Best Practices of Assisted History Matching Using Design of Experiments
- Boxiao Li (Chevron Energy Technology Company) | Eric W. Bhark (Chevron Asia Pacific E&P Company) | Stephen J. Gross (Chevron Energy Technology Company (ret.)) | Travis C. Billiter (Chevron Energy Technology Company) | Kaveh Dehghani (Chevron Energy Technology Company)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- August 2019
- Document Type
- Journal Paper
- 1,435 - 1,451
- 2019.Society of Petroleum Engineers
- probabilistic forecast, best practices, design of experiments, assisted history matching, reservoir simulation
- 10 in the last 30 days
- 339 since 2007
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Assisted history matching (AHM) using design of experiments (DOE) is one of the most commonly applied history-matching techniques in the oil and gas industry. When applied properly, this stochastic method finds a representative ensemble of history-matched reservoir models for probabilistic uncertainty analysis of production forecasts. Although DOE-based AHM is straightforward in concept, it can be misused in practice because the work flow involves many statistical and modeling principles that should be followed rigorously.
In this paper, the entire DOE-based AHM work flow is demonstrated in a coherent and comprehensive case study that is divided into seven key stages: problem framing, sensitivity analysis, proxy building, Monte Carlo simulation, history-match filtering, production forecasting, and representative model selection. The best practices of each stage are summarized to help reservoir-management engineers understand and apply this powerful work flow for reliable history matching and probabilistic production forecasting.
One major difficulty in any history-matching method is to define the history-match tolerance, which reflects the engineer’s comfort level of calling a reservoir model “history matched” even though the difference between simulated and observed production data is not zero. It is a compromise to the intrinsic and unavoidable imperfectness of reservoir-model construction, data measurement, and proxy creation. A practical procedure is provided to help engineers define the history-match tolerance considering the model, data-measurement, and proxy errors.
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Aanonsen, S. I., Nævdal, G., Oliver, D. S. et al. 2009. The Ensemble Kalman Filter in Reservoir Engineering—A Review. SPE J. 14 (3): 393–412. SPE-117274-PA. https://doi.org/10.2118/117274-PA.
Bhark, E., Rey, A., Datta-Gupta, A. et al. 2012. A Multiscale Workflow for History Matching in Structured and Unstructured Grid Geometries. SPE J. 17 (3): 828–848. SPE-141764-PA. https://doi.org/10.2118/141764-PA.
Bhark, E. W. and Dehghani, K. 2014. Assisted History Matching Benchmarking: Design of Experiments-Based Techniques. Presented at the SPE Annual Technical Conference and Exhibition, Amsterdam, 27–29 October. SPE-170690-MS. https://doi.org/10.2118/170690-MS.
Burkardt, J. 2005. IHS: Improved Distributed Hypercube Sampling, http://people.sc.fsu.edu/~jburkardt/cpp_src/ihs/ihs.html (accessed 1 May 2019).
Castellini, A., Landa, J. L., and Kikani, J. 2004. Practical Methods for Uncertainty Assessment of Flow Predictions for ReservoirsWith Significant History—A Field Case Study. Presented at ECMOR IX–9th European Conference on the Mathematics of Oil Recovery, Cannes, France, 30 August–2 September.
Chatterjee, S. and Hadi, A. S. 2015. Regression Analysis by Example. Hoboken, New Jersey: John Wiley & Sons.
Chen, W. H., Gavalas, G. R., Seinfeld, J. H. et al. 1974. A New Algorithm for Automatic History Matching. SPE J. 14 (6): 593–608. SPE-4545-PA. https://doi.org/10.2118/4545-PA.
Cheng, H., Dehghani, K., and Billiter, T. C. 2008. A Structured Approach for Probabilistic-Assisted History Matching Using Evolutionary Algorithms: Tengiz Field Applications. Presented at the SPE Annual Technical Conference and Exhibition, Denver, 21–24 September. SPE-116212-MS. https://doi.org/10.2118/116212-MS.
Datta-Gupta, A. and King, M. J. 2007. Streamline Simulation: Theory and Practice, Vol. 11. Richardson, Texas: Textbook Series, Society of Petroleum Engineers.
Deutsch, C. V. and Journel, A. G. 1992. GSLIB: Geostatistical Software Library and User’s Guide. New York City: Oxford University Press.
Doherty, J. 2003. Ground Water Model Calibration Using Pilot Points and Regularization. Groundwater 41 (2): 170–177. https://doi.org/10.1111/j.1745-6584.2003.tb02580.x.
Emerick, A. A. and Reynolds, A. C. 2013. Ensemble Smoother With Multiple Data Assimilation. Comput Geosci 55 (June): 3–15. https://doi.org/10.1016/j.cageo.2012.03.011.
Fenwick, D., Scheidt, C., and Caers, J. 2014. Quantifying Asymmetric Parameter Interactions in Sensitivity Analysis: Application to Reservoir Modeling. Math Geosci 46 (4): 493–511. https://doi.org/10.1007/s11004-014-9530-5.
Gavalas, G. R., Shah, P. C., and Seinfeld, J. H. 1976. Reservoir History Matching by Bayesian Estimation. SPE J. 16 (6): 337–350. SPE-5740-PA. https://doi.org/10.2118/5740-PA.
Hastie, T., Tibshirani, R., and Friedman, J. 2009. The Elements of Statistical Learning, second edition. New York City: Springer Series in Statistics, Springer.
He, J., Reynolds, A. C., Tanaka, S. et al. 2018. Calibrating Global Uncertainties to Local Data: Is the Learning Being Over-Generalized? Presented at the SPE Annual Technical Conference and Exhibition, Dallas, 24–26 September. SPE-191480-MS. https://doi.org/10.2118/191480-MS.
Helton, J. C., Johnson, J. D., Sallaberry, C. J. et al. 2006. Survey of Sampling-Based Methods for Uncertainty and Sensitivity Analysis. Reliab Eng Syst Safe 91 (10–11): 1175–1209. https://doi.org/10.1016/j.ress.2005.11.017.
Jafarpour, B. and McLaughlin, D. B. 2009. Reservoir Characterization With the Discrete Cosine Transform. SPE J. 14 (1): 182–201. SPE-106453-PA. https://doi.org/10.2118/106453-PA.
James, G., Witten, D., Hastie, T. et al. 2013. An Introduction to Statistical Learning With Applications in R. New York City: Springer.
Joosten, G. J. P., Altintas, A., Van Essen, G. et al. 2014. Reservoir Model Maturation and Assisted History Matching Based on Production and 4D Seismic Data. Presented at the SPE Annual Technical Conference and Exhibition, Amsterdam, 27–29 October. SPE-170604-MS. https://doi.org/10.2118/170604-MS.
King, G. R., Lee, S., Alexandre, P. et al. 2005. Probabilistic Forecasting for Mature Fields With Significant Production History: A Nemba Field Case Study. Presented at the SPE Annual Technical Conference and Exhibition, Dallas, 9–12 October. SPE-95869-MS. https://doi.org/10.2118/95869-MS.
Larue, D. K. and Hovadik, J. 2012. Rapid Earth Modelling for Appraisal and Development Studies of Deep-Water Clastic Reservoirs and the Concept of “Procycling”. Pet Geosci 18 (2): 201–218. https://doi.org/10.1144/1354-079311-033.
LaVenue, A. M., RamaRao, B. S., De Marsily, G. et al. 1995. Pilot Point Methodology for Automated Calibration of an Ensemble of Conditionally Simulated Transmissivity Fields: 2. Application. Water Resour Res 31 (3): 495–516. https://doi.org/10.1029/94WR02259.
Li, B. and Friedmann, F. 2005. Novel Multiple Resolutions Design of Experiment/Response Surface Methodology for Uncertainty Analysis of Reservoir Simulation Forecasts. Presented at the SPE Reservoir Simulation Symposium, Houston, 31 January–2 February. SPE-92853-MS. https://doi.org/10.2118/92853-MS.
Li, R., Reynolds, A. C., and Oliver, D. S. 2003. History Matching of Three-Phase Flow Production Data. SPE J. 8 (4): 328–340. SPE-87336-PA. https://doi.org/10.2118/87336-PA.
Montgomery, D. C. 2008. Design and Analysis of Experiments. Hoboken, New Jersey: John Wiley & Sons.
Oliver, D. S. and Chen, Y. 2011. Recent Progress on Reservoir History Matching: A Review. Computat Geosci 15 (1): 185–221. https://doi.org/10.1007/s10596-010-9194-2.
Sarma, P., Chen, W. H., and Xie, J. 2013. Selecting Representative Models From a Large Set of Models. Presented at the SPE Reservoir Simulation Symposium, The Woodlands, Texas, 18–20 February. SPE-163671-MS. https://doi.org/10.2118/163671-MS.
Sarma, P., Yang, C., Xie, J. et al. 2015. Identification of “Big Hitters" With Global Sensitivity Analysis for Improved Decision Making Under Uncertainty. Presented at the SPE Reservoir Simulation Symposium, Houston, 23–25 February. SPE-173254-MS. https://doi.org/10.2118/173254-MS.
Schaaf, T., Coureaud, B., Labat, N. et al. 2009. Using Experimental Designs, Assisted History-Matching Tools, and Bayesian Framework To Get Probabilistic Gas-Storage Pressure Forecasts. SPE Res Eval & Eng 12 (5): 724–736. SPE-113498-PA. https://doi.org/10.2118/113498-PA.
Schmidt, S. R. and Launsby, R. G. 1989. Understanding Industrial Designed Experiments. Colorado, Springs, Colorado: Air Academy Press.
Schulze-Riegert, R. W., Axmann, J. K., 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.
Skjervheim, J. and Evensen, G. 2011. An Ensemble Smoother for Assisted History Matching. Presented at the SPE Reservoir Simulation Symposium, The Woodlands, Texas, 21–23 February. SPE-141929-MS. https://doi.org/10.2118/141929-MS.
Van Doren, J., Van Essen, G., Wilson, O. B. et al. 2012. A Comprehensive Workflow for Assisted History Matching Applied to a Complex Mature Reservoir. Presented at the SPE Europec/EAGE Annual Conference, Copenhagen, Denmark, 4–7 June. SPE-154383-MS. https://doi.org/10.2118/154383-MS.
van Leeuwen, P. J. and Evensen, G. 1996. Data Assimilation and Inverse Methods in Terms of a Probabilistic Formulation. Mon Weather Rev 124 (12): 2898–2913. https://doi.org/10.1175/1520-0493(1996)124%3C2898:DAAIMI%3E2.0.CO;2.
Vasco, D. W., Seongsik, Y., and Datta-Gupta, A. 1999. Integrating Dynamic Data Into High-Resolution Reservoir Models Using Streamline-Based Analytic Sensitivity Coefficients. SPE J. 4 (4): 389–399. SPE-59253-PA. https://doi.org/10.2118/59253-PA.
Watanabe, S., Han, J., Hetz, G. et al. 2017. Streamline-Based Time-Lapse-Seismic-Data Integration Incorporating Pressure and Saturation Effects. SPE J. 22 (4): 1261–1279. SPE-166395-PA. https://doi.org/10.2118/166395-PA.
Williams, M. A., Keating, J. F., and Barghouty, M. F. 1998. The Stratigraphic Method: A Structured Approach to History Matching Complex Simulation Models. SPE Res Eval & Eng 1 (2): 169–176. SPE-38014-PA. https://doi.org/10.2118/38014-PA.
Yeten, B., Castellini. A., Guyaguler, B. et al. 2005. A Comparison Study on Experimental Design and Response Surface Methodologies. Presented at the SPE Reservoir Simulation Symposium, Houston, 31 January–2 February. SPE-93347-MS. https://doi.org/10.2118/93347-MS.