PREDICTION OF RESERVOIR PERMEABILITY USING GENETIC ALGORITHMS

The determination of permeability is an example of many geological pro blems where laboratory-measured data is expensive and limited in quant ity. We related permeability values to well logs. We used neural netwo rks trained both with the popular backpropagation algorithm and with a genetic algorithm. The genetic training produced smaller errors and b etter generalization than backpropagation training on the same network topology. The cost includes,greater average computation time as well as,greater variation in computation time for the genetic training. The genetic training is robust and not sensitive to selection of the cros sover and mutation parameters.