Robust Uncertainty Quantification through Integration of Distributed Gauss-Newton Optimization with Gaussian Mixture Model and Parallelized Sampling Algorithms
- Guohua Gao (Shell Global Solutions, US Inc.) | Jeroen C. Vink (Shell Global Solutions International B.V.) | Chaohui Chen (Shell International Exploration & Production Inc.) | Mariela Araujo (Shell International Exploration & Production Inc.) | Benjamin Ramirez (Shell International Exploration & Production Inc.) | Jim W. Jennings (Shell International Exploration & Production Inc.) | Yaakoub El Khamra (Shell Global Solutions, US Inc.) | Joel Ita (Shell Global Solutions, US Inc.)
- Document ID
- Society of Petroleum Engineers
- SPE Annual Technical Conference and Exhibition, 24-26 September, Dallas, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Petroleum Engineers
- 5.5.8 History Matching, 5.5 Reservoir Simulation, 2.7 Completion Fluids, 2.7.1 Completion Fluids, 5 Reservoir Desciption & Dynamics, 5.6.9 Production Forecasting, 5.6 Formation Evaluation & Management, 2 Well completion
- Distributed Gauss-Newton Method, History Matching, Uncertainty Quantification, Gaussian Mixture Model, Sampling Algorithms
- 0 in the last 30 days
- 175 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 9.50|
|SPE Non-Member Price:||USD 28.00|
Uncertainty quantification of production forecasts is crucially important for business planning of hydrocarbon field developments. This is still a very challenging task, especially when subsurface uncertainties must be conditioned to production data. Many different approaches have been proposed, each with their strengths and weaknesses. In this work, we develop a robust uncertainty quantification workflow by seamless integration of a distributed Gauss-Newton (DGN) optimization method with Gaussian Mixture Model (GMM) and parallelized sampling algorithms. Results are compared with those obtained from other approaches.
Multiple local maximum-a-posteriori (MAP) estimates are located with the local-search DGN optimization method. A GMM is constructed to approximate the posterior probability density function, by fitting simulation results generated during the DGN minimization process. The traditional acceptance-rejection (AR) algorithm is parallelized and applied to improve the quality of GMM samples by rejecting unqualified samples. AR-GMM samples are independent, identically-distributed (i.i.d.) samples that can be directly used for uncertainty quantification of model parameters and production forecasts.
The proposed method is first validated with 1-D nonlinear synthetic problems having multiple MAP points. The AR-GMM samples are better than the original GMM samples. Then, it is tested with a synthetic history-matching problem using the SPE-1 reservoir model with 8 uncertain parameters. The proposed method generates conditional samples that are better than or equivalent to those generated by other methods, e.g., Markov chain Monte Carlo (MCMC) and global search DGN combined with the Randomized Maximum Likelihood (RML) approach, but have a much lower computational cost (by a factor of 5 to 100). Finally, it is applied to a real field reservoir model with synthetic data, having 235 uncertain parameters. A GMM with 27 Gaussian components is constructed to approximate the actual posterior PDF. 105 AR-GMM samples are accepted from the 1000 original GMM samples, and are used to quantify uncertainty of production forecasts. The proposed method is further validated by the fact that production forecasts for all AR-GMM samples are quite consistent with the production data observed after the history matching period.
The newly proposed approach for history matching and uncertainty quantification is quite efficient and robust. The DGN optimization method can efficiently identify multiple local MAP points in parallel. The GMM yields proposal candidates with sufficiently high acceptance ratios for the AR algorithm. Parallelization makes the AR algorithm much more efficient, which further enhances the efficiency of the integrated workflow.
|File Size||2 MB||Number of Pages||23|
Box, G. E. P. and Muller, M. E. A Note on the Generation of Random Normal Deviates. Ann. Math. Statist. 29 (2): 610-611, 1958. http://dx.doi.org/10.1214/aoms/1177706645.
Chen, C., Gao, G., Ramirez, B. A., Vink, J. C., Girardi, A. M. Assisted History Matching of Channelized Models Using Pluri-Principal Component Analysis. SPE J., 2016 (DOI: http://dx.doi.org/10.2118/173192-PA).
Chen, C., Gao, G., Li, R., Cao, R., Chen, T., Vink, J. C., Gelderblom, P. Global Search Distributed-Gauss-Newton Optimization Methods and Its Integration with the Randomized-Maximum-Likelihood Method for Uncertainty Quantification of Reservoir Performance. SPE J. (DOI: http://dx.doi.org/10.2118/182639-PA), June, 2018.
Gao, G., Vink, J. C., Chen, C., Alpak, F. O., Du, K. A Parallelized and Hybrid Data-Integration Algorithm for History Matching of Geologically Complex Reservoirs. SPE J., 2016a (DOI: http://dx.doi.org/10.2118/175039-PA).
Gao, G., Vink, J. C., Chen, C., El Khamra, Y., Tarrahi, M. Distributed Gauss-Newton Optimization Method for History Matching Problems with Multiple Best Matches. Comput. Geosci., 2017 (doi: 10.1007/s10596-017-9657-9).
Grana, D., Fjeldstad, T., and Omer, H. Bayesian Gaussian Mixture Linear Inversion in Geophysical Inverse Problems. Math Geosci, 2017 (doi:10.1007/s11004-016-9671-9).