A new computational approach to predict hot-water flooding (HWF) performance in unconsolidated heavy oil reservoirs

Abstract

In this study, a new computational approach for predicting the performance of hot-water flooding in unconsolidated heavy oil reservoirs is presented. The proposed model predicts the changes in the oil–water viscosity ratio (μo/μw) by estimating the reservoir temperature distribution through porous media. Then, the dimensionless and normalized variables were redefined to forecast water fractional flow as a function of temperature and water saturation. Moreover, the proposed approach predicts the cumulative heavy oil production and recovery factor more accurately and with less required input data and runtime compared to commercial simulators such as CMG-STARS (computer modeling group), etc.Estimated results were validated using laboratory experimental data and numerical simulation outputs. A total of 4 core-flooding experiments were conducted at injection temperatures of 20–80 °C. Numerical simulation models based on the laboratory HWF tests were developed in CMG-STARS. The water–oil relative permeability curves were tuned to history match simulation results with those obtained from experiments.Finally, the predicted cumulative heavy oil productions, recovery factors (RF) and production rates by the proposed approach were compared to the results from the laboratory sandpacked tests and simulation runs for three scenarios. The relative errors between heavy oil recovery factors obtained from computational approach and experimental data were measured to be 11.01%, 14.51% and 13.69% for injection temperatures of 40, 60 and 80 °C, respectively.