Abstract
Prediction of the performance of oil wells under Cyclic Steam Stimulation (CSS) is challenging in complex and heterogeneous reservoirs, especially with limited data. Analytical and numerical simulation models do not usually give accurate predictions in such conditions. In this work, a semi-analytical model was developed to determine consistent mathematical relationships between the injected steam and some of the effective oil production parameters for more accurate prediction of oil production rates. Field investigation indicates that the change of the Cumulative Oil to Steam Ratio (COSR) to production days is related to a group of effective oil production parameters. This group of parameters includes the cumulative injected steam relative to the drainage volume, the oil net pay thickness relative to the gross pay thickness, and the vertical permeability relative to the thermal diffusivity. These parameters were arranged in two dimensionless groups. It was found that plotting these two dimensionless groups on Log–Log scale for any reservoir yields a straight line (correlation). For any reservoir under CSS, measurements of two steam cycles are sufficient to identify the constants of the proposed correlation. This method has been applied and validated on six reservoirs with different reservoir characterizations. Six different wells with a total of 43 steam cycles from these reservoirs were analyzed with the same approach. The mathematical relationships of the dimensionless groups were calculated, and the Log–Log plot was constructed for each well using the data of the first two cycles. Then, the proposed correlation was developed for each well and used to predict the well performance starting from the third steam cycle. At the end, the predicted performance of each well was compared with the corresponding actual measurements. The results showed that the average absolute percentage deviation between the actual and the predicted cumulative oil production through the well lifetime is less than 5% for the six wells. In addition, the absolute instantaneous deviation between the actual and the predicted cumulative oil production for each individual cycle in all cases is (1) less than 15% for about 42% of the tested CSS cycles, (2) between 15 to 25% for about 39% of the tested CSS cycles, and (3) higher than 25% for about 19% of the tested CSS cycles. This work is considered an original contribution to develop dimensionless relationships that can be used to predict the oil production of the CSS operations for reservoirs with limited data. The required data are the historical production rate, steam injection rate, and basic petrophysical parameters.
Highlights
Conventional oil resources are estimated at only 30% of the total worldwide oil resources, while all other viscous oil including heavy oil, extra heavy oil, bitumen, and oil sand make up the remaining 70% (Finbow and Koop 1978)
The Cyclic Steam Stimulation (CSS) has proved its effectiveness as a thermal enhanced oil recovery method in several heavy oil reservoirs with different reservoir characteristics (Basta 2020; Failed 2013)
These comparisons were performed through the well lifetime per each steam cycle
Summary
Conventional oil resources are estimated at only 30% of the total worldwide oil resources, while all other viscous oil including heavy oil, extra heavy oil, bitumen, and oil sand make up the remaining 70% (Finbow and Koop 1978). CSS oil production predictive tools are essential requirements to manage reservoirs under CSS (Yang et al 2014). There are three categories of methods of CSS oil production prediction: analytical models, numerical simulation, or analogy methods (Abou-Kassem 1996). The analytical predictive models use different assumptions to simplify the calculations of oil production prediction. The assumptions affect the accuracy of the analytical models (Yang et al 2014). Several predictive models have been developed for different conditions and assumptions. Several other analytical models were developed to take the effects of the depleted reservoirs and gravity derange into consideration (Ali and Tamin 1998; Alvarez and Sungyun 2013; Boberg and Lantz 1966)
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