An Integrated Approach for History Matching of Multiscale-Fractured Reservoirs
- Mengbi Yao (Peking University) | Haibin Chang (Peking University) | Xiang Li (Peking University) | Dongxiao Zhang (Peking University)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- August 2019
- Document Type
- Journal Paper
- 1,508 - 1,525
- 2019.Society of Petroleum Engineers
- history matching, dual-porosity/dual-permeability model, embedded discrete fracture model, multiscale-fractured reservoirs, Hough-transform
- 25 in the last 30 days
- 130 since 2007
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Naturally or hydraulically fractured reservoirs usually contain fractures at various scales. Among these fractures, large-scale fractures might strongly affect fluid flow, making them essential for production behavior. Areas with densely populated small-scale fractures might also affect the flow capacity of the region and contribute to production. However, because of limited information, locating each small-scale fracture individually is impossible. The coexistence of different fracture scales also constitutes a great challenge for history matching. In this work, an integrated approach is proposed to inverse model multiscale fractures hierarchically using dynamic production data. in the proposed method, a hybrid of an embedded discrete fracture model (EDFM) and a dual-porosity/dual-permeability (DPDP) model is devised to parameterize multiscale fractures. The large-scale fractures are explicitly modeled by EDFM with Hough-transform-based parameterization to maintain their geometrical details. For the area with densely populated small-scale fractures, a truncated Gaussian field is applied to capture its spatial distribution, and then the DPDP model is used to model this fracture area. After the parameterization, an iterative history-matching method is used to inversely model the flow in a fractured reservoir. Several synthetic cases, including one case with single-scale fractures and three cases with mutliscale fractures, are designed to test the performance of the proposed approach.
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Baca, R. G., Arnett, R. C., and Langford, D. W. 1984. Modelling Fluid Flow in Fractured-Porous Rock Masses by Finite-Element Techniques. Int. J. Numer. Methods Fluids 4 (4): 337–348. https://doi.org/10.1002/fld.1650040404.
Barenblatt, G. I., Zheltov, I. P., and Kochina, I. N. 1960. Basic Concepts in the Theory of Seepage of Homogeneous Liquids in Fissured Rocks. J. Appl. Math. Mech. 24 (5): 1286–1303. https://doi.org/10.1016/0021-8928(60)90107-6.
Chai, Z., Yan, B., Killough, J. E. et al. 2018. An Efficient Method for Fractured Shale Reservoir History Matching: The Embedded Discrete Fracture Multi-Continuum Approach. J. Pet. Sci. Eng. 160: 170–181. https://doi.org/10.1016/j.petrol.2017.10.055.
Chang, H., Zhang, D., and Lu, Z. 2010. History Matching of Facies Distribution With the EnKF and Level Set Parameterization. J. Comput. Phys. 229 (20): 8011–8030. https://doi.org/10.1016/j.jcp.2010.07.005.
Chen, Y. and Oliver, D. S. 2013. Levenberg–Marquardt Forms of the Iterative Ensemble Smoother for Efficient History Matching and Uncertainty Quantification. Comput. Geosci. 17 (4): 689–703. https://doi.org/10.1007/s10596-013-9351-5.
Chen, Y. and Oliver, D. S. 2017. Localization and Regularization for Iterative Ensemble Smoothers. Comput. Geosci. 21 (1): 13–30. https://doi.org/10.1007/s10596-016-9599-7.
Cui, H. and Kelkar, M. G. 2005. Automatic History Matching of Naturally Fractured Reservoirs and a Case Study. Presented at the SPE Western Regional Meeting, Irvine, California, 30 March–1 April. SPE-94037-MS. https://doi.org/10.2118/94037-MS.
Dean, R. H. and Lo, L. L. 1988. Simulations of Naturally Fractured Reservoirs. SPE Res Eval & Eng 3 (2): 638–648. SPE-14110-PA. https://doi.org/10.2118/14110-PA.
Emerick, A. A. and Reynolds, A. C. 2012. History Matching Time-Lapse Seismic Data Using the Ensemble Kalman Filter With Multiple Data Assimilations. Comput. Geosci. 16 (3): 639–659. https://doi.org/10.1007/s10596-012-9275-5.
Emerick, A. A. and Reynolds, A. C. 2013. Ensemble Smoother With Multiple Data Assimilation. Comput. Geosci. 55 (Suppl. C): 3–15. https://doi.org/10.1016/j.cageo.2012.03.011.
Furrer, R. and Bengtsson, T. 2007. Estimation of High-Dimensional Prior and Posterior Covariance Matrices in Kalman Filter Variants. J. Multivar. Anal. 98 (2): 227–255. https://doi.org/10.1016/j.jmva.2006.08.003.
Gang, T. and Kelkar, M. G. 2006. Efficient History Matching in Naturally Fractured Reservoirs. Presented at the SPE/DOE Symposium on Improved Oil Recovery, Tulsa, Oklahoma, 22–26 April. SPE-99578-MS. https://doi.org/10.2118/99578-MS.
Gu, Y. and Oliver, D. S. 2007. An Iterative Ensemble Kalman Filter for Multiphase Fluid Flow Data Assimilation. SPE J. 12 (4): 438–446. SPE-108438-PA. https://doi.org/10.2118/108438-PA.
Hajibeygi, H., Karvounis, D., and Jenny, P. 2011. A Hierarchical Fracture Model for the Iterative Multiscale Finite Volume Method. J. Comput. Phys. 230 (24): 8729–8743. https://doi.org/10.1016/j.jcp.2011.08.021.
Hardebol, N. J., Maier, C., Nick, H. et al. 2015. Multiscale Fracture Network Characterization and Impact on Flow: A Case Study on the Latemar Carbonate Platform. J. Geophys. Res.: Solid Earth. 120 (12): 8197–8222. https://doi.org/10.1002/2015JB011879.
Hu, L. Y. 2000. Gradual Deformation and Iterative Calibration of Gaussian-Related Stochastic Models. Math. Geol. 32 (1): 87–108. https://doi.org/10.1023/a:1007506918588.
Jafarpour, B. and McLaughlin, D. 2007. Efficient Permeability Parameterization With the Discrete Cosine Transform. Presented at the SPE Reservoir Simulation Symposium, Houston, Texas, 26–28 February. SPE-106453-MS. https://doi.org/10.2118/106453-MS.
Jiang, J. and Younis, R. M. 2016. Hybrid Coupled Discrete-Fracture/Matrix and Multicontinuum Models for Unconventional-Reservoir Simulation. SPE J. 21 (3): 1–009. SPE-178430-PA. https://doi.org/10.2118/178430-PA.
Karimi-Fard, M., Durlofsky, L. J., and Aziz, K. 2004. An Efficient Discrete-Fracture Model Applicable for General-Purpose Reservoir Simulators. SPE J. 9 (2): 227–236. SPE-88812-PA. https://doi.org/10.2118/88812-PA.
Kazemi, H., Merrill L. S., Jr., Porterfield K. L. et al. 1976. Numerical Simulation of Water-Oil Flow in Naturally Fractured Reservoirs. SPE J. 16 (6): 317–326. SPE-5719-PA. https://doi.org/10.2118/5719-PA.
Lee, S. H., Lough, M. F., and Jensen, C. L. 2001. Hierarchical Modeling of Flow in Naturally Fractured Formations With Multiple Length Scales. Water Resour. Res. 37 (3): 443–455. https://doi.org/10.1029/2000WR900340.
Li, G. and Reynolds, A. C. 2007. An Iterative Ensemble Kalman Filter for Data Assimilation. Presented at the SPE Annual Technical Conference and Exhibition, Anaheim, California, 11–14 November. SPE-109808-MS. https://doi.org/10.2118/109808-MS.
Li, L. and Lee, S. H. 2006. Efficient Field-Scale Simulation for Black Oil in a Naturally Fractured Reservoir via Discrete Fracture Networks and Homogenized Media. Presented at the International Oil & Gas Conference and Exhibition in China, Beijing, 11–14 November. SPE-103901-MS. https://doi.org/10.2118/103901-MS.
Li, X., Zhang, D., and Li, S. 2015. A Multi-Continuum Multiple Flow Mechanism Simulator for Unconventional Oil and Gas Recovery. J. Nat. Gas Sci. Eng. 26: 652–669. https://doi.org/10.1016/j.jngse.2015.07.005.
Liu, N. and Oliver, D. S. 2005. Critical Evaluation of the Ensemble Kalman Filter on History Matching of Geologic Facies. Presented at the SPE Reservoir Simulation Symposium, The Woodlands, Texas, 31 January–2 February. SPE-92867-MS. https://doi.org/10.2118/92867-MS.
Lorentzen, R. J. and Naevdal, G. 2011. An Iterative Ensemble Kalman Filter. IEEE Trans. Autom. Control 56 (8): 1990–1995. https://doi.org/10.1109/TAC.2011.2154430.
Lu, L. and Zhang, D. 2015. Assisted History Matching for Fractured Reservoirs by Use of Hough-Transform-Based Parameterization. SPE J. 20 (5): 942–961. SPE-176024-PA. https://doi.org/10.2118/176024-PA.
Maschio, C. and Schiozer, D. J. 2018. A New Methodology for History Matching Combining Iterative Discrete Latin Hypercube With Multi-Start Simulated Annealing. J. Pet. Sci. Eng. 169 (October): 560–577. https://doi.org/10.1016/j.petrol.2018.06.004.
Maschio, C., Vidal, A. C., and Schiozer, D. J. 2008. A Framework to Integrate History Matching and Geostatistical Modeling Using Genetic Algorithm and Direct Search Methods. J. Pet. Sci. Eng. 63 (1–4): 34–42. https://doi.org/10.1016/j.petrol.2008.08.001.
Moinfar, A., Narr, W., Hui, M.-H. et al. 2011. Comparison of Discrete-Fracture and Dual-Permeability Models for Multiphase Flow in Naturally Fractured Reservoirs. Presented at the SPE Reservoir Simulation Symposium, The Woodlands, Texas, 21–23 February. SPE-142295-MS. https://doi.org/10.2118/142295-MS.
Moinfar, A., Varavei, A., Sepehrnoori, K. et al. 2013. Development of a Coupled Dual Continuum and Discrete Fracture Model for the Simulation of Unconventional Reservoirs. Presented at the SPE Reservoir Simulation Symposium, The Woodlands, Texas, 18–20 February. SPE-163647-MS. https://doi.org/10.2118/163647-MS.
Moinfar, A., Varavei, A., Sepehrnoori, K. et al. 2014. Development of an Efficient Embedded Discrete Fracture Model for 3D Compositional Reservoir Simulation in Fractured Reservoirs. SPE J. 19 (2): 289–303. SPE-154246-PA. https://doi.org/10.2118/154246-PA.
Nejadi, S., Leung, J. Y., Trivedi, J. J. et al. 2015. Integrated Characterization of Hydraulically Fractured Shale-Gas Reservoirs—Production History Matching. SPE Res Eval & Eng 18 (4): 481–494. SPE-171664-PA. https://doi.org/10.2118/171664-PA.
Nicotra, G., Godi, A., Cominelli, A. et al. 2005. Production Data and Uncertainty Quantification: A Real Case Study. Presented at SPE Reservoir Simulation Symposium, The Woodlands, Texas, 31 January–2 February. SPE-93280-MS. https://doi.org/10.2118/93280-MS.
Noorishad, J. and Mehran, M. 1982. An Upstream Finite Element Method for Solution of Transient Transport Equation in Fractured Porous Media. Water Resour. Res. 18 (3): 588–596. https://doi.org/10.1029/WR018i003p00588.
Ouenes, A. and Saad, N. 1993. A New, Fast Parallel Simulated Annealing Algorithm for Reservoir Characterization. Presented at the SPE Annual Technical Conference and Exhibition, Houston, Texas, 3–6 October. SPE-26419-MS. https://doi.org/10.2118/26419-MS.
Ping, J. and Zhang, D. 2013. History Matching of Fracture Distributions by Ensemble Kalman Filter Combined With Vector Based Level Set Parameterization. J. Pet. Sci. Eng. 108 (Suppl. C): 288–303. https://doi.org/10.1016/j.petrol.2013.04.018.
Pluimers, S. 2015. Hierarchical Fracture Modeling Approach. MSc thesis, Delft University of Technology, The Netherlands.
Pruess, K. 1985. A Practical Method for Modeling Fluid and Heat Flow in Fractured Porous Media. SPE J. 25 (1): 14–26. SPE-10509-PA. https://doi.org/10.2118/10509-PA.
Roggero, F. and Hu, L. 1998. Gradual Deformation of Continuous Geostatistical Models for History Matching. Presented at the SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, 27–30 September. SPE-49004-MS. https://doi.org/10.2118/49004-MS.
Romero, C. and Carter, J. 2001. Using Genetic Algorithms for Reservoir Characterisation. J. Pet. Sci. Eng. 31 (2–4): 113–123. https://doi.org/10.1016/S0920-4105(01)00124-3.
Rotondi, M., Nicotra, G., Godi, A. et al. 2006. Hydrocarbon Production Forecast and Uncertainty Quantification: A Field Application. Presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 24–27 September. SPE-102135-MS. https://doi.org/10.2118/102135-MS.
Saidi, A. M. 1983. Simulation of Naturally Fractured Reservoirs. Presented at the SPE Reservoir Simulation Symposium, San Francisco, California, 15–18 November. SPE-12270-MS. https://doi.org/10.2118/12270-MS.
Schulze-Riegert, R., Axmann, J., Haase, O. et al. 2002. Evolutionary Algorithms Applied to History Matching of Complex Reservoirs. SPE Res Eval & Eng 5 (2): 163–173. SPE-77301-PA. https://doi.org/10.2118/77301-PA.
Suzuki, S., Daly, C., Caers, J. et al. 2007. History Matching of Naturally Fractured Reservoirs Using Elastic Stress Simulation and Probability Perturbation Method. SPE J. 12 (1): 118–129. SPE-95498-PA. https://doi.org/10.2118/95498-PA.
Vasco, D. and Datta-Gupta, A. 1997. Integrating Field Production History in Stochastic Reservoir Characterization. SPE Form Eval 12 (3): 149–156. SPE-36567-PA. https://doi.org/10.2118/36567-PA.
Warren, J. E. and Root, P. J. 1963. The Behavior of Naturally Fractured Reservoirs. SPE J. 3 (3): 245–255. SPE-426-PA. https://doi.org/10.2118/426-PA.
Xie, J., Yang, C., Gupta, N. et al. 2015. Integration of Shale Gas Production Data and Microseismic for Fracture and Reservoir Properties Using Fast Marching Method. SPE J. 20 (2): 1–13. SPE-161357-PA. https://doi.org/10.2118/161357-PA.
Yao, M., Chang, H., Li, X. et al. 2018. Tuning Fractures With Dynamic Data. Water Resour. Res. 54 (2): 680–707. https://doi.org/10.1002/2017WR022019.
Zhang, K., Zhang, X., Zhang, L. et al. 2017. Assisted History Matching for the Inversion of Fractures on the Basis of Discrete Fracture-Matrix Model With Different Combinations of Inversion Parameters. Comput. Geosci. 21 (5): 1365–1383. https://doi.org/10.1007/s10596-017-9690-8.