Optimization of an Integrated Reservoir-Production System Using Polynomial Chaos Expansion and Sobol Sensitivity Analysis

Integrated reservoir-production modeling is a collaborative multidisciplinary tool that can facilitate optimization of oil and gas production operations during the field development planning stage of exploiting subsurface resources. The critical issue with this technique is the excessive computational burden of the large integrated model with many input variables, which has not been effectively addressed to date. This study aims to reduce the computational costs and runtimes associated with the production integration and optimization process from oil fields. To do so, the reservoir and the surface network models of an Iranian oil field were coupled to create an integrated model for the optimization of field parameters to achieve the highest oil production rate. In the first step of simplification, polynomial chaos expansion (PCE) was used to establish a surrogate model from the integrated system. Next, Sobol sensitivity analysis, which is a variance-based, global, and model-free sensitivity analysis technique, was performed to reduce the number of input variables by identifying the most influential variables. Finally, the optimization was implemented using genetic algorithm (GA) on the PCE surrogate model of the integrated system with the most important variables. The results from the case study showed that the integrated model can be replaced with the PCE surrogate model while the accuracy is maintained. Moreover, performing sensitivity analysis considerably decreased the number of input variables for optimization by revealing their significance. The proposed methodology in this study can substantially improve the computational efficiency of the optimization for the integrated reservoir-production system.