Experimental, Numerical, and Soft Computing-Based Analysis of the Vapex Process in Heavy Oil Systems
There are significant heavy oil and bitumen resources in Canada. Considering increasing energy demands, these abundant resources are a potential energy source. Regardless, looking for an economically viable and environmentally friendly heavy oil recovery technique is essential for exploiting not just these resources, but all future heavy oil resources. The problems with highly viscous heavy oil reservoirs—excessive heat loss to the surrounding formations, low permeability carbonate reservoirs, and the large amount of CO2 emitted during these thermal processes—introduce economic and environmental drawbacks for thermal methods. In fact, solvent-based heavy oil recovery methods have recently gained attention due to the potential environmental and economic advantages over the thermal processes. In this research, an extensive experimental investigation was carried out to evaluate the effect of solvent type and drainage height, as the key parameters of VAPEX in heavy oil recovery. To accomplish this goal, two large, visual rectangular, sand-packed VAPEX models with 24.5 cm and 47.5 cm heights were employed to run the experiments using Plover Lake heavy oil (5650mPa.s) with a low permeability (6~9 D) sand pack. Propane, methane, CO2, butane, propane/CO2 mixture, and propane/methane mixture were considered as respective solvents for the experiments. Various parameters were monitored and recorded during the course of experiments. Moreover, separate experiments were carried out at the end of each VAPEX experiment to measure the asphaltene precipitation at different locations of the VAPEX models. To observe the drainage height effect in more detail, a comprehensive image analysis was completed during the solvent chamber evolution. As a result, it was determined that drainage height has a significant impact on production rate and heavy oil recovery. The results prove the complexity of the effect of drainage height and the up-scaling issues with the VAPEX process. Furthermore, in terms of solvents, propane showed the best recovery performance due to its favourable low vapour pressure and high solubility. Ultimately, promising recovery performance after introducing CO2 and methane as the carrier gases was observed. Separate experiments were conducted to obtain adequate PVT data for the heavy oil and solvents used in this study. A numerical simulation study was carried out to match experimental results and investigate the effect of well spacing, permeability, and diffusivity on the VAPEX process. Finally, the data gathered from the experiments were combined with available data in the literature and a soft computing approach was utilized to develop a model that predicts the recovery performance of the VAPEX process. Several experimental studies together with various analytical models have been proposed to simulate and describe the performance of the VAPEX process. However, due to the complexity of the mechanisms associated with the solvent injection process (i.e., diffusion and gravity drainage processes), such models are incapable of accurately predicting the production rate during the VAPEX process. In this research, artificial neural networks (ANN) technique was utilized to tackle the limitations that analytical methods encounter where there is uncertainty, and imprecision.