Assisted seismic history matching of the nelson field - Managing large numbers of unknowns by divide and conquer (SPE 154892)

Automatic history matching may be used to condition reservoir simulation models by including time-lapse seismic data. Stochastic optimization algorithms are used to perform a good search of the parameter space and to ensure proper determination of the best models. These approaches can require many thousands of simulations for large dimensional problems. Divide and conquer is an assisted history matching approach that enables deconvolution of the parameters so that they can be searched more efficiently and also leads to better uncertainty analysis. We present an application of this approach to the Nelson field. Nine years of production history data were used along with seismic baseline and monitor surveys. Localised variations were made to permeability and net:gross. We were able to divide the reservoir model into separate parameter regions as a form of localization by combining experimental design and proxy model analysis. The former enabled insignificant parameters to be discarded. The latter showed that each region could be treated as a separate history matching sub-problem which was solved simultaneously using an adapted genetic algorithm. We found that a forty-two dimensional problem could be reduced to a combination of three 9D problems and a 3D problem due to the spatial deconvolution of parameters and misfits. An improved match was obtained for the production and seismic data. Compared to a full stochastic search of the parameter space, the number of models was several orders of magnitude smaller. Further, improved uncertainly analysis was made possible resulting in better forecasting. An improved match to reservoir models leads to better confidence in their prediction and thus they can be used more effectively in reservoir management. The method presented here improves the match and retains the benefits of stochastic searching without the penalty of requiring an impractical number of simulations.