The probability perturbation method: A new look at Bayesian inverse modeling

Mathematical Geology

Volumn 38, Issue 1, 2006, Pages 81-100

  Caers, Jef ^a   Hoffman, Todd ^a  

^a MONTANA TECH OF THE UNIVERSITY OF MONTANA   (United States)

Author keywords

Bayes' theory; Geostatistics; History matching; Inverse modeling

Indexed keywords

COMPUTER SIMULATION; EARTH SCIENCES; INVERSE PROBLEMS; ITERATIVE METHODS; MATHEMATICAL MODELS; PERTURBATION TECHNIQUES; PROBABILITY; STATISTICAL METHODS;

BAYESIAN ANALYSIS; GEOSTATISTICS; INVERSE PROBLEM; NUMERICAL MODEL; PROBABILITY;

EID: 33745177370     PISSN: 08828121     EISSN: 15738868     Source Type: Journal    
DOI: 10.1007/s11004-005-9005-9     Document Type: Article

References (16)

1. Besag, J., and Green, P. J., 1993, Spatial statistics and Bayesian computation: J. R. Stat. Soc. B, v. 55, p. 3-23.
2. Caers, J., 2003, History matching under a training image-based geological model constraint: Soc. Petrol. Eng. J., SPE #74716, p. 218-226
3. Deutsch, C. V., and Journel, A. G., 1998, GSLIB: The geostatistical software library: Oxford University Press, New York, 369 p.
4. Hegstad, B. K., and Omre, H., 1996, Uncertainty assessment in history matching and forecasting, in Baafi, E. Y., and Schofield, N. A., eds., Geostatistics Wollongong, Kluwer, Dordrecht, v. 1, p. 585-596.
5. Hoffman, B. T., and Caers, J., 2003, Geostatistical history matching using the regional probability perturbation method, in Proceedings of the SPE Annual Conference and Technical Exhibition, Soc. Petrol. Eng., Paper no. 84409, Denver, CO, October 5-8, 16 p.
6. Hoffman, B. T., and Caers, J., 2004, History matching with the regional probability perturbation method - Applications to a North Sea reservoir, in Proceedings of the ECMOR IX, Cannes, August 29 - September 2, 2004.
7. Hu, L.-Y., Blanc, G., and Noetinger, B., 2001, Gradual deformation and iterative calibration of sequential simulations: Math. Geol., v. 33, p. 475-489.
8. Journel, A. G., 1993, Geostatistics: Roadblocks and challenges, in Soares, A., ed., Geostatistic-Troia: Kluwer Academic, Dordrecht, v. 1, p. 213-224.
9. Journel, A. G., 2002, Combining knowledge from diverse data sources: An alternative to traditional data independence hypothesis: Math. Geol., v. 34, p. 573-596.
10. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E., 1953, Equation of state calculation by fast computing machines: J. Chem. Phys., v. 21, p. 1087-1092.
11. Moosegard, K., and Tarantola, A., 1995, Monte Carlo sampling of solutions to inverse problems: J. Geophys. Res. B, v. 100, p. 12431-12447.
12. Neal, R. M., 1993, Probabilistic inference using Markov chain Monte Carlo methods: Technical report CRG-TR-93-1, Department of Computer Science, University of Toronto.
13. Omre, H., and Tjelmeland, H., 1996, Petroleum geostatistics, in Baafi, E.Y., and Schofield, N. A. eds., in Geostatistics Wollongong: Kluwer, Dordrecht, v. 1, p. 41-52.
14. Strebelle, S., 2002, Conditional simulation of complex geological structures using multiple-point geostatistics: Math. Geol., v. 34, p. 1-22.
15. Suzuki, S., 2003, Determining petrophysical properties of facies using a hierarchical history matching method: MSc Thesis, Stanford University, Stanford, CA.
16. Tarantola, A., 1987, Inverse problem theory: Elsevier Science, Amsterdam, 613 p.