Optimal field synthesis for enhancing the modeling capabilities of reservoir/aquifer fields

One field identified by an inverse method is one of multiple candidate solutions those are independently obtained through a specific estimation technique. While averaging of optimized fields can provide a better description of the spatial feature of an unknown field, it deteriorates the flow and transport characteristics of the optimized fields. As a result, the averaged field is not suited for modeling aquifer performances. Based on genetic algorithm, an optimal field synthesis technique is developed, which combines diversely optimized fields into a refined group of fields. Each field in the population is paired, and a sub-region of each field is exchanged by crossover operation to create a group of synthesized fields of enhanced modeling capability. The population of the fields is evolved till the synthesized fields become sufficiently similar. Applications of the optimal field synthesis to synthetic cases indicate that the objective functions of the fields assessing the modeling capabilities are further reduced after the optimal field synthesis. The identified fields from various inverse techniques may yield a range of modeling results under varied flow situations. The uncertainty is narrowed down through the optimal field synthesis and the associated modeling results converge on that of the reference field. The developed inverse modeling facilitates the construction of a reliable simulation model and hence trustworthy predictions of the future performances.