Storage 4 - Modeling for CO 2 Storage. Professor John Kaldi Chief Scientist, CO2CRC Australian School of Petroleum, University of Adelaide, Australia

Transcription

1 Storage 4 - Modeling for CO 2 Storage Professor John Kaldi Chief Scientist, CO2CRC Australian School of Petroleum, University of Adelaide, Australia 1

2 Modelling 2

3 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. 3

4 Modelling 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 4

5 Modelling 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 5

6 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. 6

7 Simulation grid 7

8 Discretisation & parameterisation Each grid block only has one value for porosity, permeability, saturation, composition etc. This has two important consequences: We cannot resolve anything in the results 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 in an intelligent way. 8

9 Upscaling 9

10 The 3D static geological model 3D representation(s) of the subsurface Each cell 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. 10

11 The Basics: Static (Geological) Modelling 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 11

12 Input data types Hard Data: Direct measurement from the subsurface: Cores (metres), cuttings (a few mms), Plugs (10s cms) fluid samples 12

13 Integrated data for reservoir characterisation & modelling Core data Seismic -Dep. Env. - Poro/Perm - Stratigraphy Outcrop Analogs - Stratigraphy - Geometry Wireline log data (correlate between wells) Dynamic Static (Res model Sim) model 13

14 Reservoir Simulation: Dynamic Models 14

15 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) 15

16 Dealing with uncertainty: requirement for Probabilistic Modelling Multiple Interpretations Reservoir Heterogeneity + Data Scarcity Data Fuzziness Uncertainty Need Probabilistic Approach Stochastic Model 16

17 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

18 Modelling the dissolution of injected CO 2 40 yr 130 yr 330 yr From: J. Ennis-King 18

19 Modelling the dissolution of injected CO yr 1300 yr 2400yr From: J. Ennis-King 19

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

21 Models can be simple... e.g. Analytical models q r c,max h(r,t) h Nordbotten et al. (2005) 21

22 or more complex Solves a large set of linear equations at a number of given time-steps for a large number of cells Computationally demanding Coupled geochem / geomech e.g. 3D Numerical Models Example from Gorgon Project: Inject and store 3-4 MT PA Long-term modelling and monitoring Reference: Flett, M. A., et al. (2008) SPEdoi: / MS 22

23 Basin-scale vs Small-scale (pilot) projects Solve a large set of linear equations at multiple time-steps for a large number of cells in a broad geographic area -Computationally demanding 500m Make technical predictions in a site-specific context to aid decision-making -Computationally manageable Anderson & Woessner (1992) 23

24 Modelling injection and migration of CO 2 Kingfish Field, Gippsland Basin, Austtralia CO 2 injection well Lakes Entrance Formation Image from: C. Gibson-Poole 24

25 CO2CRC Otway Project 25

26 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 The CO2CRC Otway Project 26

27 Depth mss Naylor-1 Monitor well CRC-1 Injector Pre-production gas spill point 0 300m T.Dance Naylor South-1 27

28 T.Dance 28

29 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. 29

30 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. 30

31 Upscaled Reservoir Model Monitoring well CO 2 accumulation Y.Cinar CO 2 injection well 31

32 CO2CRC Otway project: CO 2 mass fraction Carbon dioxide mass fraction: 18 Sept Carbon dioxide mass fraction: 31 Dec 32

33 CO2CRC Otway project: pressure difference Pressure difference: 18 Sept 31 Dec Pressure difference: 18 Sept 31 Dec (NW SE slice) 33

34 Modelling Time and effort (from Tyson, 2008) 35

35 Modelling Conclusions Modelling is a useful tool in the design of carbon dioxide storage projects. Modelling depends on the quality of the data and the skill of the user (old saying: garbage in, garbage out). Most effort and time in modelling is in data gathering and grid parameterization In the right hands with correct questions it can provide powerful answers. 36

36 Questions? 37 CO2CRC 2015