Estimation of Production Rates by Use of Transient Well-Flow Modeling and the Auxiliary Particle Filter: Full-Scale Applications
- Rolf J. Lorentzen (International Research Institute of Stavanger) | Andreas S. Stordal (International Research Institute of Stavanger) | Xiaodong Luo (International Research Institute of Stavanger) | Geir Naevdal (International Research Institute of Stavanger)
- Document ID
- Society of Petroleum Engineers
- SPE Production & Operations
- Publication Date
- May 2016
- Document Type
- Journal Paper
- 163 - 175
- 2016.Society of Petroleum Engineers
- rate allocation, well flow modeling, estimation
- 6 in the last 30 days
- 257 since 2007
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In the current work, we demonstrate the use of a detailed transient multiphase well-flow model and the auxiliary sequential-importance resampling filter, for better representation of flow rates in petroleum wellbores (influx and/or outflux). The fundamentals of the methodology have been presented by the authors in a previous work, and the purpose of this paper is full-scale case studies. Performance, robustness, and flexibility are demonstrated with data sets from two different wells.
The first case is an application to an inclined gas producer with two inflow reservoir zones. Observations consist of pressure and temperature at two locations in the well. Next, we present an application to an inclined water injector with nine outflow zones. Observations consist of data from a distributed temperature sensor (DTS) and one pressure observation at the end of the DTS rod.
Estimate of zonal flow rates, including error bounds, lead to better well control and reservoir management, which again are important for improved recovery of oil from existing petroleum fields. The results presented in this paper clearly demonstrate the feasibility of automatic identification of reservoir flow-rate distribution from wellbore measurements.
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Al-Safran, E. M. and Kelkar, M. 2009. Predictions of Two-Phase Critical-Flow Boundary and Mass-Flow Rate Across Chokes. SPE Prod & Oper 24 (2): 249–256. SPE-109243-PA. http://dx.doi.org/10.2118/109243-PA.
Azim, R. R. A. 2012. Novel Applications of Distributed Temperature Measurements to Estimate Zonal Flow Rate and Pressure in Offhore Gas Wells. Presented at the SPE International Production and Operations Conference and Exhibition, Doha, Qatar, 14–16 May. SPE-154098-MS. http://dx.doi.org/10.2118/154098-MS.
Chen, N. H. 1979. An Explicit Equation for Friction Factor in Pipe. Ind. Eng. Chem. Fundamen. 18 (3): 296–297. http://dx.doi.org/10.1021/i160071a019.
Evensen, G. 2009. Data Assimilation: The Ensemble Kalman Filter, 2nd edition. Berlin: Springer-Verlag Berlin Heidelberg.
Gomez, L. E., Shoham, O., Schmidt, Z. et al. 2000. Unified Mechanistic Model for Steady-State Two-Phase Flow: Horizontal to Vertical Upward Flow. SPE J. 5 (3): 339–350. SPE-65705-PA. http://dx.doi.org/10.2118/65705-PA.
Gryzlov, A., Schiferli, W., and Mudde, R. F. 2013. Soft-sensors: Modelbased estimation of inflow in horizontal wells using the extended Kalman filter. Flow Measurement and Instrumentation 34: 91–104. http://dx.doi.org/10.1016/j.flowmeasinst.2013.09.002.
Jansen, J. D. 2015. Nodal Analysis of Oil and Gas Wells - Theory and Matlab Implementation. Dept. of Geoscience and Engineering, Delft University of Technology, The Netherlands (under review).
Kawaguchi, K., Takekawa, M., Wada, H. et al. 2013. Optimal Sensor Placement for Multi-Phase Flow Rate Estimation Using Pressure and Temperature Measurements. Presented at the SPE Digital Energy Conference, The Woodlands, Texas, USA, 5–7 March. SPE-163727-MS. http://dx.doi.org/10.2118/163727-MS.
Leemhuis, A. P., Nennie, E. D., Belfroid, S. P. C. et al. 2008. Gas Coning Control for Smart Wells Using a Dynamic Coupled Well-Reservoir Simulator. Presented at the SPE Intelligent Energy Conference and Exhibition, Amsterdam, 25–27 February. SPE-112234-MS. http://dx.doi.org/10.2118/112234-MS.
Leskens, M., de Kruif, B., Belfroid, S. et al. 2008. Downhole Multiphase Metering in Wells by Means of Soft-Sensing. Presented at the SPE Intelligent Energy Conference and Exhibition, Amsterdam, 25–27 February. SPE-112046-MS. http://dx.doi.org/10.2118/112046-MS.
Lorentzen, R. J. and Fjelde, K. K. 2005. Use of slopelimiter techniques in traditional numerical methods for multi-phase flow in pipelines and wells. International Journal for Numerical Methods in Fluids 48 (7): 723–745. http://dx.doi.org/10.1002/fld.952.
Lorentzen, R. J., Sævareid, O., and Nævdal, G. 2010a. Rate Allocation: Combining Transient Well Flow Modeling and Data Assimilation. Presented at the SPE Annual Technical Conference and Exhibition, Florence, Italy, 19–22 September. SPE-135073-MS. http://dx.doi.org/10.2118/135073-MS.
Lorentzen, R. J., Sævareid, O., and Nævdal, G. 2010b. Soft Multiphase Flow Metering for Accurate Production Allocation. Presented at the SPE Russian Oil and Gas Conference and Exhibition, Moscow, 26–28 October. SPE-136026-MS. http://dx.doi.org/10.2118/136026-MS.
Lorentzen, R. J., Stordal, A. S., Nævdal, G. et al. 2014. Estimation of Production Rates With Transient Well-Flow Modeling and the Auxiliary Particle Filter. SPE J. 19 (1): 172–180. SPE-165582-PA. http://dx.doi.org/10.2118/165582-PA.
Luo, X., Lorentzen, R. J., Stordal, A. S. et al. 2014. Toward an enhanced Bayesian estimation framework for multiphase flow soft-sensing. Inverse Problems 30 (11): 114012. http://dx.doi.org/10.1088/0266-5611/30/11/114012.
Petalas, N. and Aziz, K. 2000. A Mechanistic Model for Multiphase Flow in Pipes. J Can Pet Tech 39 (6): 43–55. PETSOC-00-06-04. http://dx.doi.org/10.2118/00-06-04.
Pitt, M. K. and Shephard, N. 1999. Filtering via Simulation: Auxiliary Particle Filters. Journal of American Statistical Association 94 (446): 590–599. http://dx.doi.org/10.1080/01621459.1999.10474153.
Sworder, D. D. and Boyd, J. E. 1999. Estimation Problems in Hybrid Systems. Cambridge University Press. http://dx.doi.org/10.1017/CBO9780511546150.2.