An Optimization Methodology of Alkaline-Surfactant-Polymer Flooding Processes Using Field Scale Numerical Simulation and Multiple Surrogates

Abstract

Abstract After conventional waterflood 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. A critical step to make ASP floodings more effective is to find the optimal values of design variables that will maximize a given performance measure (e.g. net present value, cumulative oil recovery) considering a heterogeneous and multiphase petroleum reservoir. Previously reported works using reservoir numerical simulation have been limited to sensitivity analyses at core and field scale levels because the formal optimization problem includes computationally expensive objective function evaluations (field scale numerical simulation). The proposed methodology estimates the optimal values for a set of design variables (slug size and concentration of the chemical agents) to maximize the cumulative oil recovery from a heterogeneous and multiphase petroleum reservoir subject to an ASP flooding. The surrogate-based optimization approach has been shown to be useful in the optimization of computationally expensive simulation-based models in the aerospace, automotive, and oil industries. In this work we have extended this idea along two directions:using multiple surrogates for optimization, andincorporating an adaptive weighted average model of the individual surrogates. The proposed approach involves the coupled execution of a global optimization algorithm and fast surrogates (i.e.. based on Polynomial Regression, Kriging, and a Weighted Average Model) constructed from field scale numerical simulation data. The global optimization program implement the DIRECT algorithm and the reservoir numerical simulations are conducted using the UTCHEM program from the University of Texas at Austin. The effectiveness and efficiency of the proposed methodology is demonstrated using a well-known field scale case study. Introduction After conventional waterflood 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)1. 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. Some ASP pilot tests reported in the literature have reached an oil recovery over 60% OOIP2–5. In ASP floodings the surfactant is responsible for reducing the interfacial tension between oil and water phases to a level that promotes the mobilization of trapped oil drops. The alkaline agent is intended to react with the acids to generate in situ surfactant to attain ultralow tension6 and to overcome the surfactant depletion in the liquid phases due to retention. The role of the polymer is to increase the viscosity, reducing the mobility ratio and hence reaching a greater volumetric swept efficiency. Details of the physical processes taking place can be found in Shah and Schechter7. The design of an ASP flooding process must achieve three main objectives: propagation of the chemicals in an active mode, the injection of enough chemicals accounting for the retention, and a complete swept of the area of interest8. Achieving these objectives is significantly affected by the selection of the chemicals, the concentration of the ASP solution and the slug size, among other factors.