Abstract
In oil industry and subsurface hydrology, geostatistical models are often used to represent the porosity or the permeability field. In history matching of a geostatistical reservoir model, we attempt to find multiple realizations that are conditional to dynamic data and representative of the model uncertainty space. A relevant way to simulate the conditioned realizations is by generating Monte Carlo Markov chains (MCMC). The huge dimensions (number of parameters) of the model and the computational cost of each iteration are two important pitfalls for the use of MCMC. In practice, we have to stop the chain far before it has browsed the whole support of the posterior probability density function. Furthermore, as the relationship between the production data and the random field is highly nonlinear, the posterior can be strongly multimodal and the chain may stay stuck in one of the modes. In this work, we propose a methodology to enhance the sampling properties of classical single MCMC in history matching. We first show how to reduce the dimension of the problem by using a truncated Karhunen–Loève expansion of the random field of interest and assess the number of components to be kept. Then, we show how we can improve the mixing properties of MCMC, without increasing the global computational cost, by using parallel interacting Markov Chains. Finally, we show the encouraging results obtained when applying the method to a synthetic history matching case.
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Andrieu, C., Jasra, A., Doucet, A., Moral, P.D.: On non-linear Markoc Chain Monte Carlo via self-interacting approximations. Tech. rep., University of Bristol (2007)
Andrieu, C., Moulines, E.: On the ergodicity properties of some adaptive MCMC algorithms. Ann. Appl. Probab. 16, 1462–1505 (2003)
Andrieu, C., Robert, C.: Controlled MCMC for Optimal Sampling. Tech. rep., Cérémade, Université de PARIS - DAUPHINE (2001)
Caers, J., Hoffman, T.: The probability perturbation method: a new look at bayesian inverse modeling. Math. Geol. 38(1), 81–100 (2006)
Chen, Y., Zhang, D.: Data assimilation for transient flow in geologic formations via ensemble Kalman filter. Adv. Water Res. 29, 1107–1122 (2006)
Dostert, P., Efendiev, Y., Hou, T., Luo, W.: Coarse-gradient Langevin algorithms for dynamic data integration and uncertainty quantification. J. Comput. Phys. 217(1), 123–142 (2006)
Earl, D., Deem, M.: Parallel tempering : theory, applications, and new perspectives. Phys. Chem. Chem. Phys. 7, 3910–3916 (2005)
Gavalas, G., Shah, P., Seinfeld, J.: Reservoir history matchng by Bayesian estimation. SPE J. 16(6), 337–350 (1976)
Geyer, C.: Markov chain Monte Carlo maximum likelihood. In: Computing Science and Statistics: Proceedings of 23rd Symposium on the Interface Foundation, p. 156. American Statistical Association, Fairfax Station, New York (1991)
Ghanem, R., Spanos, P.: Stochastic Finite Elements, a Spectral Approach. Springer, New York (1991)
Haario, H., Saksman, E., Tamminen, J.: An adaptive metropolis algorithm. Bernoulli 7, 223–242 (2001)
Holden, L., Sannan, S., Soleng, H., Arntzen, O.: History Matching using Adaptive Chains. Tech. rep., Norwegian Computing Center (2002)
Hu, L.Y.: Gradual deformation and iterative calibration of Gaussian-Related stochastic models. Math. Geol. 32(1), 87–108 (2000)
Iba, Y.: Extended ensemble Monte Carlo. Int. J. Modern Phys. C 12(5), 623–656 (2001)
Kitanidis, P.: Quasi-linear geostatistical theory for inversing. Water Resour. Res. 31(10), 2411–2419 (1995)
Kou, S., Zhou, Q., Wong, W.: Equi-energy sampler with applications in statistical inference and statistical mechanics. Ann. Stat. 34(4), 1581–1619 (2006)
Lehoucq, R., Sorensen, D., Yang, C.: ARPACK Users’ Guide: Solution of Large Scale Eigenvalue Problems with Implicitly Restarted Arnoldi Methods. SIAM, Philadelphia (1997)
Loève, M.: Probability Theory. Princeton University Press, Princeton (1955)
Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A.T.M.: Equations of state calculations by fast computing machines. J. Chem. Phys. 21, 1087–1091 (1953)
Oliver, D.: On conditional simulation to inaccurate data. Math. Geol. 28(6), 811–817 (1996)
Oliver, D., Reynolds, A., Bi, Z., Abacioglu, Y.: Integration of production data into reservoir models. Pet. Geosci. 7(9), 65–73 (2001)
Oliver, D.S., Cunha, L.B., Reynolds, A.C.: Markov chain Monte Carlo methods for conditionning a permeability field to pressure data. Math. Geol. 29(1), 61–91 (1997)
Robert, C., Casella, G.: Monte-Carlo Statistical Methods, 2nd edn. Springer, New York (2004)
Romary, T., Hu, L.: Assessing the dimensionality of random fields with Karhunen–Loève expansion. In: Petroleum Geostatistics 2007. EAGE, Cascais, 10–14 September 2007
Sarma, P., Durlofsky, L., Aziz, K., Chen, W.: Efficient real-time reservoir management using adjoint-based optimal control and model updating. Comput. Geosci. 10(1), 3–36 (2006)
StreamSim Technologies, Inc.: 3dsl User Manual, Version 2.10 edn. (2003)
Tarantola, A.: Inverse Problem Theory and Model Parameter Estimation. SIAM, Philadelphia (2005)
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Romary, T. Integrating production data under uncertainty by parallel interacting Markov chains on a reduced dimensional space. Comput Geosci 13, 103–122 (2009). https://doi.org/10.1007/s10596-008-9108-8
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DOI: https://doi.org/10.1007/s10596-008-9108-8