Estimation of Production Rates With Transient Well-Flow Modeling and the Auxiliary Particle Filter
- Rolf J. Lorentzen (International Research Institute of Stavanger (IRIS)) | Andreas Stordal (International Research Institute of Stavanger (IRIS)) | Geir Nævdal (International Research Institute of Stavanger (IRIS)) | Hans A. Karlsen (University of Bergen) | Hans J. Skaug (University of Bergen)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- February 2014
- Document Type
- Journal Paper
- 172 - 180
- 2013. Society of Petroleum Engineers
- 1.7.5 Well Control, 2.3 Completion Monitoring Systems/Intelligent Wells
- 7 in the last 30 days
- 313 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 12.00|
|SPE Non-Member Price:||USD 35.00|
In the current work, we combine a detailed transient multiphase well-flow model and modern estimation techniques into a tool for better representation of flow rates in petroleum wellbores (influx or outflux). Accurate flow rates lead to better well control and reservoir management, which again are important for the improved recovery of oil from existing petroleum fields. To achieve this, it is possible to use the fact that smart wells with multiple zones and laterals are more common, and they may be equipped with permanent instrumentation and control. Many wells have pressure and temperature gauges in each zone, or even distributed temperature sensing with high spatial resolution. Today, accurate flow-rate measurements or accurate estimates for each zone are lacking, and existing tools are often limited to steady-state models with no uncertainty analysis. The estimation technique applied here is the auxiliary-sequential-importance-resampling (ASIR) filter, which has the advantage of being more-robust and -reliable than the traditional particle filter (PF). The ASIR filter is used to tune the output of specific stochastic models of the flow rates. To perform this tuning, we have chosen a jump-type model for the flow rates. These kinds of models are popular within areas such as econometrics and finance. More specifically, the model implies that the flow-rate process changes structure governed by an underlying Markov jump process. Using this type of model makes us capable of capturing not only smooth transitions but also more-abrupt changes of the flow rates. We have applied the methodology on two synthetic studies (involving multiple zones and fluids), and our case studies clearly demonstrate the feasibility of the automatic identification of reservoir flow-rate distribution from wellbore measurements.
|File Size||473 KB||Number of Pages||9|
Azim, R.R.A. 2012. Novel Applications of Distributed TemperatureMeasurements to Estimate Zonal Flow Rate and Pressure in Offshore Gas Wells.Paper SPE 154098 presented at the SPE International Production and OperationsConference and Exhibition, Doha, Qatar, 14-16 May. http://dx.doi.org/10.2118/154098-MS.
Doucet, A. and Ristic, B. 2002. Recursive State Estimation for MultipleSwitching Models With Unknown Transition Probabilities. IEEE Transaction onAerospace and Electrical Systems 38 (3): 1098-1104. http://dx.doi.org/10.1109/TAES.2002.1039427.
Evensen, G. 2006. Data Assimilation: The Ensemble Kalman Filter, NewYork: Springer.
Gryzlov, A., Schiferli, W., and Mudde, R.F. 2010. Estimation of MultiphaseFlow Rates in a Horizontal Wellbore Using an Ensemble Kalman Filter. Paperpresented at the 7th International Conference on Multiphase Flow ICMF, Tampa,Florida, 30 May-4 June.
Hasan, A.R., Kabir, C.S., and Sayarpour, M. 2007. A Basic Approach toWellbore Two-Phase Flow Modeling. Paper SPE 109868 presented at the AnnualTechnical Conference and Exhibition, Anaheim, California, 11-14 November. http://dx.doi.org/10.2118/109868-MS.
Lage, A.C.V.M. and Time, R.W. 2002. An Experimental and TheoreticalInvestigation of Upward Two-Phase Flow in Annuli. SPE J. 7(3): 325-336. http://dx.doi.org/10.2118/79512-PA.
Leemhuis, A.P., Nennie, E.D., Belfroid, S.P.C. et al. 2008. Gas ConingControl for Smart Wells Using a Dynamic Coupled Well-Reservoir Simulator. PaperSPE 112234 presented at the SPE Intelligent Energy Conference and Exhibition,Amsterdam, The Netherlands, 25-27 February. http://dx.doi.org/10.2118/112234-MS.
Leskens, M., de Kruif, B. Smeulers, J. et al. 2008. Downhole MultiphaseMetering in WellsbBy Means of Soft-Sensing. Paper SPE 112046 presented at theSPE Intelligent Energy Conference and Exhibitions, Amsterdam, The Netherlands,25-27 February. http://dx.doi.org/10.2118/112046-MS.
Lorentzen, R.J. and Fjelde, K.K. 2005. Use of Slopelimiter Techniques inTraditional Numerical Methods for Multi-Phase Flow in Pipelines and Wells.International J. 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. Paper SPE 135073presented at the SPE Annual and Technical Conference andExhibition, Florence, Italy, 19-22 September. http://dx.doi.org/10.2118/135073-MS.
Lorentzen, R.J., Sævareid, O., and Nævdal, G. 2010b. Soft Multiphase FlowMetering for Accurate Production Allocation. Paper SPE 136026 presented at theSPE Russian Oil and Gas Technical Conference and Exhibition, Moscow, Russia,26-28 October. http://dx.doi.org/10.2118/136026-MS.
Pitt, M.K. 2002. Smooth Particle Filters for Likelihood Evaluation andMaximisation. Working Paper in Warwick Economics Research Papers,Coventry, England: University of Warwick, Department of Economics.
Pitt, M.K. and Shephard, N. 1999. Filtering Via Simulation Based AuxiliaryParticle Filters. J. Am. Statistical Assoc. 94 (446):590-599.
Shi, H., Holmes, J.A. Durlofsky, L.J. et al. 2005. Drift-Flux Modeling ofTwo-Phase Flow in Wellbores. SPE J. 10 (1): 24-33. http://dx.doi.org/10.2118/84228-PA.
Sworder, D.D. and Boyd, J.E. 1999. Estimation Problems in HybridSystems, Cambridge, UK: Cambridge University Press.