Polymer flooding is a technically and economically proven enhanced oil recovery method. The serious drawbacks of polymer flooding are the high injection pressure with associated pumping cost and potential mechanical degradation of the polymer due to high shear near wellbore. Recently, the use of partially HPAM polymer under controlled pH conditions has been proposed to overcome these problems. Since the polymer viscosity changes by several orders-of-magnitude with pH change, estimating the viscosity in terms of pH, salinity, polymer concentration and molecular weight, degree of hydrolysis, and shear rate, is difficult. Our rheological model, based on an artificial neural network (ANN) that is trained with available laboratory data, predicts the above dependencies more accurately than the existing Huh-Choi-Sharma (HCS) model. The HCS model has difficulty predicting polymer viscosity accurately when salinity is high. Its viscosity computation also requires solving a non-linear equation, so that its use in reservoir simulators will require sizeable computation time. The ANN-based rheological model consists of four ANN layers - one input layer, one output layer, and two hidden layers. The ANN-based model, which has only 6 model parameters, can estimate polymer viscosity more accurately and quickly than the HCS model with 21 model parameters.


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