Robust Optimization of ASP Flooding Under Oil Price Uncertainty

Abstract

Abstract This paper presents a novel workflow to optimize Alkaline, Surfactant, and Polymer (ASP) flooding under oil price uncertainty. ASP flooding is a relatively expensive recovery process and the optimization approach must be able to account for the change in oil price to provide realistic economical predictions. A workflow, combining time-event optimization with Robust Optimization has been developed and applied to the optimization of the NPV of the ASP process under uncertain time varying oil prices. Several forecasts of the future oil price from different energy organizations and financial institutions were used to account for the uncertainty of the oil price in the NPV calculations. The proposed workflow introduces the parameterization of time-dependent events to identify the best time to start the ASP flooding project after the secondary water flooding, and to determine the size and composition of different chemical slugs. The Robust optimization workflow is compared to the Nominal Optimization workflow, which is carried out using the average oil price forecase to calculate NPV. Results show that Robust Optimization increases the project Net Present Value (NPV) by over 18% compared with Nominal Optimization. Robust Optimization generates optimal ASP design with desirable performance under different oil price forecast scenarios. In addition to ASP flooding, the Robust Optimization workflow can be efficiently applied to other recovery processes for both conventional and unconventional reservoirs.