seal) mathematically Essential within the oil & gas industry CO 2 -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 Courtesy Bjørlykke Background Workflow Input/Interpretation Geological Modelling Dynamic Mod. Uncertainties Examples

5 Overall take away Purpose - clarify/agree prior to modelling what the usage will be (capacity, flow, rock mechanics ) 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 Background Workflow Input/Interpretation Geological Modelling Dynamic Mod. Uncertainties Examples

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

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

8 What do we model? Geometries and architecture of the reservoir/seal Faults and barriers ( visible and invisible ) CO 2 phase behaviour Rock properties Dissolution Geochemistry Rock-mechanics Multi-phase flow Well design and engineering Numerical representation of flow processes Courtesy Roxar Background Workflow Input/Interpretation Geological Modelling Dynamic Mod. Uncertainties Examples

9 «Geology controls» Effects of reservoir geology on CO 2 injection and storage are critical (but difficult to quantify) Good appreciation of geology is essential Lamina-scale permeability variations (Tilje Fm., Norway) Normal fault gouge with clay smear (Sinai, Egypt) Tidal Delta Sedimentary Architecture (Niell Klinter Formation, Greenland) Background Workflow Input/Interpretation Geological Modelling Dynamic Mod. Uncertainties Examples

10 Overall work flow Courtesy Harold Levin Consequences (site development, new well, etc.) Background Input/Interpretation Geological Modelling Dynamic Mod. Uncertainties Examples Workflow

11 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

12 Utilise all data (in CO 2 normally less available ) Thin sections Biostratigraphy Outcrop analogues Well logs Core data Courtesy In Salah JIP Seismic data Earlier models Production? Concepts

13 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

14 Framework modelling blocks 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

15 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)

16 Basic Geological Elements in Modelling Zone Horizon Surface Well Property Geological unit bounded by horizons Surface bounding a reservoir zone - Interpreted Horizons seismic interpretation - Calculated Horizons zonation and stratigraphic modelling computed using thickness information 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 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. Surfaces, horizons and zones may have multiple associated properties, e.g. a porosity or a fault multiplier

17 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)

18 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)

19 Gridding challenge (example) How can this be represented by a grid? Pillars must be straight and not cross Staircased fault to maintain cell regularity

20 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)

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

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

23 What is facies? 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) Channel bodies in Escanilla Fm, Spain Euphrates, where you see that the actual channel belt (green) is much wider than the water channel. We model channel deposits, not water-channels. Hence a sinuous river may produce a rather straight channel-belt deposit.

24 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

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

26 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

27 Rescaling Representing data on another scale, to conserve both volumes and fluid flow behaviour Fine Grid Coarse Grid Coarse cell based on sampled Grid

28 Overall aim CO 2 flow simulator We model to better understand trapping efficiencies (and hence final immobilisation of injected CO 2 ) caused by general mechanisms like: Structural entrapping Capillary retention Dissolution Mineralisation IPCC WG3

29 Relative permeability Two or more fluids flowing Have to share permeability Relative permeability: The ratio of effective to absolute permeability Both capacity, injectivity and containment relates to the relative permeability (Relperm) Keep injection pressure below fracturing pressure Areal spread & migration RelPerm functions for CO 2 -brine systems are much debated Relative Permeability 1 Sleipner Ref. Model 2010 SPE k rg k rw Stanford Rel. perm explore is a useful took: 0 Water Saturation 1

30 Uncertainty & Risk 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)

31 Uncertainties & Risks 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!

32 Examples of data related uncertainties (issue of scale) Log Resolution versus Plug Scale Outcrop Example

33 Examples of uncertainties Nightmare cores alsmost visable on logs

34 Sleipner CO 2 injection site - Location CO 2 from the Sleipner field is stored in the Utsira Formation, North Sea Water depth is 80 meter, reservoir unit at m depth One CO 2 injector - 38 meter perforation at ~1012 meter (TVD) CO 2 Plume outline Injected gas is ~98% CO 2 Wellhead pressure stable at ~65 bar Wellhead temperature held at 25 C More than 14 Mt CO 2 have been injected (as of July 2014, ~0,9M per annum)

35 Sleipner Modelling Insights Initial models built from pre-injection seismic: Seismic Coarse response grid simulations map 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 IP simulator 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 FD simulator Results presented in SPE Paper , Singh et al, Injection point N

36 Overall take away - repeated Purpose - clarify/agree prior to modelling what the usage will be (capacity, flow, rock mechanics ) 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

37 Presentation title Presenters name Presenters title Tel:

38 References Bjørlykke, Knut (in print) Petroleum Geology Bond, C. E., Wightman, R., Ringrose, P. S., The influence of fracture anisotropy on CO 2 flow. Geophysical Research Letters, 40, DOI: /grl 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, (GHGT- 10). Cavanagh, A., Calibration and prediction of the Sleipner CO2 plume from 2006 to th Int. Conference on Greenhouse Gas Technology (GHGT11), 18th-22nd November 2012, Kyoto, Japan. Chadwick, A., Clochard, V., Delepine, N., and others, Quantitative analysis of time-lapse seismic monitoring at the Sleipner CO2 storage operation. The Leading Edge, 29 (2) Gemmer, L., Hansen, O., Iding, M. Leary, S. and Ringrose, P., Geomechanical Response to CO2 injection at Krechba, In Salah, Algeria. First Break, 30, Hansen, H., Eiken, O. and Aasum, T.O 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 presented at Offshore Europe, 6-9 September 2005, Aberdeen, United Kingdom. Iding, M. & Ringrose, P., Evaluating the impact of fractures on the performance of the In Salah CO2 storage site. International Journal of Greenhouse Gas Control, 4, Lopez, O., Idowa, N., Störer, S., Rueslatten, H., Boassen, T., Leary, S. & Ringrose, P., Pore-scale modelling of CO2- brine Flow Properties at In Salah, Algeria. Energy Procedia, Volume 4, Mathieson, A., Midgley, J., Dodds, K., Wright, I., Ringrose, P. and Saoula, N., CO2 sequestration monitoring and verification technologies applied at Krechba, Algeria. The Leading Edge (February 2010),

39 References Pau, G.S.H., Bell, J. B., Pruess, K., Almgren, A. S. Lijewski, M. J. and Zhang, K., High-resolution simulation and characterization of density-driven flow in CO2 storage in saline aquifers. Advances in Water Resources, 33 (4), Riaz, A., Hesse, M. Tchelepi, H. A. & Orr, F. M., Onset of convection in a gravitationally unstable diffusive boundary layer in porous media. Journal of Fluid Mechanics, 548, Ringrose, P., Atbi, M., Mason, D., Espinassous, M., Myhrer, Ø., Iding, M., Mathieson, A. & Wright, I., Plume development around well KB-502 at the In Salah CO2 Storage Site. First Break, 27, Ringrose, P. S., Mathieson, A. S., Wright, I. W., Selama, F., Hansen, O., Bissell, R., Saoula, N. & Midgley, J The In Salah CO2 storage project: lessons learned and knowledge transfer. 11 th Int. Conference on Greenhouse Gas Technology (GHGT11), 18th-22nd November 2012, Kyoto, Japan. 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., Reservoir modeling of CO2 plume behavior calibrated against monitoring data from Sleipner, Norway. SPE paper presented at the SPE Annual Technical Conference and Exhibition held in Florence, Italy, September Vasco D. W., Ferretti A., Novali F 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., Satellite-based measurements of surface deformation reveal fluid flow associated with the geological storage of carbon dioxide. Geophysical Research Letters, Vol. 37, L03303.