A Real Parameter Genetic Algorithm for Cluster Identification in History Matching

A012 A REAL PARAMETER GENETIC ALGORITHM FOR CLUSTER IDENTIFICATION IN HISTORY MATCHING Abstract Non-linear inverse problems by their very nature can be expected to yield multiple solutions. This will occur even when the problem is well defined in the sense that the number of measurements is significantly greater than the number of free parameters. These solutions will manifest themselves as local optima for some objective function and will be separated by regions of poor objective function value. In history matching the challenge is to identify all of the high quality local optima and sample the parameter space around them. Within