Production Data Integration In Reservoir Modeling For Large Oil And Gas Fields

Optimal reservoir management requires reliable reservoir performance forecasts with as little uncertainty as possible. The reliable reservoir porosity/permeability models are the foundation of reliable reservoir performance forecasts. Therefore, there is a need to integrate the available information including static and production data in reservoir modeling. Production data contain important information about reservoir property models and fluids so that production data are often needed to integrate in reservoir modeling. However, challenges remain in the conditioning of reservoir property models to production data in large scale fields with a long production/injection history: (1) a need of many flow simulation runs makes many method impracticable; (2) high heterogeneous property models, multiple phases and complex well system are difficult to be handled by many methods; (3)pressure and production rates are not available to integrate in reservoir modeling simultaneously by using streamline-based methods.This thesis proposed two methods on production data integration in reservoir modeling for large fields: one method can integrate the information from well test interpretation and the other method can integrate production rates and well bottom hole pressure.The method that can integrate the information from well test interpretation gives a method to create porosity models that honor interpreted pore volumes from well test data without a need of flow simulation. It combines core data, seismic data and connected pore volume interpreted from well test data in co-simulation. Factors considering the connected pore volumes from well test interpretation and co-simulated porosity models are introduced to update seismic dada. The updated seismic data are then used in co-simulation to create the updated porosity models. Seismic data is modified iteratively until the well test pore volumes calculated from the co-simulated porosity models match the values interpreted from well test. The advantage of this method is there is no need for flow simulation runs. The applicationshows that this method can create porosity models honoring well test information and reduce the uncertainty of the co-simulated porosity models.This thesis proposed a superposition theory to show the relationship between reservoir responses caused by joint multiple property perturbations and the sum of the reservoir responses caused by individual property perturbation. The superposition theory is used to calculate all sensitivity coefficients. Instead of analytical calculation of the sensitivity coefficients, all sensitivity coefficients with respect to all perturbations required in the methodology are calculated in a numerical way through only two flow simulation runs. The approximate sensitivity coefficients are then used in optimization to get optimal property changes without flow simulation. The optimal changes are used to locally update the property models. The iterative scheme is used to eliminate the errors caused by sensitivity coefficients and the assumption of the linear relationship between property changes and the pressure/rate response changes. The procedure is iterated until the fit is satisfied or fit can not be improved. Applications illustrate the practicability of the proposed method. The methodology can reduce the mismatch between production data and simulation results. Suitable selection of parameters can make the methodology more efficient.