Utilizing deterministic smart tools to predict recovery factor performance of smart water injection in carbonate reservoirs

Smart water injection (SWI) is a practical enhanced oil recovery (EOR) technique that improves displacement efficiency on micro and macro scales by different physiochemical mechanisms. However, the development of a reliable smart tool to predict oil recovery factors is necessary to reduce the challenges related to experimental procedures. These challenges include the cost and complexity of experimental equipment and time-consuming experimental methods for obtaining the recovery factor (RF). In this paper, three predictive algorithms including adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and multigene genetic programming (MGGP) are developed to predict the RF of smart water flooding in carbonate reservoirs. Accordingly, 205 data points from coreflooding tests and 122 from Amott-cell tests were collected from previous studies. Porosity, permeability, oil viscosity, and oil density at reservoir temperature, injection rate, total dissolved solids (TDS), temperature, injection time, and initial water saturation (S_wi) were selected as the input parameters. Results show the great performance of ANN, compared to other employed algorithms. Coefficients of determination (R²) of ANN obtained from Amott-cell data for training, testing, validation, and overall data are 0.9748, 0.9021, 0.9765, and 0.9646, respectively. The corresponding values from coreflooding data are 0.9502, 0.9582, 0.9837, and 0.9523, respectively. Moreover, parametric sensitivity analysis was performed for the input parameters. Based on this analysis, time and injection rate have the most positive impact on the Amott-cell and coreflooding, respectively. Sensitivity analysis from Amott-cell data introduces TDS and oil viscosity have the most negative effects on RF performance. Furthermore, the most negative effects belong to porosity and permeability for coreflooding experiments.