Chemical enhanced oil recovery (EOR) method includes the injection of a mixture of chemicals to improve sweep efficiency and produce residual oil saturation left behind in the swept volume after waterfloods. The way chemical flood works is by the synergistic effect of polymer to improve mobility (slow down the movement of water to match that of the oil, i.e., mobility ratio of 1) and surfactant (with or without alkaline) to reduce the interfacial tension between oil and water to displace the discontinuous trapped oil remained in reservoir after the waterflood. Surfactant flooding uses the same concept as using detergent/soap to remove oil. The primary objective of surfactant/polymer (SP) or alkaline/surfactant/polymer (ASP) flooding is to reduce the interfacial tension between oil and water to values on the order of 0.001 dyne/cm or less in order to displace the trapped oil from rock pores.

We present a novel method to predict the results of the chemical EOR processes using an analytical and robust forecasting model. This fast and simple analytical approach will aid to screen chemical EOR projects where numerical simulations are not feasible due to either lack of data or the time and expertise required conducting such studies.

The proposed forecasting model is based on a general approach for predicting the performance of isothermal EOR processes. The model incorporates fundamental principles of material balance, segregated flow and fractional flux while with effects of mobility ratio, chemical slug size, miscibility and reservoir heterogeneity. The permeability heterogeneity is modeled using Koval fractional flux model with its applicability to wide range of permeability and degree of heterogeneity. The forecasting tool has been successfully validated against numerous SP/ASP field and numerical simulation results.

We also developed correlations to describe the model variables as functions of mobility ratio, slug size and reservoir heterogeneity based on the experimental design methodology and numerical simulation. Successful history matching against numerical simulation results and the strength of the developed correlations proved the ability of the model to predict SP/ASP results when field results are not available.

The forecasting tool provides key results to assess the flood performance. These include average oil saturation, cumulative oil recovery, oil cut, oil production rate, recovery efficiency and volumetric sweep efficiency as functions of time. An estimate of volumetric sweep efficiency helps in a better design and increases the chance of successful field projects.

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