TCCS-9, 13-14 June 2017, Trondheim, Norway Parameter uncertainty in geological formations, and its impact on CO 2 storage capacity estimation 13 June, 2017 Rebecca Allen*, Halvor M. Nilsen, Odd Andersen, Knut-Andreas Lie, Olav Møyner SINTEF Digital, Norway
Introduction “HOW MUCH?” [1] Halland et al. (2014) CO2 Storage Atlas: Norwegian Continental Shelf, Norwegian Petroleum Directorate. 2 / 23
Introduction Static estimates Dynamic estimate ◮ based on pore volume ◮ based on structural traps ◮ simulate injection and migration periods ◮ M = V p ρ co2 S eff ◮ M = � V trap φρ co2 (1 − S rw ) ◮ quantify long-term Ω ◮ S eff based on aquifer leakage boundaries In this work, we consider structural trapping and long-term plume migration. 3 / 23
Introduction “HOW MUCH?” [1] Halland et al. (2014) CO2 Storage Atlas: Norwegian Continental Shelf, Norwegian Petroleum Directorate. 4 / 23
Introduction Many uncertain parameters! e a t n r i o u t s o l d i s p porosity r e s s u max. dissolution r e f a u l t i n g caprock rugosity permeability “HOW MUCH?” y t e i r u l i caprock shape (macro) t b a r e i s p m s e e t y n i t a l i s r p m o y t c i s n e d k anisotropy c o r [1] Halland et al. (2014) CO2 Storage Atlas: Norwegian Continental Shelf, Norwegian Petroleum Directorate. 4 / 23
Introduction ...ones considered in this work e a t n r i o u t s o l d i s p porosity r e s s u max. dissolution r e f a u l t i n g caprock rugosity permeability “HOW MUCH?” y t e i r u l i caprock shape (macro) t b a r e i s p m s e e t y n i t a l i s r p m o y t c i s n e d k anisotropy c o r [1] Halland et al. (2014) CO2 Storage Atlas: Norwegian Continental Shelf, Norwegian Petroleum Directorate. 4 / 23
Introduction For example... Impact of some parameters on plume migration Increased Increased Increased Base case permeability porosity caprock tilt upslope injection point 5 / 23
Methods 5 / 23
Our approach Representing uncertainty: We generate a Gaussian-type field, and apply it to the (spatially correlated) parameters one-at-a-time. For example... Perturbations applied to top surface elevations of Sandnes geomodel Base grid Perturbed grid Gaussian-type perturbation (+/- 15 meters) 6 / 23
Our approach Model response: We then (i) compute the structural trapping capacity (static estimate) or (ii) simulate long-term plume migration and leakage (dynamic estimate). For example... Model response to perturbations applied to top surface elevations of Sandnes geomodel 500 realizations standard deviation = 50 Mt 500 450 400 P90 350 P50 300 Realization i Base Case 250 200 150 P10 100 50 0 7700 7750 7800 7850 7900 7950 Structural Trapping Capacity (Mt) 7 / 23
Our approach Model response to perturbed caprock (Utsira geomodel) Initial plume 1000 5000 15 000 0 years 100 years years years years 8 / 23
Our approach Model response to perturbed caprock (Utsira geomodel) Initial plume 1000 5000 15 000 0 years 100 years years years years Area C Plume mis-match Area A quantified using 2 C S = A + B Sørensen-Dice coefficient Area B 8 / 23
Our approach Model response to perturbed caprock (Utsira geomodel) Initial plume 1000 5000 15 000 0 years 100 years years years years S = 1 µ = 0 . 96 µ = 0 . 93 µ = 0 . 85 µ = 0 . 83 σ = 0 . 007 σ = 0 . 014 σ = 0 . 031 σ = 0 . 036 35 1 20 1 10 1 15 1 30 0.8 0.8 8 0.8 0.8 15 25 10 Frequency Frequency Frequency Frequency Probability Probability Probability Probability 0.6 0.6 6 0.6 0.6 20 10 15 0.4 0.4 4 0.4 0.4 5 10 5 0.2 0.2 2 0.2 0.2 5 0 0 0 0 0 0 0 0 0.7 0.8 0.9 1 0.7 0.8 0.9 1 0.7 0.8 0.9 1 0.7 0.8 0.9 1 S S S S 8 / 23
Our approach Model response to perturbed caprock (Utsira geomodel) Initial plume 1000 5000 15 000 0 years 100 years years years years 1 µ 0.95 Sørensen-Dice coefficient 0.9 0.85 µ ± σ 0.8 0.75 0 5000 10000 15000 Year 8 / 23
Our approach Challenge : When dealing with uncertain parameters, we might have to consider 1000s of geomodel realizations... thus evaluating the model response can be computationally demanding. In our work... ◮ Static capacity estimates are relatively cheap (traps are identified by spill-point analysis , a topography analysis algorithm). ◮ With regards to long-term simulations, Vertical-Equilibrium (VE) modelling and migration/leakage forecasting reduces our CPU time. 9 / 23
Our approach Spill-point analysis : identifies traps, spill points, spill paths, and catchments. Used for capacity estimation and migration forecasting. Out of domain trap 1 catchment 1 spill path 10 / 23
Our approach Spill-point analysis : identifies traps, spill points, spill paths, and catchments. Used for capacity estimation and migration forecasting. Year 10 10 / 23
Our approach Spill-point analysis : identifies traps, spill points, spill paths, and catchments. Used for capacity estimation and migration forecasting. Year 210 10 / 23
Our approach: Vertical equilibrium (VE) modelling Segregated, migrating plume x y CO 2 + residual brine only brine brine + residual CO 2 θ h h max H g z ◮ From top to bottom: free CO 2 , residual CO 2 , brine ◮ Vertical equilibrium: no significant flow in vertical direction ◮ Upscale 3D flow equations onto 2D domain ◮ Reduce CPU time without reducing quality of result Nilsen, H.M., Lie, KA. & Andersen, O. Comput Geosci (2016) 20: 93. doi:10.1007/s10596-015-9549-9 11 / 23
Results: porosity, permeability, and top surface elevations uncertainty 11 / 23
Results Utsira-South: model response (plume migration) to perturbations Perturbed k Perturbed φ Perturbed z t Base grid ( [0 . 15 , 4 . 96] darcy) ( ± 0 . 05 ) ( ± 5 meters) Base grid permeability porosity caprock elevation perturbed plume base outlines plume outline 1 1 1 injection SDC SDC SDC point 0.9 0.9 0.9 0.8 0.8 0.8 30 2800 30 2800 30 2800 Years Years Years 12 / 23
Results Utsira-South: model response (plume migration) to perturbations Perturbed k Perturbed φ Perturbed z t Base grid ( [0 . 15 , 4 . 96] darcy) ( ± 0 . 05 ) ( ± 5 meters) Base grid permeability porosity caprock elevation perturbed plume base outlines plume outline 1 1 1 injection SDC SDC SDC point 0.9 0.9 0.9 0.8 0.8 0.8 30 2800 30 2800 30 2800 Years Years Years Model response is likely sensitive to magnitude of the perturbations... 12 / 23
Results Sensitivity of model response to magnitude of perturbations Perturbation level & response Parameter Low Medium High z t (meters) ± 1 ± 5 ± 15 1 1 1 0.9 0.9 0.9 0.8 0.8 0.8 φ (unitless) ± 0.02 ± 0.05 ± 0.10 1 1 1 0.9 0.9 0.9 0.8 0.8 0.8 k (darcy) [0.32, 2.18] [0.15, 4.96] [0.03, 25.67] 1 1 1 SDC 0.9 0.9 0.9 0.8 0.8 0.8 Years 13 / 23
Results The benefit of forecasting migration is we reduce number of simulation years required to capture long-term leakage. Initial plume 1000 5000 15 000 0 years 100 years years years years Forecast 600 to remain 500 Mass (MT) 400 300 Structural residual Residual 200 Residual in plume Structural plume Free plume 100 Exited Trapping forecast 0 0 5000 10000 15000 Years since simulation start Forecast converged 14 / 23
Results Perturbation levels versus simulated trapping ± 1 meter ± 5 meters ± 15 meters ± 30 meters +/- 1 meter +/- 5 meter +/- 15 meter +/- 30 meter Forecast to remain (Mt) Forecast to remain (Mt) Forecast to remain (Mt) Forecast to remain (Mt) 600 600 600 600 400 400 400 400 200 200 200 200 0 0 0 0 0 1000 2000 3000 0 1000 2000 3000 0 1000 2000 3000 0 1000 2000 3000 Years Years Years Years 15 / 23
Results Perturbation levels versus simulated trapping ± 1 meter ± 5 meters ± 15 meters ± 30 meters +/- 1 meter +/- 5 meter +/- 15 meter +/- 30 meter Forecast to remain (Mt) Forecast to remain (Mt) Forecast to remain (Mt) Forecast to remain (Mt) 600 600 600 600 400 400 400 400 200 200 200 200 0 0 0 0 0 1000 2000 3000 0 1000 2000 3000 0 1000 2000 3000 0 1000 2000 3000 Years Years Years Years More trapping when larger z t perturbations applied... 15 / 23
Results Sandnes and Utsira: two different geomodels...different model response 10000 Base capacity (a) Sandnes 9950 Base CO2 (Mt) (161 traps) 9900 9850 9800 0 5 10 15 20 25 30 Perturbation (+/-, meters) Perturbed 1200 Mean Base capacity (b) Utsira 1150 (165 traps) 1100 1050 CO2 (Mt) 1000 950 900 850 800 0 5 10 15 20 25 30 Perturbation (+/-, meters) 16 / 23
Results Sandnes and Utsira: two different geomodels...different model response 10000 Base capacity (a) Sandnes 9950 CO2 (Mt) 9900 9850 9800 0 5 10 15 20 25 30 Perturbed Perturbation (+/-, meters) Base Mean (51 traps) 1200 (86 traps) Base capacity (b) Utsira 1150 1100 1050 CO2 (Mt) 1000 950 900 850 800 0 5 10 15 20 25 30 Perturbation (+/-, meters) 16 / 23
Results: aquifer conditions uncertainty 16 / 23
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