Automatic History Match and Upscaling Study of Vapex Process and Its Uncertainty Analysis

Abstract

Vapour Extraction (VAPEX) is a process to recover heavy oil by injecting vapourized solvent into a reservoir. In order to ensure successful commercial application of a VAPEX process, reliable prediction of VAPEX performance is crucial. The current practice for VAPEX performance prediction is using analytical scale-up methods that translate laboratory results to field applications with analytical models. However, the drawbacks associated with the analytical scale-up methods are that they only consider single phase flow and gravity drainage and cannot take reservoir heterogeneity into account, which limits the applicability to real field cases. In this work, an effort was made to investigate the capability of predicting up-scaled VAPEX performance through numerical simulation. In this study, numerical simulation was conducted to up-scale 2-D VAPEX tests and to predict 3-D VAPEX performance. The 2-D VAPEX test was conducted under conditions very close to those for the 3-D test which is done by the Saskatchewan Research Council (SRC). In each test, the initial waterflooding was conducted prior to the subsequent solvent injection. Then, a numerical model was established to simulate the 2- D test. History match of the 2-D test was conducted by tuning the uncertainties, such as the relative permeability, capillary pressure, solubility, and the wall effect in sandpacking. Afterwards, the tuned parameters were applied to predict the 3-D test performance. Through comparison of the predicted and experimental results in the 3-D test, the capability of predicting an up-scaled VAPEX process through numerical simulation was examined, and the differences between the physical and numerical modeling were identified. As history matching of VAPEX experiments is a complex, highly nonlinear, and nonunique inverse problem, a modified genetic algorithm (GA) was developed to assist with the history matching process. A population manipulation database and artificial neural network (ANN) were incorporated with GA to enhance the computational efficiency and enlarge the search range. Compared to conventional GA, the computational time in this modified GA approach was reduced by 71%, and an excellent match between the simulation data and experimental data was achieved. The upscaling study results show that the numerical method used, compared to analytical models, has greater potential to be used as a scale-up method because of the improved prediction results. Due to the nature of the numerical simulation method, it is difficult to match the early stage of the solvent injection process, which results in the great uncertainties. It was demonstrated that the waterflooding performance can be successfully predicted, whereas the uncertainty in scaling up the VAPEX process is great. In the waterflooding period, the predicted oil recovery factor was 25.8% compared with 23.4% in the 3-D test. In the VAPEX process, the difference between the predicted and measured oil recovery factors was in the range of 0.8–25.1%, depending on the different combination of uncertain parameters. This fact indicates that more work on this topic is required to reduce the uncertainties in predicting the field-scale VAPEX performance, and it would be especially valuable for field applications.