Time-lapse Seismic Attributes Analysis Based on Parallel Genetic Algorithm

摘要

Reservoir characterization prediction of time-lapse seismic data using seismic attributes is an important technique because it allows extrapolation of reservoir characterization throughout a seismic volume. This study presents a new method for choosing the seismic attribute of time-lapse seismic using a genetic algorithm approach. The genetic algorithm is a desirable method to select the best combination of attributes. Attribute selection using genetic algorithm can choose the optimal number and type of seismic attributes for reservoir characterization prediction. One of the major disadvantages of genetic algorithms is that they are very slow. Jn this paper we show how the execution time can be reduced by using a commercial shared memory multiprocessor. This work uses the processing power of a network of heterogeneous computers (PCs, workstations, etc.) for the parallel and efficient execution of the genetic algorithms using MPI.