The construction and use of digital rock cores in accurate flow modelling for CO 2 storage

Transcription

1 The construction and use of digital rock cores in accurate flow modelling for CO 2 storage Catriona Reynolds, Sam Krevor Department of Earth Science & Engineering Imperial College London

2 Laboratory reservoir characterization is focused on avoiding heterogeneity For accurate modeling of CO 2 migration heterogeneity needs to be embraced Image from M. Bickle

3 Persistent doubts about CO 2 -brine relative permeability Wetting state Relative permeability Benson et al. (2013) GCCSI

4 CO 2 is nonwetting at reservoir conditions Al-Menhali et al. (2015) Wat. Res. Res., doi: /2015wr Ali-Menhali, Krevor (2016) ES&T, doi: /acs.est.5b05925 Niu et al. (2015) Wat. Res. Res., doi: /2014WR Reynolds, Krevor (2015) Wat. Res. Res., doi: /2015WR018046

5 So how do reservoir conditions impact relative permeability to CO 2? Increase IFT by increasing P μ CO2 = μpa s ρ CO2 = kg/m mn/m 19.6 mn/m Amaefule & Handy (1982). The effect of interfacial tensions on relative oil/water permeabilities of consolidated porous mediaspe Journal 22(3): Replotted from Bachu & Bennion (2008). Effects of in-situ conditions on relative permeability characteristics of CO2- brine systems. Environ Geol 54:

6 Experimental Conditions Bentheimer sandstone with a simple heterogeneity Steady state relative permeability Temp: C Pressure : MPa Salinity (NaCl): 0 5 M Immiscible fluids IFT correlations from Li et al. (2012). Interfacial Tension of (Brines+ CO2):(0.864 NaCl KCl) at Temperatures between (298 and 448) K, Pressures between (2 and 50) MPa, and Total Molalities of (1 to 5) mol kg 1 J. Chem. Eng. Data, 57,

7 Rock heterogeneity not impacting fluid distribution Rock heterogeneity controlling fluid distribution

8 High P Low P Rock heterogeneity significant with low CO 2 viscosity 34 mn/m 53 μpa s 27 μpa s Grey symbols - heterogeneity not impacting fluid distribution White symbols - heterogeneity controlling fluid distribution 37 mn/m 58 μpa s 24 μpa s 41 mn/m 58 μpa s 24 μpa s

9 Scaling A continuum scale capillary number characterises the importance of capillary heterogeneity Virnovsky, Friis, Lohne (2004) A Steady-State Upscaling Approach for Two-Phase Flow. Transport in Porous Media, 54, Zhou, Fayers, and Orr (1997) Scaling of Multiphase Flow in Simple Heterogeneous Porous Media. SPE Reservoir Engineering, 12, 03

10 Small heterogeneities can lead to large variations in saturation And thus control relative permeability [kpa] Larger flow potential can support larger capillary pressure gradients

11 Hypothesis tested with Nitrogen Different fluid Different mechanism: flow velocity rather than viscosity increase

12 Saturation maps are used to estimate capillary heterogeneity Only 500 Pa for Bentheimer!

13 Capillary numbers of this work and literature data for CO 2 -brine systems Impacts of rock heterogeneity are the rule, not the exception Impacted by heterogeneity Viscous limit

14 CO 2 relperm data is condition/flow rate specific But the impacts are also present in the field Need appropriate equivalent properties for accurate flow modelling

15 1. Pressure, temperature, fluids must match reservoir 2. Multiple/low flow velocities 3. Heterogeneity orientation in the core cannot be controlled Many practical limitations to performing core floods on heterogeneous rocks Krause, Krevor, Benson (2013) A Procedure for the Accurate Determination of Sub-Core Scale Permeability Distributions with Error Quantification. Transport in Porous Media, 98, 3, Potential to overcome these limitations by using the observations to construct a digital rock whole core

16 Change in the core flood paradigm Needed for numerical model 1. High flow limit relperm 2. Capillary heterogeneity Goals 1. Parameterise a digital model 2. Perform numerical core floods to obtain appropriate equivalent property

17 Characterising capillary heterogeneity The primary information is saturation heterogeneity at steady state flow conditions Flow 1 Step 1: Coarsen data to improve precision 0.8 N 2 Saturation f N2 = cm cm 0.2 Building on approach of many others: Huang, Ringrose & Sorbie (1995); Egermann & Lenormand (2005); Pini, Krevor, Benson (2012); Krause, Krevor, Benson (2013) 0

18 Step 2: Extract individual voxel P c curves First guess assumes P c in a slice is given by the average slice saturation Flow z- direction P c (x,y) = constant

19 Step 3: Scaling the average measured curve (e.g., MICP) to fit individual voxel data E.g., vertical scaling: P c (S w ) = k P c,ave (S w ) 8 k Flow N 2 Saturation Capillary pressure [kpa] Saturation 0.6 0

20 0.8 Qatar Carbonates and Carbon Experiment Step 4: Build simulation Check match to experimental data Increase heterogeneity until saturation & pressure match observations N 2 Saturation 1 Simulation, best fit P c scaling from data Simulation, Increase variance in P c scaling by two 0.2 0

21 Step 5: Use the numerical model to derive relative permeability at reservoir relevant conditions

22 Opportunities 4 mm Digital rocks: Digitally derived capillary pressure curves, either as the average, or to capture heterogeneity, can be used as the basis for digital models. Previously relative permeability was the focus of pore scale digital rocks. Rock orientation: Rock models core models can be re-oriented with respect to flow to obtain properties relevant to the principal flow direction 10 cm Fluids, reservoir conditions: Observations can be made using fluids and at conditions optimised for experimental accuracy, cost

23 Conclusions Low potential gradient in multiphase fluid flow implies that cm scale heterogeneity will control the flow properties derived in laboratory measurements Accurate equivalent flow properties must be derived as the basis for large scale flow simulation Numerical modeling can be used to overcome limitations in rock orientation, flow rate, reservoir conditions, and fluid properties imposed by the need to obtain equivalent properties representative of the reservoir condition The key to constructing the models is in the parameterisation of capillary heterogeneity from core flood observations There is further opportunity for digital rocks to play a role through the characterisation of capillary heterogeneity, until now have focused on relative permeability

24 Acknowledgements We gratefully acknowledge funding from the Qatar Carbonates and Carbon (QCCSRC), Provided jointly by: Qatar Petroleum Shell Qatar Science & Technology Park