Practical considerations in the application of simulated annealing to stochastic simulation |
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| Authors: | Clayton V Deutsch and Perry W Cockerham |
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| Institution: | (1) Exxon Production Research Co., P.O. Box 2189, 77252-2189 Houston, Texas |
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| Abstract: | Realizations generated by conditional simulation techniques must honor as much data as possible to be reliable numerical models of the attribute under study. The application of optimization methods such as simulated annealing to stochastic simulation has the potential to honor more data than conventional geostatistical simulation techniques. The essential feature of this approach is the formulation of stochastic imaging as an optimization problem with some specified objective function. The data to be honored by the stochastic images are coded as components in a global objective function. This paper describes the basic algorithm and then addresses a number of practical questions: (1) what are the criteria for adding a component to the global objective function? (2) what perturbation mechanism should be employed in the annealing simulation? (3) when should the temperature be lowered in the annealing procedure? (4) how are edge/border nodes handled? (5) how are local conditioning data handled? and (6) how are multiple components weighted in the global objective function? |
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| Keywords: | conditional simulation cosimulation edge effects objective functions simulated annealing |
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