A New, Fast Parallel Simulated Annealing Algorithm for Reservoir Characterization

Abstract

ABSTRACT This paper presents a new parallel simulated annealing algorithm for computational intensive problems. The new algorithm enables us to reduce the overall time required to solve reservoir engineering problems by using the simulated annealing method (SAM). A simple geostatistical optimization problem (variogram matching) applied to two fields is used for illustration purposes. The reduction of computation time starts by optimizing the sequential simulated annealing algorithm. This task is achieved by an efficient coding and an appropriate choice of topology. Three different topologies are used and their effects on the overall run time and the quality of the generated image are discussed. After optimizing the sequential algorithm, the problem of high rejection rate at low annealing temperature is solved by using parallelization. The new algorithm uses, in an optimal manner concurrently many sequential algorithms. The number of concurrent algorithms is adjusted throughout the optimization to increase the acceptance rate with the optimal use of a CPU. The new algorithm was implemented on a CRAY Y-MP with 4 processors. A 50,400 (280x180) gridblock field was used to test the parallel optimization method. The overall run (clock) time was reduced by approximately the number of concurrent calls of the sequential algorithm.