A Robust Embedded Discrete Fracture Modeling Workflow for Simulating Complex Processes in Field-Scale Fractured Reservoirs
- Mun-Hong Hui (Chevron Energy Technology Company) | Gaelle Dufour (Chevron Energy Technology Company) | Sarah Vitel (Chevron Energy Technology Company) | Pierre Muron (Chevron Energy Technology Company) | Reza Tavakoli (Chevron Energy Technology Company) | Matthieu Rousset (Chevron Energy Technology Company) | Alvaro Rey (Chevron Energy Technology Company) | Bradley Mallison (Chevron Energy Technology Company)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Simulation Conference, 10-11 April, Galveston, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- 5.5 Reservoir Simulation, 5.4.1 Waterflooding, 4.3.4 Scale, 5.4 Improved and Enhanced Recovery, 5.8.7 Carbonate Reservoir, 5 Reservoir Desciption & Dynamics, 5.5.3 Scaling Methods, 5.5.8 History Matching, 5.8 Unconventional and Complex Reservoirs
- fracture network gridding, complex recovery processes, Embedded discrete fracture modeling, naturally fractured reservoirs, aggregation-based upscaling
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- 121 since 2007
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Traditionally, fractured reservoir simulations use Dual-Porosity, Dual-Permeability (DPDK) models that can idealize fractures and misrepresent connectivity. The Embedded Discrete Fracture Modeling (EDFM) approach improves flow predictions by integrating a realistic fracture network grid within a structured matrix grid. However, small fracture cells with high conductivity that pose a challenge for simulators can arise and ad hoc strategies to remove them can alter connectivity or fail for field-scale cases. We present a new gridding algorithm that controls the geometry and topology of the fracture network while enforcing a lower bound on the fracture cell sizes. It honors connectivity and systematically removes cells below a chosen fidelity factor. Furthermore, we implemented a flexible grid coarsening framework based on aggregation and flow-based transmissibility upscaling to convert EDFMs to various coarse representations for simulation speedup. Here, we consider pseudo-DPDK (pDPDK) models to evaluate potential DPDK inaccuracies and the impact of strictly honoring EDFM connectivity via Connected Component within Matrix (CCM) models. We combine these components into a practical workflow that can efficiently generate upscaled EDFMs from stochastic realizations of thousands of geologically realistic natural fractures for ensemble applications.
We first consider a simple waterflood example to illustrate our fracture upscaling to obtain coarse (pDPDK and CCM) models. The coarse simulation results show biases consistent with the underlying assumptions (e.g., pDPDK can over-connect fractures). The preservation of fracture connectivity via the CCM aggregation strategy provides better accuracy relative to the fine EDFM forecast while maintaining computational speedup. We then demonstrate the robustness of the proposed EDFM workflow for practical studies through application to an improved oil recovery (IOR) study for a fractured carbonate reservoir. Our automatable workflow enables quick screening of many possibilities since the generation of full-field grids (comprising almost a million cells) and their preprocessing for simulation completes in a few minutes per model. The EDFM simulations, which account for complicated multiphase physics, can be generally performed within hours while coarse simulations are about a few times faster. The comparison of ensemble fine and coarse simulation results shows that on average, a DPDK representation can lead to high upscaling errors in well oil and water production as well as breakthrough time while the use of a more advanced strategy like CCM provides greater accuracy. Finally, we illustrate the use of the Ensemble Smoother with Multiple Data Assimilation (ESMDA) approach to account for field measured data and provide an ensemble of history-matched models with calibrated properties.
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Abdrakhmanova, A., Iskakov, E., King, G.R., Chaudhri, M.M., Liu, N., and Bateman, P. (2014). Probabilistic history matching of dual-porosity and dual-permeability Korolev model using three discrete fracture models. Paper SPE 170854 presented at the SPE Annual Technical Conference and Exhibition, Amsterdam, Netherlands, January 1. https://doi.org/10.2118/170854-MS.
Allan, J. and Sun, S. (2003). Controls on recovery factor in fractured reservoirs: lessons learned from 100 fractured fields. Paper SPE 84590 presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, USA, October 5-8. https://doi.org/10.2118/84590-MS.
Bertino, L., Evensen, G. and Wackernagel, H. (2003). Sequential data assimilation techniques in oceanography. International Statistical Review, 71(2), 223–241. https://doi.org/10.1111/j.1751-5823.2003.tb00194.x.
Cipolla, C. and Wallace, J. (2014). Stimulated Reservoir Volume: A misapplied concept? Paper SPE 168596 presented at SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, USA, February 4-6. Society of Petroleum Engineers. https://doi.org/10.2118/168596-MS
Crestani, E., Camporese, M., Bau, D., and Salandin, P. (2013). Ensemble Kalman filter versus ensemble smoother for assessing hydraulic conductivity via tracer test data assimilation. Hydrology and Earth System Sciences, 17(4), 1517–1531, https://doi.org/10.5194/hess-17–1517-2013.
Ding, D. Y., Farah, N., Bourbiaux, B., Wu, Y.-S., and Mestiri, I. (2018). Simulation of matrix/fracture interaction in low-permeability fractured unconventional reservoirs. Society of Petroleum Engineers. https://doi.org/10.2118/182608-PA.
Durlofsky, L.J. and Chen, Y. (2012). Uncertainty quantification for subsurface flow problems using coarse-scale models. Numerical Analysis of Multiscale Problems, Lecture Notes in Computational Science and Engineering, 83, pp 163–202, I.G. Graham., eds., Springer. https://doi.org/10.1007/978-3-642-22061-6_6.
Emerick, A., and Reynolds, A. (2012). Ensemble smoother with multiple data assimilation. Computational Geosciences, 55, 3–15, https://doi.org/10.1016/j.cageo.2012.03.011.
Evensen, G. (1994). Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, Journal of Geophysical Research, 99, 10143–10162. https://doi.org/10.1029/94JC00572
Evensen, G., and van Leeuwen, P.J. (2000). An ensemble Kalman smoother for nonlinear dynamics. Monthly Weather Review, 128(6), 1852–1867. https://doi.org/10.1175/1520-0493(2000)128%3C1852:AEKSFN%3E2.0.CO;2.
Evensen, G. (2004). Sampling strategies and square root analysis schemes for the EnKF. Ocean Dynamics, 54, 539–560, https://doi.org/10.1007/s10236-004-0099-2.
Fumagalli, A., Pasquale, L., Zonca, S., and Micheletti, S. (2016). An upscaling procedure for fractured reservoirs with embedded grids. Water Resources Research, 52, 6506-6525. https://doi.org/10.1002/2015WR017729.
Hajibeygi, H., Karvounis, D., and Jenny, P. (2011). A hierarchical fracture model for the iterative multiscale finite volume method. Journal of Computational Physics, 230, 8729-8743. https://doi.org/10.1016/j.jcp.2011.08.021.
Houtekamer, P. L.,H. L. Mitchell,G. Pellerin,M. Buehner,M. Charron,L. Spacek, and B. Hansen (2005). Atmospheric data assimilation with an ensemble Kalman filter: Results with real observations. Monthly Weather Review, 133(3), 604–620. https://doi.org/10.1175/MWR-2864.1.
Hui, M.H., Mallison, B., and Lim, K.T. (2008). An innovative workflow to model fractures in a giant carbonate reservoir. Paper IPTC 12572 presented at the International Petroleum Technology Conference, Kuala Lumpur, Malaysia, December 3–5. https://doi.org/10.2523/IPTC-12572-MS.
Hui, M.H., Mallison, B., Heidary-Fyrozjaee, M., and Narr, W. (2013). The upscaling of discrete fracture models for faster, coarse-scale simulations of IOR and EOR Processes for fractured reservoirs. Paper SPE 166075 presented at the SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, USA, September 30–October 2. https://doi.org/10.2118/166075-MS.
Hui, M.H., Karimi-Fard, M., Mallison, B., and Durlofsky, L. J. (2018). A general modeling framework for simulating complex recovery processes in fractured reservoirs at different resolutions. SPE Journal, 23, 598–613. https://doi.org/10.2118/182621-PA.
Jiang, J. and Younis, R.M. (2017). An improved projection-based embedded discrete fracture model (pEDFM) for multiphase flow in fractured reservoirs. Advances in Water Resources, 109, 267–289. https://doi.org/10.1016/j.advwatres.2017.09.017.
Kalman, R. E. (1960), A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82, 35–45. http://dx.doi.org/10.1115/1.3662552.
Karimi-Fard, M. and Durlofsky, L.J. (2016). A general gridding, discretization, and coarsening methodology for modeling flow in porous formations with discrete geological features. Advances in Water Resources, 96, 354–372. https://doi.org/10.1016/j.advwatres.2016.07.019.
Karimi-Fard, M., Durlofsky, L.J., and Aziz, K. (2004). An efficient discrete-fracture model applicable for general purpose reservoir simulators. SPE Journal, 9, 227–236. https://doi.org/10.2118/88812-PA.
Gerritsen, M.G. and Lambers, J.V. (2008). Integration of local-global upscaling and grid adaptivity for simulation of subsurface flow in heterogeneous formations. Computational Geosciences, 12(2), 193–1208. https://doi.org/10.1007/s10596-007-9078-2.
Lee, S.H., Lough, M.L., and Jensen, C.L. (2001). Hierarchical modeling of flow in naturally fractured formations with multiple length scales. Water Resources Research, 37(3), 443-455. https://doi.org/10.1029/2000WR900340.
Li, L. and Lee, S.H. (2008). Efficient field-scale simulation of black oil in a naturally fractured reservoir through discrete fracture networks and homogenized media. SPE Reservoir Evaluation & Engineering, 11, 750-758. https://doi.org/10.2118/103901-PA.
Lim, K.T., Hui, M.H., and Mallison, B. (2009). A next-generation reservoir simulator as an enabling technology for a complex discrete fracture modeling workflow. Paper SPE 124980 presented at the SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, USA, October 4–7. https://doi.org/10.2118/124980-MS.
Lough, M.F., Lee, S.H., and Kamath, J. (1998). An efficient boundary integral formulation for flow through fractured porous media. Journal of Computational Physics, 143 (2), 462-483. https://doi.org/10.1006/jcph.1998.5858.
Mallison, B., Hui, M.H., and Narr, W. (2010). Practical gridding algorithms for discrete fracture modeling workflows. 12th European Conference on the Mathematics of Oil Recovery, Oxford, United Kingdom, September 6–9. http://dx.doi.org/10.3997/2214-4609.20144950.
Moinfar, A., Narr, W., Hui, M.H., Mallison, B.T., and Lee, S.H. (2011). Comparison of discrete-fracture and dual-permeability models for multiphase flow in naturally fractured reservoirs. Society of Petroleum Engineers. https://doi.org/10.2118/142295-MS.
Moinfar, A., Varavei, A., Sepehrnoori, K., and Johns, R.T. (2014). Development of an efficient embedded discrete fracture model for 3D compositional reservoir simulation in fractured reservoirs. SPE Journal, 19, 289–303. https://doi.org/10.2118/154246-PA.
Oliver, D. S. and Chen, Y. (2010). Recent progress on reservoir history matching: A review. Computational Geosciences, 15(1), 185–221. https://doi.org/10.1007/s10596-010-9194-2.
Panfili, P., Colin, R., Cominelli, A., Giamminonni, D., and Guerra, L. (2015). Efficient and Effective Field Scale Simulation of Hydraulic Fractured Wells: Methodology and Application. Society of Petroleum Engineers. https://doi.org/10.2118/175542-MS.
Reichle, R.H., McLaughlin, D.B., and Entekhabi, D. (2002). Hydrologic data assimilation with the ensemble Kalman filter. Monthly Weather Review, 130(1), 103–114. https://doi.org/10.1175/1520-0493(2002)130%3C0103:HDAWTE%3E2.0.CO;2.
Reynolds, A. C.,Zafari, M. and Li, G. (2006). Iterative forms of the ensemble Kalman filter. Paper presented at 10th European Conference on the Mathematics of Oil Recovery, Amsterdam, Netherlands, September 4-7. http://dx.doi.org/10.3997/2214-4609.201402496.
Sanderson, D.J., and Nixon, C.W. (2015). The use of topology in fracture network characterization. Journal of Structural Geology. 7, 55-66. http://dx.doi.org/10.1016/j.jsg.2015.01.005.
Sun W.,Hui, M.H., and Durlofsky, L.J. (2017). Production forecasting and uncertainty quantification for naturally fractured reservoirs using a new data-space inversion procedure. Computational Geosciences, 21(5-6), 1443–1458. https://doi.org/10.1007/s10596-017-9633-4.
Tavakoli, R., Pencheva, G., Wheeler, M.F., and Ganis, B. (2012). A parallel ensemble-based framework for reservoir history matching and uncertainty characterization. Computational Geosciences, 17(1), 83–97. https://doi.org/10.1007/s10596-012-9315-1.
Tene, M., Bosma, S.B.M.,Al Kobaisi, M.S., and Hajibeygi, H. (2017). Projection-based embedded discrete fracture model (pEDFM). Advances in Water Resources, 105, 205-216. https://doi.org/10.1016/j.advwatres.2017.09.017.
van Leeuwen, P. J. and Evensen, G. (1996). Data assimilation and inverse methods in terms of a probabilistic formulation. Monthly Weather Review, 124, 2898–2913. https://doi.org/10.1175/1520-0493(1996)124%3C2898:DAAIMI %3E2.0.CO;2.
Virues, C., Shimamoto, T., and Kuroda, S. (2015). Understanding fracture geometry of the Canadian Horn River shale gas via an unconventional complex fracture propagation model in multi-staged pad with 9 horizontal wells. Paper SPE 175933 presented at the CSUR Unconventional Resources Conference, Calgary, Alberta, Canada, October 20-22. https://doi.org/10.2118/175933-MS.
Vitel, S. and Souche, L. (2007). Unstructured upgridding and transmissibility upscaling for preferential flow paths in 3D fractured reservoirs. Paper SPE 106483 presented at the SPE Reservoir Simulation Symposium, Houston, Texas, USA, February 26-28. https://doi.org/10.2118/106483-MS.
Warren, J.E. and Root, P.J. (1963). The behavior of naturally fractured reservoirs. SPE Journal, 3, 245-255. https://doi.org/10.2118/426-PA.
Wen, X.-H and Chen, W.C. (2007). Some practical issues on real time reservoir updating using Ensemble Kalman Filter. SPE Journal, 12(2), 156-166. https://doi.org/10.2118/111571-PA.