Continuous gas injection (CGI) in the conventional horizontal flooding patterns leads to severe gravity segregation and poor reservoir contact (sweep) volumes. To improve the sweep efficiency, the Water-Alternating-Gas (WAG) process has been widely practiced in the industry. However, the WAG process has not measured up to expectations, as evidenced by the low recoveries observed in several field cases.
The Gas-Assisted Gravity Drainage (GAGD) process is designed and practiced based on gravity drainage idea and uses the advantage of density difference between injected gas and reservoir oil to overcome the drawbacks of the WAG process.
In this study, a comparison of enhanced heavy-oil recovery by three methods (CGI, WAG, and GAGD) is conducted by a commercial numerical simulator in a sector model of an Iranian offshore field. For this purpose, different production scenarios are designed. Moreover, the effect of several parameters such as injection fluid (CO2, hydrocarbon gas), in situ oil viscosity (heavy or extremely heavy oil), rate and volume of injection, and also three different WAG ratios are studied.
The results show that the GAGD method is not suitable for this field as the thickness of pay zone is low for vertical sweep efficiency and causes early breakthrough of injected gas. Also, CGI leads to lower recoveries compared to the WAG process, due to its unfavorable mobility ratio. Injection of CO2 instead of hydrocarbon gas results in higher sweep efficiency because of its viscosity reduction and swelling effects. In case of extremely heavy-oil recovery, with decreasing well spacing, the oil recovery factor increases as the drainage radius of wells reduces substantially due to high-viscosity oil. 相似文献
Hybrid system is a potential tool to deal with nonlinear regression problems. The authors present an efficient prediction model for gas assisted gravity drainage injection recovery process based on artificial neural network (ANN) and dimensionless groups. Ant colony optimization (ACO) is applied to determine the network parameters. Results show that ACO optimization algorithm can obtain the optimal parameters of the ANN model with very high predictive accuracy. The predicted recovery from the ACO-ANN model, in comparison with other proposed models in literature, were in good agreement with those measured from simulations, and were comparable to those estimated from the other proposed models. 相似文献