A consistent stochastic model for faults and horizons is described. The faults are represented as a parametric invertible deformation operator. The faults may truncate each other. The horizons are modeled as correlated Gaussian fields and are represented in a grid. Petrophysical variables may be modeled in a reservoir before faulting in order to describe the juxtaposition effect of the faulting. It is possible to condition the realization on petrophysics, horizons, and fault plane observations in wells in addition to seismic data. The transmissibility in the fault plane may also be included in the model. Four different methods to integrate the fault and horizon models in a common model is described. The method is illustrated on an example from a real petroleum field with 18 interpreted faults that are handled stochastically.
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- Abrahamsen, P., 1992, Bayesian Kriging for seismic depth conversion of a multi-layer reservoir, in Proceedings from Fourth International Geostatistical Congress, Troia, Portugal, September 13-18.Google Scholar
- Journel, A. G., and Huijbregts, Ch. J., 1978, Mining geostatistics: Academic Press, New York, 600 p.Google Scholar
- Hollund, K., Mostad, P., Nielsen, B. F., and Holden, L., 2000, Havana—A fault modeling tool, in proceedings from the Norw. Petr. Soc. Conference, Stavanger, Norway.Google Scholar
- Hollund, K., Mostad, P., Nielsen, B. F., and Holden, L., 2003, Havana web page: www.nr.noGoogle Scholar
- Lia, O., Omre, H., Tjelmeland, H., Holden, L., and Egeland, T., 1997, Uncertainties in reservoir production forecasts: ssoc. Petrol. Geol. Bull, v.81, no.5, p. 775-802.Google Scholar
- Lindsay, N. G., Murphy, F. C., Walsh, J. J., and Watterson, J., 1993, Outcrop studies of shale smear on fault surfaces, in Flint, S. S., and Bryant, I. D., eds., The geological modeling of hydrocarbon reservoirs and outcrop analogues, The International Association of Sedimentologists, Special Publication, No. 1: Blackwell Science, Oxford, p. 113-123.Google Scholar
- Manzocchi, T., Walsh, J.J., Nell, P. and Yielding, G., 1999, Fault transmissibility multipliers for flow simulation models: Petrol. Geosci., v. 5, p. 53-63.Google Scholar
- Omre, H., and Sølna, K., 1990, Stochastic modeling and simulation of fault zones, in Proceedings from The Second CODATA Conference on Geomathematics and Geostatistics, Leeds, England.Google Scholar
- Omre, H., Sølna, K., Dahl, N., and Tørudbakken, B., 1992, Impact of fault heterogeneity in fault zones on fluid flow, in Aasen, J. O., Berg, E., Buller, A. T., Hjelmeland, O., Holt, R. M., Kleppe, J., and Torsæter, O., eds., Third International Conference on North Sea Oil and Gas Reservoirs, Trondheim, November 30–December 2, p. 185-200.Google Scholar
- Ottesen, S. and Townsend, C., Investigating the effect of varying fault geometry and transmissibility on recovery. Using the (HAVANA-SUM) workflow for structural uncertainty modeling in a clastic reservoir, manuscript in preparation.Google Scholar
- Thore, P., Shtuka, A., Lecour, M., Ait-Ettajer, T., and Cognot, R., 2002, Structural uncertainties: Determination, management, and applications: Geophysics, v.67, no.3, p. 840-852.Google Scholar
- Yielding, G., Freeman, B., and Needham, D. T., 1997, Quantitative fault seal prediction: Am Assoc. Petrol. Geol. Bull, v.81, no.6, p. 897-917.Google Scholar