Storage 6 - Modeling for CO 2 Storage. Professor John Kaldi Chief Scientist, CO2CRC Australian School of Petroleum, University of Adelaide, Australia

Transcription

1 Storage 6 - Modeling for CO 2 Storage Professor John Kaldi Chief Scientist, CO2CRC Australian School of Petroleum, University of Adelaide, Australia Regina, Sask., Canada, July, 2016

2 Modeling 2

3 What & Why: Modeling in Context of CCS Business Decision Making Conceptual Geological Model Surface Facility Model Data Analysis Static Reservoir Model Dynamic Reservoir Model Economics Model Capacity, Injectivity, Containment Monitoring Planning Data QC Injection Forecasting Well/facilities Planning Reservoir Management Stored CO2 Volumes Value Decisions 3 3

4 On Models. All models are wrong. some are useful George Box There is no substitute for: Critical independent evaluation on the part of the geoscientists and engineers to assure the success of a modeling project. Most failures occur because a basic assumption was found to be wrong. 4

5 Multiple Interpretations Probabilistic Modeling: (Dealing with Uncertainty) Reservoir Heterogeneity + Data Scarcity Data Fuzziness Uncertainty Need Probabilistic Approach Stochastic Model 5

6 Uncertainty, Accuracy & Precision At what cost? Is it feasible? Is it worth it? Reality Reality Accurate & Precise Accurate & Imprecise Reality Which is better? Reality Inaccurate & Precise Inaccurate & Imprecise 6 6

7 Modeling for CO 2 Storage Modeling is used to: Design injection (location and number of wells, Forecast the migration of injected carbon dioxide Simulate fluid flow Estimate storage capacity Predict reservoir response Know when and where to monitor 7

8 Modeling for CO 2 Storage Modeling can include: coupled geochemistry; coupled geomechanics; tracer migration. Other benefits of modeling Ascertain uncertainty Impress stakeholders: communication tool Most modelling uses computer models Although analytical models are being developed 8

9 Computer models for CO2 Storage Computer models usually: solve the multiphase equations for fluid flow in porous media; use finite-difference techniques for solving flow equations; require the simulated region to be broken up into grid blocks; are based on techniques and code developed in the petroleum industry over the past 4 decades. 9

10 Simulation grid Static & dynamic models comprised of variable numbers & sizes of Grid Blocks 10

11 Discretisation & parameterisation Each grid block only has one value for porosity, permeability, saturation, composition etc. This has two important consequences: We cannot resolve anything below the size of a grid block, i.e. may need to refine grid in areas of interest. Geological data measured on different scales e.g. core data, has to be upscaled or averaged for application to field or basin scale. 11

12 Upscaling 12

13 Static (Geological) Modeling Aim: Capture effects of structure, stratigraphy, sedimentary architecture Reservoirs and seals Lateral and vertical scale & heterogeneity Faults & fractures petrophysical properties (porosity, permeability, seismic) Fluid properties 13

14 Static (Geological) Modeling 3D representation(s) of the subsurface Each cell ( grid block ) contains values for geographic position, depth, volume, rock type, poro/perm, and other static properties. Size and complexity important Grid resolution a key decision: trade-off between detail vs. computational limits. 14

15 Static (Geological) Reservoir Models 3D Model of XYZ Oil Field, NW Shelf, Australia using Petrel 15

16 Input data types Hard Data: Direct measurement from the subsurface: Cores (metres), cuttings (a few mms), Plugs (10s cms) fluid samples 16

17 Integrated data for reservoir characterisation & modeling Core data Seismic -Dep. Env. - Poro/Perm - Stratigraphy Outcrop Analogs - Stratigraphy - Geometry Wireline log data (correlate between wells) Dynamic Static (Res model Sim) model 17

18 Reservoir Simulation: Dynamic Models 18

19 Simulation input and output Input: Static model (permeability, porosity, fault boundaries ) Dynamic properties (relative permeability, capillary pressure) Initial conditions (pressure, temperature, ) Boundary conditions (aquifer drive, ) Flow rates at wells Output: Maps of pressure, fluid saturation,. Tracer concentration (if implemented) Dissolved components (if implemented) Chemical reaction products (if implemented) Stress and strain (if implemented in a geomechanical model) 19

20 Models should be fit for purpose 1. To address scientific questions in a generic context e.g.: the effect of shale barriers on vertical migration of CO 2 the effect of a hydrodynamic gradient on CO 2 migration Dissolution of CO 2 Image Reference: Cardoso& Andres, 2014, Nature Communications Volume:

21 Modeling the dissolution of injected CO yr 1300 yr 2400yr From: J. Ennis-King 21

22 Models should be fit for purpose 2. To make technical predictions in a site-specific context to support decisions e.g.: What is the breakthrough (arrival) time of CO 2 in an EOR project? What is the effect of wellspacing on the maximum injectivity? What is the predicted seismic response? Image source:

23 e.g. Analytical models q Models can be simple... r c,max h(r,t) h r ( c,max t) k rc qt k c w rw h Nordbotten et al. (2005) 23

24 or more complex Example from the Gorgon Project: Plan to inject and store 3-4 MT PA Long-term modelling and monitoring required Reference: Flett, M. A., et al. (2008) SPEdoi: / MS 24

25 Basin-scale modeling Solve a large set of linear equations at multiple timesteps for a large number of cells in a broad geographic area Can be coupled (eg geochemical / geomechanical Computationally demanding Small-scale (pilot projects) modeling Make technical predictions in a sitespecific context to aid decision-making Computationally manageable 25

26 Basin-scale modeling: injection and migration of CO2 Example Central Gippsland Basin, Australia CO2 injection well Lakes Entrance Formation Image from: C. Gibson-Poole 26

27 Small-scale (Pilot project) modeling: Example CO2CRC Otway Project 500m Geological model: incorporates structure (faults) & fluid contacts Static model - based on: facies (rocktype) grid parameterization - Stochastic: multiple realisations of properties (eg porosity, permeability) Dynamic model: upscaled; simulates various injection / migration scenarios 27

28 Depth mss Naylor-1 Monitor well CRC-1 Injector Pre-production gas spill point 0 300m T.Dance Naylor South-1 28

29 T.Dance 29

30 Geo-cellular model Details: 10x10m lateral cell size Layers ~1m Total cells: 132,396 Layers follow top 5 Realisations of sands and shale Poro/perm conditioned to facies CRC-1 Naylor-1 CRC-2 Porosity Naylor South Fault Injection zone in contact with Timboon Aquifer T.Dance Screen grab of 3D model looking West. 30

31 Geo-cellular model Details: 10x10m lateral cell size Layers ~1m CRC-1 Naylor-1 CRC-2 Total cells: 132,396 Layers follow top 5 Realisations of sands and shale Poro/perm conditioned to facies Permeability Naylor South Fault Injection zone in contact with Timboon Aquifer T.Dance Screen grab of 3D model looking West. 31

32 Upscaled Reservoir Model Monitoring well CO 2 accumulation Y.Cinar CO 2 injection well 32

33 CO2CRC Otway project: CO 2 mass fraction Carbon dioxide mass fraction: 18 Sept Carbon dioxide mass fraction: 31 Dec 33

34 CO2CRC Otway project: pressure difference Pressure difference: 18 Sept 31 Dec Pressure difference: 18 Sept 31 Dec (NW SE slice) 34

35 Modeling Time and effort (from Tyson, 2008) 35

36 Modeling Conclusions Modeling is a useful tool in the design of carbon dioxide storage projects. Modeling depends on the quality of the data and the skill of the user (old saying: garbage in, garbage out). Most effort and time in modeling is in data gathering and grid parameterization Models will always be just models uncertainty is inherent! But, in the right hands with the right questions models can provide powerful answers. 36

37 Questions? Regina, Sask., Canada, July, 2017 CO2CRC