AN EXPERIENCE OF PARAMETRIC ESTIMATION OF DIGITAL OIL RESERVOIR MODELS

A system that is designed to identify parameters of digital oil reservoir models is presented in this paper. An application of common optimization methods, proposed intelligent optimization algorithms and other tools for finding solution, making sensitivity analysis and searching for hidden dependencies between parameters of interest is investigated. The system developed is suited for solving optimization tasks with fitness function value depending on results of resource consuming calculations.

Keywords

parameters' identification, an optimization, genetic algorithms, neural networks, hydrodynamic models