Various studies have shown that optimization of SAGD well locations and operating conditions has a great potential to improve the economics of SAGD operations. However, such optimization often ignores geological uncertainties of the reservoir and is based on a single realization. Due to the significant impact of reservoir properties on SAGD performance, the optimal solution obtained based on a single realization may deviate severely from the actual optimality.

This paper presents a SAGD optimization workflow which takes into account geological uncertainties of the reservoir. To capture geological uncertainties, a large number of realizations need to be generated honoring geological constraints. Due to the high computational cost of SAGD simulations, it is impractical to use all realizations in the optimization workflow. So the first step of the workflow is to select a small set of realizations that represent the overall uncertainty of the reservoir. This is achieved by ranking all realizations according to the performance of each realization in terms of net present value (NPV) under the base operating conditions. Based on the ranking, a small set of representative realizations are chosen to represent the overall uncertainty of the reservoir. With the selected representative realizations, a robust optimization methodology is applied to account for the uncertainty of the reservoir model. In robust optimization, the decision-maker seeks an optimal risk weighted solution that is most likely to give good performance for any realization of the uncertainty in a given set. The robust optimization objective consists of two components: the expected value and standard deviation of NPV over the set of representative realizations. The weighting of the standard deviation term can be adjusted to reflect the risk tolerance of the decision-maker. Finally, the robust optimization problem is solved using an optimization algorithm assisted by second-order polynomial proxy models.

The robust optimization procedure is applied to a SAGD model with three well pairs and 100 realizations. The results are compared with the optimization results obtained from a single realization (nominal optimization). The comparison shows that the robust optimization workflow not only increases the average NPV but also increases all the NPV percentile values (P10, P20, …, P90). This indicates the increased robustness of the optimal solution under geological uncertainties and thus, adoption of the robust optimization procedure can significantly reduce the project risk.

You can access this article if you purchase or spend a download.