Uncertainty Quantification of an Explicitly Coupled Multiphysics Simulation of In-Situ Pyrolysis by Radio Frequency Heating in Oil Shale
- Travis Ramsay (Iowa State University)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- June 2020
- Document Type
- Journal Paper
- 1,443 - 1,461
- 2020.Society of Petroleum Engineers
- electromagnetic simulation, radio frequency heating, mechanical, uncertainty quantification, phase field, polynomial chaos expansion, thermal, in-situ pyrolysis
- 13 in the last 30 days
- 49 since 2007
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In-situ pyrolysis provides an enhanced oil recovery (EOR) technique for exploiting oil and gas from oil shale by converting in-place solid kerogen into liquid oil and gas. Radio-frequency (RF) heating of the in-place oil shale has previously been proposed as a method by which the electromagnetic energy gets converted to thermal energy, thereby heating in-situ kerogen so that it converts to oil and gas. In order to numerically model the RF heating of the in-situ oil shale, a novel explicitly coupled thermal, phase field, mechanical, and electromagnetic (TPME) framework is devised using the finite element method in a 2D domain. Contemporaneous efforts in the commercial development of oil shale by in-situ pyrolysis have largely focused on pilot methodologies intended to validate specific corporate or esoteric EOR strategies. This work focuses on addressing efficient epistemic uncertainty quantification (UQ) of select thermal, oil shale distribution, electromagnetic, and mechanical characteristics of oil shale in the RF heating process, comparing a spectral methodology to a Monte Carlo (MC) simulation for validation. Attempts were made to parameterize the stochastic simulation models using the characteristic properties of Green River oil shale. The geologic environment being investigated is devised as a kerogen-poor under- and overburden separated by a layer of heterogeneous yet kerogen-rich oil shale in a target formation. The objective of this work is the quantification of plausible oil shale conversion using TPME simulation under parametric uncertainty; this, while considering a referenced conversion timeline of 1.0×107 seconds. Nonintrusive polynomial chaos (NIPC) and MC simulation were used to evaluate complex stochastically driven TPME simulations of RF heating. The least angle regression (LAR) method was specifically used to determine a sparse set of polynomial chaos coefficients leading to the determination of summary statistics that describe the TPME results. Given the existing broad use of MC simulation methods for UQ in the oil and gas industry, the combined LAR and NIPC is suggested to provide a distinguishable performance improvement to UQ compared to MC methods.
|File Size||7 MB||Number of Pages||19|
Akash, B. A. 2003. Characterization of Shale Oil as Compared to Crude Oil and Some Refined Petroleum Products. Energy Sources 25 (12): 1171–1182. https://doi.org/10.1080/00908310390233612.
Al-Mudhafar, W. 2015. Comparison of Permeability Estimation Models through Bayesian Model Averaging and LASSO Regression. Paper presented at the Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, UAE, 9–12 November. SPE-177556-MS. https://doi.org/10.2118/177556-MS.
Al-Mudhafar, W. 2016. Applied Geostatistical Reservoir Characterization in R: Review and Implementation of Permeability Estimation Modeling and Prediction Algorithms—Part II. Paper presented at the Offshore Technology Conference, Houston, Texas, USA, 2–5 May, OTC-26932-MS. https://doi.org/10.4043/26932-MS.
Allix, P., Burnham, A., Fowler, T. et al. 2011. Coaxing Oil from Shale. Oilfield Rev 22 (4): 4–15.
Bandyopadhyah, K. 2009. Seismic Anisotropy: Geological Causes and Its Implications to Reservoir Geophysics. PhD thesis, Stanford University, Stanford, California, USA (August 2009).
Belhamadia, Y., Kane, A. S., and Fortin, A. 2012. An Enhanced Mathematical Model for Phase Change Problems with Natural Convection. International Journal of Numerical Analysis and Modeling, Series B 3 (2): 192–206.
Bello, O., Yang, D., Lazarus, S. et al. 2017. Next Generation Downhole Big Data Platform for Dynamic Data-Driven Well and Reservoir Management. Paper presented at the SPE Reservoir Characterisation and Simulation Conference and Exhibition, Abu Dhabi, UAE, 8–10 May. SPE-186033-MS. https://doi.org/10.2118/186033-MS.
Blatman, G. and Sudret, B. 2011. Adaptive Sparse Polynomial Chaos Expansion Based on Least Angle Regression. J Comput Phys 230 (6): 2345–2367. https://doi.org/10.1016/j.jcp.2010.12.021.
Boak, J. 2007. CO2 Release from In-Situ Production of Shale Oil from the Green River Formation in the Western United States. Paper presented at the 27th Oil Shale Symposium, Golden, Colorado, USA, 15–17 October.
Borgh, G.-P. and Podenzani, F. 2006. Modeling Oil Production from Oil Shale In Situ Pyrolysis. Paper presented at the Canadian International Petroleum Conference, Calgary, Alberta, Canada, 13–15 June. PETSOC-2006-065-EA. https://doi.org/10.2118/2006-065-EA.
Burnham, A. K. 2003. Slow Radio-Frequency Processing of Large Oil Shale Volumes to Produce Petroleum-Like Shale Oil. Technical Report UCRL-ID-155045, Lawrence Livermore National Laboratory, Livermore, California, USA (20 August 2003). https://doi.org/10.2172/15004663.
Choi, C. and Konrad, A. 1991. Finite Element Modeling of the RF Heating Process. IEEE Trans Magn 27 (5): 4227–4230. https://doi.org/10.1109/20.105034.
Crawford, P., Biglarbigi, K., Dammer, A. et al. 2008. Advances in World Oil Shale Technologies. Paper presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, USA, 21–24 September. SPE-116570-MS. https://doi.org/10.2118/116570-MS.
Dean, R., Gai, X., Stone, C. et al. 2006. A Comparison of Techniques for Coupling Porous Flow and Geomechanics. SPE J. 11 (1): 132–140. SPE-79709-PA. https://doi.org/10.2118/79709-PA.
Dubow, J., Nottenberg, R., and Collins, G. 1976. Thermal and Electrical Conductivities of Green River Oil Shale. Am Chem Soc Div Fuel Chem 21 (6) (Prepr. United States), OSTI ID: 7311664.
Dyja, R., Ganapathysubramanian, B., and van Der Zee, K. G. 2018. Parallel-in-Space-Time, Adaptive Finite Element Framework for Nonlinear Parabolic Equations. SIAM J. Sci. Comput. 40 (3): C283–C304. https://doi.org/10.1137/16M108985X.
Efron, B., Hastie, T., Johnstone, I. et al. 2004. Least Angle Regression. Ann Stat 32 (2): 407–4999.
Elam, S. K., Tokura, I., Saito, K. et al. 1989. Thermal Conductivity of Crude Oils. Exp Thermal Fluid Sci 2 (1): 1–6. https://doi.org/10.1016/0894-1777(89)90043-5.
Engineering ToolBox. 2018. Specific Heat of Liquids and Fluids, http://www.engineeringtoolbox.com/specific-heat-fluids-d_151.html (accessed 27 December 2018).
Fakcharoenphol, P., Xiong, Y., Hu, L. et al. 2013. User’s Guide of TOUGH2-EGS: A Coupled Geomechanical and Reactive Geochemical Simulator for Fluid and Heat Flow in Enhanced Geothermal Systems Version 1.0. Golden, Colorado, USA: Colorado School of Mines.
Fan, Y., Durlofsky, L., and Tchelepi, H. 2010. Numerical Simulation of the In-Situ Upgrading of Oil Shale. SPE J. 15 (2): 368–381. SPE-118958-PA. https://doi.org/10.2118/118958-PA.
Hakala, J. A., Stanchina, W., Soong, Y. et al. 2011. Influence of Frequency, Grade, Moisture and Temperature on Green River Oil Shale Dielectric Properties and Electromagnetic Heating Processes. Fuel Process Technol 92 (1): 1–12. https://doi.org/10.1016/j.fuproc.2010.08.016.
Han, D.-H. and Batzle, M. 2000. Velocity, Density and Modulus of Hydrocarbon Fluids—Data Measurement. Paper presented at the SEG Annual Meeting held in Calgary, Alberta, Canada, 6–11 August. SEG-2000-1862. https://doi.org/10.1190/1.1815792.
Hoda, N., Fang, C., Lin, M. et al. 2010. Numerical Modeling of ExxonMobil’s ElectrofracTM Field Experiment at Colony Mine. Paper presented at the 30th Oil Shale Symposium, Golden, Colorado, USA, 18–20 October.
Hosder, S., Perez, R., and Walters, R. 2006. A Non-Intrusive Polynomial Chaos Method for Uncertainty Propagation in CFD Simulations. Paper presented at the 44th AIAA Aerospace Sciences Meeting and Exhibit, Reno, Nevada, USA, 9–12 January.
Hosder, S. and Walter, R. 2010. Non-Intrusive Polynomial Chaos Methods for Uncertainty Quantification in Fluid Dynamics. Paper presented at the 48th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, Orlando, Florida, USA, 4–7 January.
Huang, Z., Zhu, H., and Wang, S. 2015. Finite Element Modeling and Analysis of Radio Frequency Heating Rate in Mung Beans. ASABE 58: 149–160. https://doi.org/10.13031/trans.58.10660.
Hui, H., Ning-Ning, Z., Cai-Xia, H. et al. 2016. Numerical Simulation of In Situ Conversion of the Continental Oil Shale in Northeast China. Oil Shale 33 (2): 45–57. https://doi.org/10.3176/oil.2016.1.04.
Kelkar, S., Pawar, R., and Hoda, N. 2011. Numerical Simulation of Coupled Thermal-Hydrological-Mechanical-Chemical Processes during In Situ Conversion and Production of Oil Shale. Paper presented at the 31st Oil Shale Symposium, Golden, Colorado, USA, 17–19 October.
Kinzer, D. 2008. Past, Present, and Pending Intellectual Property for Electromagnetic Heating of Oil Shale. Paper presented at the 28th Oil Shale Symposium, Golden, Colorado, USA, 13–15 October.
Kumar, D., Raisee, M., and Lacor, C. 2016. An Efficient Non-Intrusive Reduced Basis Model for High Dimensional Stochastic Problems in CFD. Comput Fluids 138: 67–82. https://doi.org/10.1016/j.compfluid.2016.08.015.
Lee, K., Moridis, G., and Ehlig-Economides, C. 2016. A Comprehensive Simulation Model of Kerogen Pyrolysis for the In-Situ Upgrading of Oil Shales. SPE J. 21 (5): 1612–1630. SPE-173299-PA. https://doi.org/10.2118/173299-PA.
Li, F, Xie, R., Song, W. et al. 2017. Optimal Lq Norm Regularization for Sparse Reflectivity Inversion. Paper presented at the SEG International Exposition and Annual Meeting, Houston, Texas, USA, 24–29 September. SEG-2017-17666814. https://doi.org/10.1190/segam2017-17666814.1.
Maitre, O. and Knio, O. 2010. Spectral Methods for Uncertainty Quantification: With Applications to Computational Fluid Dynamics. New York, New York, USA: Springer.
Marelli, S. and Sudret, B. 2014. UQLab: A Framework for Uncertainty Quantification in MATLAB. Paper presented at the 2nd International Conference on Vulnerability and Risk Analysis and Management (ICVRAM), Liverpool, England, UK, 13–16 July. https://doi.org/10.1061/9780784413609.257.
Martemyanov, S. M., Bukharkin, A. A., Koryashov, I. A. et al. 2016. Analysis of Applicability of Oil Shale for In Situ Conversion. AIP Conf Proc 1772: 020001. https://doi.org/10.1063/1.4964523.
Mukhametshina, A. and Martynova, E. 2013. Electromagnetic Heating of Heavy Oil and Bitumen: A Review of Experimental Studies and Field Applications. J Pet Eng 2013: 7. https://doi.org/10.1155/2013/476519.
Najm, H. N. 2009. Uncertainty Quantification and Polynomial Chaos Techniques in Computational Fluid Dynamics. Annu Rev Fluid Mech 41 (1): 35–52. https://doi.org/10.1146/annurev.fluid.010908.165248.
Pan, Y., Chen, C., Yang, S. et al. 2012a. Development of Radio Frequency Heating Technology for Shale Oil Extraction. OJAppS 2012 (2): 66–69. https://doi.org/10.4236/ojapps.2012.22008.
Pan, Y., Mu, J., Ning, J. et al. 2012b. Research on In-situ Oil Shale Mining Technology. J Pharm Sci Invent 1 (1): 1–7.
Patzer, J. 1983. Distribution of Kerogen in Green River Shale: An Assessment of Oil Shale Beneficiation Potential. Fuel 62 (11): 1289–1295. https://doi.org/10.1016/S0016-2361(83)80011-8.
Pereg, D., Cohen, I., and Vassiliou, A. 2017. Sparse Seismic Time-Variant Deconvolution Using Q Attenuation Model. Paper presented at the SEG International Exposition and Annual Meeting, Houston, Texas, USA, 24–29 September. SEG-2017-17680948.
Peters, K. E., Walters, C. C., and Moldowan, J. M. 2005. The Biomaker Guide, Volume 2: Biomakers and Isotopes in Petroleum Exploration and Earth History, second edition. Cambridge, England, UK: Cambridge University Press.
Potter, J., Craddock, P. R., Kleinburg, R. L. et al. 2017. Downhole Estimate of the Enthalpy Required to Heat Oil Shale and Heavy Oil Formations. Energy Fuels 31: 362–373. https://doi.org/10.1021/acs.energyfuels.6b02495.
Prats, M. and O’Brien, S. M. 1975. The Thermal Conductivity and Diffusivity of Green River Oil Shales. J Pet Technol 27 (1): 97–106. SPE-4884-PA. https://doi.org/10.2118/4884-PA.
Reagan, M., Najm, H., Ghanem, R. et al. 2003. Uncertainty Quantification in Reacting—Flow Simulations through Non-Intrusive Spectral Projection. Combust Flame 132 (3): 545–555. https://doi.org/10.1016/S0010-2180(02)00503-5.
Rocks.comparenature.com. 2018. Properties of Oil Shale, http://rocks.comparenature.com/en/properties-of-oil-shale/model-35-6 (accessed 27 December 2018).
Sahni, A., Kumar, M., and Knapp, R. B. 2000. Electromagnetic Heating Methods for Heavy Oil Reservoirs. Paper presented at the SPE/AAPG Western Regional Meeting, Long Beach, California, USA, 19–23 June. SPE-62550-MS. https://doi.org/10.2118/62550-MS.
Sone, H. and Zoback, M. D. 2013. Mechanical Properties of Shale-Gas Reservoir Rocks—Part 1: Static and Dynamic Elastic Properties and Anisotropy. Geophysics 78 (5): D381–D392. https://doi.org/10.1190/geo2013-0050.1.
Sullivan, T. J. 2015. Introduction to Uncertainty Quantification. In Texts in Applied Mathematics, Vol. 63, Switzerland: Springer International Publishing.
Sweeney, J., Roberts, J., and Harben, P. 2007. A Study of the Dielectric Properties of Dry and Saturated Green River Oil Shale. Energy Fuels 21 (5): 2769–2777. https://doi.org/10.1021/ef070150w.
Symington, W. A., Olgaard, D. L., Otten, G. A. et al. 2008. ExxonMobil’s Electrofrac Process for In Situ Oil Shale Conversion. Oral presentation given at the AAPG Annual Convention, San Antonio, Texas, USA, 20–23 April.
Vincent, P. and Schaaf, T. 2019. Reservoir and Economic-Uncertainties Assessment for Recovery-Strategy Selection Using Stochastic Decision Trees. SPE Res Eval & Eng 22 (4): 1575−1592. SPE-190858-PA. https://doi.org/10.21188/190858-PA.
Zhou, Y., Chen, W., Shi, Z. et al. 2018. Seismic Reconstruction Via Constrained Dictionary Learning. Paper presented at the SEG International Exposition and Annual Meeting, Anaheim, California, USA, 14–19 October, SEG-2018-2996536.