Characterising multjphase fmow in heterogeneous rocks Samuel Jackson, Simeon Agada, Catriona Reynolds & Samuel Krevor Department of Earth Science & Engineering, Imperial College London, UK SPE London Evening Meetjng, 30th January 2018
Introductjon & Motjvatjons Relatjve permeability controls • plume migratjon at large scale. Capillary pressure heterogeneity • controls relatjve permeability, 100m which with hysteresis govern residual trapping. We must accurately characterise • these multjphase fmow functjons to efectjvely model plume migratjon and storage. 2/34
Capillary dominated fmow 100m h r Injection rate, Q [Mt/yr.] 3/34
How does this impact relatjve permeability? Relatjve permeability at scales of cm-m in • heterogeneous rocks highly dependent on: Capillary number – Capillary pressure heterogeneity – 20cm C. Reynolds (2016) Ph.D thesis Imperial College London 4/34
Low potentjal fmow at large scales Fine scale solution ‘Correct’ upscaled solution ‘Incorrect’ upscaled VL rel perm - mm Equiv rel perm - cm-m solution • • scale. scale. VL rel perm - cm-m scale. • Heterogeneous Pc Single Pc curve - cm-m Single Pc curve - cm-m • • • curves - mm scale. scale. scale. CO2 injectjon Li and Benson (2015) Ad. Wat. Res., doi: 10.1016/j.advwatres.2015.07.010 5/34
Low potentjal fmow at large scales Equivalent relatjve permeability required to accurately model subsurface fow • on cm-m scale grid blocks. Impractjcal to measure for many fmow regimes in the laboratory. Solutjon: 6/34 Use experiments & calibrated numerical models to fjnd equivalent functjons.
UK Carbon Capture & Storage settjng Quartz rich permeable sandstones: Bentheimer ‘Homogenou s’ outcrop Today’s Bunter talk S. North sea Captain N. North sea 7/34
Characterisatjon approach overview Conduct two steady-state relatjve permeability core • fmood experiments with medical X-ray scanning: High fmow rate, viscous limit experiment Porosity • Absolute permeability • Viscous limit relatjve permeability • Low fmow rate, capillary limit experiment Capillary pressure heterogeneity • Calibrate a digital rock core model and use to simulate • core fmoods Derive equivalent relatjve permeabilitjes •. numerically, without experimental constraints 8/34
Characterisatjon approach – 1/7 Conduct a viscous limit & • capillary limit steady-state relatjve permeability core fmood experiment. Bunter sandstone • L = 15.1cm, r = 1.8cm Bentheimer sandstone • L = 19.8cm, r = 1.8cm 9/34
Characterisatjon approach – 2/7 Post-process experimental data. • Medical X-Ray CT data used to create 3D gas/liquid saturatjons. – Coarsen saturatjons maps to improve precision. – Filter pressure transducer data. 3.81 cm – Saturation Saturation Saturation N2 N2 N2 1x 5x 10x 10/34
Characterisatjon approach – 3/7 Find viscous limit propertjes from high fmow rate experiments: • Porosity – Absolute permeability – Relatjve permeability through regression in SENDRA. – 11/34
Characterisatjon approach – 3/7 Find average capillary pressure propertjes. • Capillary pressure - mercury intrusion data conversion. – 12/34
Characterisatjon approach – 4/7 Characterise the capillary heterogeneity using low fmow rate experiment • Assume slice average Pc curves as the fjrst guess. • Pc = Pc = c1 c2 Pini, R. & Benson, S.M. (2017) Adv. Wat. Res . DOI:10.1016/j.advwatres.2017.08.011 Krause, M. & Benson, S.M. (2015) Adv. Wat. Res. DOI: 10.1016/j.advwatres.2015.07.009 13/34
Characterisatjon approach – 5/7 Bunter Bentheime Flow directjon r 20cm 14/34
Characterisatjon approach – 6/7 Build the 3D model in CMG IMEX. • Simulate the low fmow rate core fmood experiments. • Experiment Simulation 15/34
Characterisatjon approach – 7/7 Calibrate the capillary pressure heterogeneity iteratjvely. • 16/34
Iteratjvely calibrated simulatjon results Experiment , f (N2) = Experiment , f (CO2) = 0.9929 0.975 Simulation , f (N2) = Simulation , f (CO2) = 0.9929 0.975 Bunter Bentheimer 17/34
Experimental uncertainty , k rw [-] 10 -1 Simulation Relative Permeability, k r N 2 equivalent k r Experiment 10 -2 equivalent k r Viscous limit k r 10 -3 Iteratjvely calibrated 10 -4 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 simulatjon results Water Saturation, S w [-] (a) (b) , k rw [-] 10 -1 Simulation equivalent k r Relative Permeability, k r CO 2 Experiment equivalent k r 10 -2 Viscous limit k r 10 -3 10 -4 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 Water Saturation, S w [-] (c) (d) 18/34
Characterising using a single dataset Can we calibrate using less data? • What happens when we calibrate Pc(Sw) with other experimental data? • Calibrate using: High fmow rate (viscous limit) exp data • vs Low fmow rate (capillary limit) exp data 19/34
Characterising using a single dataset Low fmow rate (capillary limit) scaling vs high fmow rate (viscous limit) scaling • 20/34
Experimental uncertainty Iteratjvely calibrated results using high fmow rate data (a) (b) (c) (d) 21/34
Using the calibrated model Simulatjng experiments outside laboratory conditjons. 1) With end efects 22/34
Using the calibrated model Simulatjng experiments outside laboratory conditjons. 2) Without end efects 23/34
Using the calibrated model Simulatjng experiments outside laboratory conditjons. 3) Rotated capillary pressure heterogeneity. 24/34
Using the calibrated model Simulatjng experiments outside laboratory conditjons. 3) Rotated capillary pressure heterogeneity. 25/34
What does this mean for plume migratjon? ? 26/34
Equivalent relatjve permeability impacts: 2D sharp interface model 1Mt/yr. t = 50 days Viscous Limit Capillary Limit 27/34
Equivalent relatjve permeability impacts: 2D sharp interface model 1Mt/yr. Δr = 35m t = 150 days Viscous Limit Capillary Limit 28/34
Equivalent relatjve permeability impacts: 2D sharp interface model 1Mt/yr. Δr = 32m t = 250 days Viscous Limit Capillary Limit 29/34
Equivalent relatjve permeability impacts: 2D sharp interface model 1Mt/yr. Δr = 31m t = 350 days Viscous Limit Capillary Limit 8% decrease in r 5% increase Avg. ΔP 30/34
What does this mean for residual trapping? 31/34
Residual trapping – Capillary heterogeneity efects 1.0 Exp 37 V oxel average 0.9 Exp 37 Slice average Exp 37 Core average Exp 38 V oxel average 0.8 [-] Exp 38 Slice average Exp 38 Core average Residual CO 2 saturation, S CO 2 Land model, C = 1.3 0.7 Land model, C = 0.0 0.6 0.5 Bunter Residual CO2 saturation Residual CO2 saturation 0.4 0.3 0.2 S CO 2 [-] S CO 2 [-] 0.1 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Initial CO 2 saturation, S CO 2 [-] 1.0 Exp 41 Voxel average 0.9 Exp 41 Slice average Exp 41 Core average Initial CO2 saturation Initial CO2 saturation Land model, C = 2.0 0.8 S CO 2 [-] S CO 2 [-] [-] Land model, C = 0.0 Residual CO 2 saturation, S CO 2 Captain Bunter simulatjon Bunter simulatjon 0.7 0.6 With Pc heterogeneity Without Pc heterogeneity 0.5 0.4 Olugbade (2017) Digital Rock Core Simulaton of CO2 0.3 Storage , MSc Thesis, Imperial College London 0.2 0.1 0.0 32/34 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Initial CO 2 saturation, S CO 2 [-]
Conclusions 33/34
Thank you Pre-print paper available now: Characterising multjphase fow in heterogeneous sandstones htups://eartharxiv.org/wcxny DOI: 10.17605/OSF.IO/WCXNY NERC highlights www.krevorlab.co.uk grant NE/N016173/1 34/34
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