Resumen
Steam huff and puff injection is one of the thermal EOR methods in which steam is injected in a cyclical manner alternating with oil production. The cost and time inefficiency problem of reservoir simulation persists in the design of a steam huff and puff injection scheme. Building predictive proxy models is a suitable solution to deal with this issue. In this study, predictive models of the steam huff and puff injection method were developed using two machine learning algorithms, comprising conventional polynomial regression and an artificial neural network algorithm. Based on a one-well cylindrical synthetic reservoir model, 6043 experiment cases with 28 input parameter values were generated and simulated. Outputs from the results such as cumulative oil production, maximum oil production rate and oil rate at cycle end were extracted from each simulation case to build the predictive model. Reservoir properties that could change after an injection cycle were also modeled. The developed models were evaluated based on the fitting performance from the R-square value, the mean absolute error (MAE) value and the root mean square error (RMSE) value. Then, Sobol analysis was conducted to determine the significance of each parameter in the model. The results show that neural network models have better performance compared to the polynomial regression models. Neural network models have an average R-square value of over 0.9 and lower MAE and RMSE values than the polynomial regression model. The result of applying the Sobol analysis also indicates that initial reservoir water saturation and oil viscosity are the most important parameters for predicting reservoir production performance.