Updating static reservoir models based on seismic matching loop approach

Hydrocarbon reservoir modeling is an important tool in characterizing reservoir and forecasting its performance. A realistic reservoir model can be generated by combining different datasets in an optimal manner. Seismic data has always been of interest due to its extensive areal coverage and high lateral resolution compared to well-based data. Using time-lapse (4D) seismic data, the reservoir dynamic modeling is improved. Integrating 4D seismic data into dynamic reservoir modeling process requires that the static reservoir model to be coherent with the pre-production 2D/3D seismic data. To do so, we use seismic matching loop approach, which is based on geostatistical techniques and optimization algorithms to update static reservoir models. A key point in this regard is that seismic data or information is used twice. Acoustic data are first used as a constraint when generating reservoir models based on a geostatistical simulation algorithm. Then, they are compared to the acoustic responses computed for the simulated reservoir models. The implemented optimization algorithm in this study is a hybrid of particle swarm optimization (PSO) and genetic algorithm (GA). The obtained results on a synthetic case study show that the proposed method, compared to the conventional geostatistical methods, results in generation of static reservoir models that are qualitatively and quantitatively more consistent with pre-production seismic data.