An Inversion Method of Relative Permeability Curves in Polymer Flooding Considering Physical Properties of Polymer
- Yongge Liu (China University of Petroleum (East China)) | Jian Hou (China University of Petroleum (East China)) | Lingling Liu (China University of Petroleum (East China)) | Kang Zhou (China University of Petroleum (East China)) | Yanhui Zhang (Tianjin Bohai Oilfield Institute) | Tao Dai (Sinopec Shengli Oilfield Company) | Lanlei Guo (Sinopec Shengli Oilfield Company) | Weidong Cao (Sinopec Shengli Oilfield Company)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- October 2018
- Document Type
- Journal Paper
- 1,929 - 1,943
- 2018.Society of Petroleum Engineers
- numerical inversion, cubic-spline, relative permeability curve, polymer flooding
- 7 in the last 30 days
- 266 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 10.00|
|SPE Non-Member Price:||USD 30.00|
Reliable relative permeability curves of polymer flooding are of great importance to the history matching, production prediction, and design of the injection and production plan. Currently, the relative permeability curves of polymer flooding are obtained mainly by the steady-state, nonsteady-state, and pore-network methods. However, the steady-state method is extremely time-consuming and sometimes produces huge errors, while the nonsteady-state method suffers from its excessive assumptions and is incapable of capturing the effects of diffusion and adsorption. As for the pore-network method, its scale is very small, which leads to great size differences with the real core sample or the field. In this paper, an inversion method of relative permeability curves in polymer flooding is proposed by combining the polymer-flooding numerical-simulation model and the Levenberg-Marquardt (LM) algorithm. Because the polymer-flooding numerical-simulation model by far offers the most-complete characterization of the flowing mechanisms of polymer, the proposed method is able to capture the effects of polymer viscosity, residual resistance, diffusion, and adsorption on the relative permeability. The inversion method was then validated and applied to calculate the relative permeability curve from the experimental data of polymer flooding. Finally, the effects of the influencing factors on the inversion error were analyzed, through which the inversion-error-prediction model of the relative permeability curve was built by means of multivariable nonlinear regression. The results show that the water relative permeability in polymer flooding is still far less than that in waterflooding, although the residual resistance of the polymer has been considered in the numerical-simulation model. Moreover, the accuracy of the polymer parameters has great effect on that of the inversed relative permeability curve, and errors do occur in the inversed water relative permeability curve—the measurements of the polymer solution viscosity, residual resistance factor, inaccessible pore-volume (PV) fraction, or maximum adsorption concentration have errors.
|File Size||994 KB||Number of Pages||15|
Alvarado, V. and Manrique, E. 2010. Enhanced Oil Recovery: An Update Review. Energies 3 (9): 1529–1575. https://doi.org/10.3390/en3091529.
Barreau, P., Lasseux, D., Bertin, H. et al. 1999. An Experimental and Numerical Study of Polymer Action on Relative Permeability and Capillary Pressure. Pet. Geosci. 5 (2): 201–206. https://doi.org/10.1144/petgeo.5.2.201.
Behruz, S. S. and Arne, S. 2013. Enhanced Oil Recovery (EOR) by Combined Low Salinity Water/Polymer Flooding. Energy Fuels 27 (3): 1223–1235. https://doi.org/10.1021/ef301538e.
Bo, Q., Zhong, T., and Liu, Q. 2003. Pore Scale Network Modeling of Relative Permeability in Chemical Flooding. Presented at the SPE International Improved Oil Recovery Conference in Asia Pacific, Kuala Lumpur, 20–21 October. SPE-84906-MS. https://doi.org/10.2118/84906-MS.
Chang, H. L., Zhang, Z. Q., Wang, Q. M. et al. 2006. Advances in Polymer Flooding and Alkaline/Surfactant/Polymer Processes as Developed and Applied in the People’s Republic of China. J Pet Technol 58 (2): 84–89. SPE-89175-JPT. https://doi.org/10.2118/89175-JPT.
Chavent, G., Cohen, G., and Espy, M. 1980. Determination of Relative Permeabilities and Capillary Pressures by an Automatic Adjustment Method. Presented at the SPE Annual Technical Conference and Exhibition, Dallas, 21–24 September. SPE-9237-MS. https://doi.org/10.2118/9237-MS.
Chen, G., Zhao, G., and Ma, Y. L. 2006. Mathematical Model of Enhanced Oil Recovery for Viscous-Elastic Polymer Flooding. J. Tsinghua Univ. 46 (6): 882–885.
Chen, Z. and Zhao, X. 2015. Enhancing Heavy-Oil Recovery by Using Middle Carbon Alcohol-Enhanced Waterflooding, Surfactant Flooding and Foam Flooding. Energy Fuels 29 (4): 2153–2161. https://doi.org/10.1021/ef502652a.
Delshad, M. 2000. Volume II: Technical Documentation for UTCHEM-9.0, A Three-Dimensional Chemical Flood Simulator. Reservoir Engineering Research Program, Center for Petroleum and Geosystems Engineering, University of Texas at Austin, Austin, Texas, July 2000.
Dong, H., Fang, S., Wang, D. et al. 2008. Review of Practical Experience & Management by Polymer Flooding at Daqing. Presented at the SPE Symposium on Improved Oil Recovery, Tulsa, 20–23 April. SPE-114342-MS. https://doi.org/10.2118/114342-MS.
Ebeltoft, E., Lomeland, F., Brautaset, A. et al. 2014. Parameter Based Scal-Analysing Relative Permeability for Full Field Application. Presented at the International Symposium of the Society of Core Analysts, Avignon, France, 8–11 September. SCA2014-080.
Eydinov, D., Gao, G., Li, G. et al. 2009. Simultaneous Estimation of Relative Permeability and Porosity/Permeability Fields by History Matching Production Data. J Can Pet Technol 48 (12): 13–25. SPE-132159-PA. https://doi.org/10.2118/132159-PA.
Fayazi, A., Bagherzadeh, H., and Shahrabadi, A. 2016. Estimation of Pseudo Relative Permeability Curves for a Heterogeneous Reservoir With a New Automatic History Matching Algorithm. J. Pet. Sci. Eng. 140 (April): 154–163. https://doi.org/10.1016/j.petrol.2016.01.013.
Guler, B., Ertekin, T., and Grader, A. S. 1999. An Artificial Neural Network Based Relative Permeability Predictor. J Can Pet Technol 49 (4): 49–57. PETSOC-03-04-02. https://doi.org/10.2118/03-04-02.
Hou, J., Wang, D., Luo, F. et al. 2012. Estimation of the Water–Oil Relative Permeability Curve from Radial Displacement Experiments. Part 1: Numerical Inversion Method. Energy Fuels 26 (7): 4291–4299. https://doi.org/10.1021/ef300018w.
Kamari, A., Gharagheizi, F., Shokrollahi, A. et al. 2016. Integrating a Robust Model for Predicting Surfactant–Polymer Flooding Performance. J. Pet. Sci. Eng. 137 (January): 87–96. https://doi.org/10.1016/j.petrol.2015.10.034.
Liang, J. T. and Seright, R. S. 1997. Further Investigations of Why Gels Reduce Water Permeability More Than Oil Permeability. SPE Prod & Fac 12 (4): 225–230. SPE-37249-PA. https://doi.org/10.2118/37249-PA.
Li, H., Chen, S., Yang, D. et al. 2010. Ensemble-Based Relative Permeability Estimation Using B-Spline Model. Transport Porous Med. 85 (3): 703–721. https://doi.org/10.1007/s11242-010-9587-7.
Li, H., Chen, S., Yang, D. et al. 2012. Estimation of Relative Permeability by Assisted History Matching Using the Ensemble Kalman Filter Method. J Can Pet Technol 51 (3): 205–213. SPE-156027-PA. https://doi.org/10.2118/156027-PA.
Liu, Y., Hou, J., Wang, Q. et al. 2017. Flow of Preformed Particle Gel Through Porous Media: A Numerical Simulation Study Based on the Size Exclusion Theory. Ind. Eng. Chem. Res. 55 (10): 2840–2850. https://doi.org/10.1021/acs.iecr.6b03656.
Lopez, X. and Blunt, M. J. 2004. Predicting the Impact of Non-Newtonian Rheology on Relative Permeability Using Pore-Scale Modeling. Presented at the SPE Annual Technical Conference and Exhibition, Houston, 26–29 September. SPE-89981-MS. https://doi.org/10.2118/89981-MS.
Manichand, R. N. and Seright, R. S. 2014. Field vs. Laboratory Polymer-Retention Values for a Polymer Flood in the Tambaredjo Field. SPE Res Eval & Eng 17 (3): 314–325. SPE-169027-PA. https://doi.org/10.2118/169027-PA.
Masuda, Y., Tang, K.-C., Miyazawa, M. et al. 1992. 1D Simulation of Polymer Flooding Including the Viscoelastic Effect of Polymer Solution. SPE Res Eng 7 (2): 247–252. SPE-19499-PA. https://doi.org/10.2118/19499-PA.
Schembre, J. M. and Kovscek, A. R. 2006. Estimation of Dynamic Relative Permeability and Capillary Pressure from Countercurrent Imbibition Experiments. Transp. Porous Med. 65 (1): 31–51. https://doi.org/10.1007/s11242-005-6092-5.
Schneider, F. N. and Owens, W. W. 1982. Steady-State Measurements of Relative Permeability for Polymer/Oil Systems. SPE J. 22 (1): 79–86. SPE-9408-PA. https://doi.org/10.2118/9408-PA.
Seright, R. S. 1991. Effect of Rheology on Gel Placement. SPE Res Eng 6 (2): 212–218. SPE-18502-PA. https://doi.org/10.2118/18502-PA.
Seright, R. S. 2017. How Much Polymer Should Be Injected During a Polymer Flood? Review of Previous and Current Practices. SPE J. 22 (1): 1–18. SPE-179543-PA. https://doi.org/10.2118/179543-PA.
Seright, R. S., Fan, T., Wavrik, K. et al. 2010. New Insights into Polymer Rheology in Porous Media. Presented at the SPE Improved Oil Recovery Symposium, Tulsa, 24–28 April. SPE-129200-MS. https://doi.org/10.2118/129200-MS.
Sharafi, M. S. and Jamialahmadi, M. 2016. Mathematical Model for Prediction of Oil Recovery of Core-Flood Tests in Process of Viscoelastic Polymer Flooding. Pet. Sci. Technol. 34 (11–12): 1098–1105. https://doi.org/10.1080/10916466.2016.1172090.
Song, Z., Liu, L., Wei, M. et al. 2015. Effect of Polymer on Disproportionate Permeability Reduction to Gas and Water for Fractured Shales. Fuel 143 (1 March): 28–37. https://doi.org/10.1016/j.fuel.2014.11.037.
Sun, X. and Mohanty, K. K. 2005. Estimation of Flow Functions During Drainage Using Genetic Algorithm. SPE J. 10 (4): 449–457. SPE-84548-PA. https://doi.org/10.2118/84548-PA.
Wang, J. and Liu, H. 2014. A Novel Model and Sensitivity Analysis for Viscoelastic Polymer Flooding in Offshore Oilfield. J. Ind. Eng. Chem. 20 (2): 656–667. https://doi.org/10.1016/j.jiec.2013.05.030.
Wang, J., Liu, H.-Q., and Xu, J. 2013. Mechanistic Simulation Studies on Viscous-Elastic Polymer Flooding in Petroleum Reservoirs. J. Disper. Sci. Technol. 34 (3): 417–426. https://doi.org/10.1080/01932691.2012.660780.
Watson, A. T., Richmond, P. C., Kerig, P. D. et al. 1988. A Regression-Based Method for Estimating Relative Permeabilities from Displacement Experiments. SPE Res Eng 3 (3): 953–958. SPE-15064-PA. https://doi.org/10.2118/15064-PA.
Zhang, Y., and Yang, D. 2013. Simultaneous Estimation of Relative Permeability and Capillary Pressure for Tight Formations Using Ensemble-Based History Matching Method. Comput. Fluids 71 (30 January): 446–460. https://doi.org/10.1016/j.compfluid.2012.11.013.
Zhang, Y., Song, C., and Yang, D. 2016. A Damped Iterative EnKF Method to Estimate Relative Permeability and Capillary Pressure for Tight Formations from Displacement Experiments. Fuel 167 (1 March): 306–315. https://doi.org/10.1016/j.fuel.2015.11.040.
Zhou, K., Hou, J., Fu, H. et al. 2016. Estimation of Relative Permeability Curves Using an Improved Levenberg-Marquardt Method With Simultaneous Perturbation Jacobian Approximation. J. Hydrol. 544 (January): 604–612. https://doi.org/10.1016/j.jhydrol.2016.12.006.