Parallelization of Ensemble Kalman Filter (EnKF) for Oil Reservoirs with Time-lapse Seismic Data
In this paper we describe the design and implementation of a parallel algorithm for data assimilation with ensemble Kalman filter (EnKF) for oil reservoir history matching problem. The use of large number of observations from time-lapse seismic leads to a large turnaround time for the analysis step, in addition to the time consuming simulations of the realizations. For efficient parallelization it is important to consider parallel computation at the analysis step. Our experiments show that parallelization of the analysis step in addition to the forecast step has good scalability, exploiting the same set of resources with some additional efforts.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1086985Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1856
 J. D. Jansen and S. D. Douma and D. R. Brouwer and P. M. J. Van den Hof and O. H. Bosgra and A. W. Heemink. Closed loop reservoir management. In SPE Reservoir Simulation Symposium, The Woodlands, Texas, U.S.A., February 2009. Society of Petroleum Engineers.
 S. Gillijns and O. BarreroMendoza and J. Chandrasekar and B. L. R. De- Moor and D. S. Bernstein and A. Ridley. What is the ensemble Kalman filter and how well does it work? In Proceedings of the 2006 American Control Conference, pp. 4448-4453, 2006.
 Herschel L. Mitchell and P. L. Houtekamer, An Adaptive Ensemble Kalman Filter, MONTHLY WEATHER REVIEW, 128(2): 416–433, February 2000.
 P. L. Houtekamer and Herschel L. Mitchell, A Sequential Ensemble Kalman Filter for Atmospheric Data Assimilation, MONTHLY WEATHER REVIEW, 129(1):123–137, JANUARY 2001.
 Christian L. Keppenne. Data assimilation into a primitive-equation model with a parallel ensemble Kalman filter. MONTHLY WEATHER REVIEW, 128(6):1971-1981, June 2000.
 Christian L. Keppenne and Michele M. Rienecker. Initial testing of a massively parallel ensemble Kalman filter with the poseidon isopycnal ocean general circulation model. MONTHLY WEATHER REVIEW, 130(12):2951-2965, December 2002.
 Christian L. Keppenne and Michele M. Rieneckerb, Assimilation of temperature into an isopycnal ocean general circulation model using a parallel ensemble Kalman filter, Journal of Marine Systems, 40-41 (2003), pp. 363380.
 Teng Xua and J. Jaime Gomez-Hernandeza and Liangping Lia and Haiyan Zhoua, Parallelized Ensemble Kalman Filter for Hydraulic Conductivity Characterization, Computers & Geosciences, 52: 42-49, March 2013.
 Edward Ott and Brian R. Hunt and Istvan Szunyogh and Aleksey V. Zimin and Eric J. Kostelich and Matteo Corazza and Eugenia Kalnay and D. J. Patil and James A. Yorkey, A local ensemble Kalman filter for atmospheric data assimilation, Tellus (2004), 56A, pp. 415-428.
 Jan Mandel, Efficient Implementation of the Ensemble Kalman Filter , CCM Report 231, May 2006, http://math.ucdenver.edu/ccm/reports/rep231.pdf
 S. Zhang and M. J. Harrison and A. T. Wittenberg and A. Rosati and J. L. Anderson and V. Balaji, Initialization of an ENSO (El NinoSouthern Oscillation (ENSO)) Forecast System Using a Parallelized Ensemble Filter, MONTHLY WEATHER REVIEW, 133: 3176–3201.
 Jeffrey L. Anderson and Nancy Collins, Scalable Implementations of Ensemble Filter Algorithms for Data Assimilation, JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY, 24(8): 1452–1463, AUGUST 2007.
 Lars Nerger and Wolfgang Hiller, Software for ensemble-based data assimilation systems - Implementation strategies and scalability, Computers & Geosciences, Available online 7 April 2012.
 Lars Nerger and Wolfgang Hiller and Jens Schroeter, PDAF - the Parallel Data Assimilation Framework: Experiences with Kalman filtering. In: Zwieflhofer, W., Mozdzynski, G. (Eds.), Use of High Performance Computing in Meteorology - Proceedings of the 11. ECMWF Workshop. World Scientific, pp. 63-83.
 Janji Tijana and Lars Nerger and Alberta Albertella and Jens Schroeter and Sergey Skachko, On Domain Localization in Ensemble-Based Kalman Filter Algorithms, Monthly Weather Review, 139: 2046-2060, 2011.
 B. Liang and K. Sepehrnoori and M. Delshad, An Automatic History Matching Module with Distributed and Parallel Computing, Petroleum Science and Technology, 27(10): 1092–1108, January 2009.
 Reza Tavakoli and Gergina Pencheva and Mary F. Wheeler. Multi-level parallelization of ensemble Kalman filter for reservoir history matching. In 2011 SPE Reservoir Simulation Symposium.
 Reza Tavakoli and Gergina Pencheva and Mary F. Wheeler and Benjamin Ganis, A parallel ensemble-based framework for reservoir history matching and uncertainty characterization, Computational Geosciences, 17(1): 83–97, February 2013.
 L. Xin, Continuous Reservoir Model Updating by Ensemble Kalman Filter on Grid Computing Architectures, Ph.D. thesis, Louisiana State University, Baton Rouge, Louisiana, 2008.
 G. Evensen.The Ensemble Kalman Filter for combined state and parameter estimation, Monte Carlo techniques for data assimilation in large systems. IEEE CONTROL SYSTEMS MAGAZINE, 2009.
 R. E. Kalman. A new approach to linear filter and prediction problems. J. Basic Eng., 82:35-45, 1960.
 S. Gillijns and O. BarreroMendoza and J. Chandrasekar and B. L. R. De- Moor and D. S. Bernstein and A. Ridley. What is the ensemble Kalman filter and how well does it work? In Proceedings of the 2006 American Control Conference, pages 4448-4453, 2006.
 Eric W. Weisstein, Monte carlo method, http://mathworld.wolfram.com/MonteCarloMethod.html, Last visited on 26-02-2013.
 G. Evensen. Data Assimilation: The Ensemble Kalman Filter. Springer: New York, 2007.