Global sensitivity analysis of Alkali–Surfactant–Polymer enhanced oil recovery processes

After conventional waterflooding processes the residual oil in the reservoir remains as a discontinuous phase in the form of oil drops trapped by capillary forces and is likely to be around 70% of the original oil in place (OOIP). The EOR method so-called Alkaline–Surfactant–Polymer (ASP) flooding has been proved to be effective in reducing the oil residual saturation in laboratory experiments and field projects through reduction of interfacial tension and mobility ratio between oil and water phases. ical step for the optimal design and control of ASP recovery processes is to find the relative contributions of design variables such as, slug size and chemical concentrations, in the variability of given performance measures (e.g., net present value, cumulative oil recovery), considering a heterogeneous and multiphase petroleum reservoir (sensitivity analysis). usly reported works using reservoir numerical simulation have been limited to local sensitivity analyses because a global sensitivity analysis may require hundreds or even thousands of computationally expensive evaluations (field scale numerical simulations). To overcome this issue, a surrogate-based approach is suggested. ate-based analysis/optimization makes reference to the idea of constructing an alternative fast model (surrogate) from numerical simulation data and using it for analysis/optimization purposes. This paper presents an efficient global sensitivity approach based on Sobolʹs method and multiple surrogates (i.e., Polynomial Regression, Kriging, Radial Base Functions and a Weighed Adaptive Model), with the multiple surrogates used to address the uncertainty in the analysis derived from plausible alternative surrogate-modeling schemes. oposed approach was evaluated in the context of the global sensitivity analysis of a field scale Alkali–Surfactant–Polymer flooding process. The design variables and the performance measure in the ASP process were selected as slug size/concentration of chemical agents, and cumulative oil recovery, respectively. The results show the effectiveness and efficiency of the proposed approach since it allows establishing the relative contribution of the design variables (main factors and interactions) to the performance measure variability using a limited number of computationally expensive reservoir simulations.