Mehdi Mohammadpoor’s research while affiliated with University of Regina and other places

As the price of oil decreases, it is becoming increasingly important for oil companies to operate in the most cost-effective manner. This problem is especially apparent in Western Canada, where most oil production is dependent on costly enhanced oil recovery (EOR) techniques such as steam-assisted gravity drainage (SAGD). Therefore, the goal of this study is to create an artificial neural network (ANN) that is capable of accurately predicting the ultimate recovery factor of oil reservoirs by steam-assisted gravity drainage (SAGD). The developed ANN model featured over 250 unique entries for oil viscosity, steam injection rate, horizontal permeability, permeability ratio, porosity, reservoir thickness, and steam injection pressure collected from literature. The collected data set was entered through a feed-forward back-propagation neural network to train, validate, and test the model to predict the recovery factor of SAGD method as accurate as possible. Results from this study revealed that the neural network was able to accurately predict recovery factors of selected projects with less than 10% error. When the neural network was exposed to a new simulation data set of 64 points, the predictions were found to have an accuracy of 82% as measured by linear regression. Finally, the feasibility of ANN to predict the recovery performance of one of the most complicated enhanced heavy oil recovery techniques with reasonable accuracy was confirmed.

This paper reviews the utilization of Big Data analytics, as an emerging trend, in the upstream and downstream oil and gas industry. Big Data or Big Data analytics refers to a new technology which can be employed to handle large datasets which include six main characteristics of volume, variety, velocity, veracity, value, and complexity. With the recent advent of data recording sensors in exploration, drilling, and production operations, oil and gas industry has become a massive data intensive industry. Analyzing seismic and micro-seismic data, improving reservoir characterization and simulation, reducing drilling time and increasing drilling safety, optimization of the performance of production pumps, improved petrochemical asset management, improved shipping and transportation, and improved occupational safety are among some of the applications of Big Data in oil and gas industry. Although the oil and gas industry has become more interested in utilizing Big Data analytics recently, but, there are still challenges mainly due to lack of business support and awareness about the Big Data within the industry. Furthermore, quality of the data and understanding the complexity of the problem are also among the challenging parameters facing the application of Big Data.

There are vast resources of heavy oil and bitumen reservoirs in Western Canadian Basin. For many of them up to 95 % of reserves still remain in place, and by considering the increase in future energy demand these abundant resources can be considered as potential sources for future years. Recently, solvent-based heavy oil recovery methods such as vapour extraction (VAPEX) have gained attention due to the potential environmental and economic assets over thermal processes. 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 study, the artificial neural networks (ANN) technique is utilized to tackle the limitations that analytical methods encounter while predicting the complex relationships, where there is uncertainty, imprecision, and partial truth. Hence, in the first phase of the research a comprehensive experimental study in two large-scale, visual rectangular VAPEX models was carried out by utilizing various injection solvents. Based on an extensive literature review and experimental results, the drainage height, solvent type, permeability, porosity, and heavy oil viscosity were considered as the inputs of the model to predict the heavy oil production rate as the output of the model. After trying different training scenarios, it was found that the back-propagation learning algorithm can be successfully used to predict the ultimate recovery factor after implementing the VAPEX method in the heavy oil system of interest. This article is protected by copyright. All rights reserved

There are significant heavy oil and bitumen resources in Canada. Global energy demand is rising while environmental constraints make heavy oil recovery more challenging. Therefore, looking for an economically viable and environmentally-friendly heavy oil recovery technique is essential. Recently, solvent-based heavy oil recovery techniques (i.e., VAPEX) have attracted attention due to their economic and environmental advantages over thermal methods. In this research, an extensive experimental and numerical simulation study on the VAPEX technique was carried out to provide more in-depth information about key parameters which affect the ultimate performance of the VAPEX process. For this purpose, VAPEX experiments were conducted in two large-scale physical models and various solvents were utilized. PVT experiments were also carried out, and CMG's STARSTM was used for numerical simulation studies and to history-match the experimental results. Image analysis of the VAPEX chamber evolution showed that the highest sweep efficiency was observed after injecting propane, followed by butane, a propane/carbon dioxide mixture, a propane/methane mixture, carbon dioxide, and methane. The experiments were simulated numerically, and satisfactory history-matching results were achieved. The major difference between the experimental and simulation results was observed after the first breakthrough of the solvent. In addition, the results showed that injection and production well configurations significantly affected the recovery performance of the process. A longer distance between the injection and production wells alongside the drainage height will increase the production rate in VAPEX. This article is protected by copyright. All rights reserved

The low cost of the injected solvent, which can be also recovered and recycled, and the applicability of VAPEX technique in thin reservoirs are among the main advantages of VAPEX process compared to thermal heavy oil recovery techniques. In this research, an extensive experimental investigation is carried out to first evaluate the technical feasibility of utilization of various solvents for VAPEX process. Then the effect of drainage height on the stabilized drainage rate in VAPEX process was studied by conducting series of experiments in two large-scale 2D VAPEX models of 24.5 cm and 47.5 cm heights. Both models were packed with low permeability Ottawa sand (#530) and saturated with a heavy oil sample from Saskatchewan heavy oil reservoirs with viscosity of 5650 mPa s. Propane, butane, methane, carbon dioxide, propane/carbon dioxide (70%/30%) and propane/methane (70%/30%) were considered as respective solvents for the experiments, and a total of twelve VAPEX tests were carried out. Moreover, separate experiments were carried out at the end of each VAPEX experiment to measure the asphaltene precipitation at various locations of the VAPEX models. It was found that injecting propane would result in the highest drainage rate and oil recovery factor. Further analysis of results showed stabilized drainage rate significantly increased in the larger physical model.

Prior to the flooding tests, interfacial tension measurements between the oil and brine samples were performed for different type of oils and 5,000-ppm surfactant solution. For the case of the nonionic surfactant-black oil system, the system was best described as a case of mixed wettability. For the anionic surfactant-black oil system, there was an increase in enhanced oil recovery as surfactant concentration increased. For the anionic surfactant-tetradecane system, again the presence of emulsion for concentrations greater than 500 ppm limited the analysis. Nevertheless, for concentrations of 1,500 ppm or less the application was efficient, resulting in enhanced oil recovery of approximately 5%.

Although some studies are performed and a series of analytical models are developed, it has been found that current analytical models are not accurately predicting the production rate by VAPEX. This is largely because the diffusion used in the current model is based on a single fluid property (oil viscosity only) while other properties such as permeability, porosity and cementation factor carry large amount of uncertainties. The main objective of this study was to use the experimental data obtained from two-dimensional (2D) and three-dimensional (3D) physical models and develop a new and more accurate correlation for diffusivity that takes into account fluid and reservoir properties based on Butler's equation. In order to develop such an accurate correlation, various parameters such as porosity, cementation factor, permeability, and viscosity were included to history match the experimental data and tune the diffusivity correlation. Next the validity of the correlation was proved using 3D experimental data and data obtained from a field pilot test. It has been observed that diffusion rate is not only a function of viscosity but also it is a function of permeability, porosity, and cementation factor.

Drawbacks of the thermal recovery techniques such as excessive heat loss to the surrounding formations and carbon dioxide emissions during these processes have directed the interests of researchers towards more viable alternatives such as solvent-based recovery techniques (e.g. VAPEX). One of the key parameters to implement a successful VAPEX process is to control the profiles of vapour chamber and consequently improve the areal sweep efficiency. In this regard, an optimum well configuration and well connectivity establishment between the injection and production wells are desirable. The main focus of this research is to extensively conduct series of experiments to investigate the effect of injection/production wells connectivity on the performance of VAPEX process. For this purpose, two large-scale physical models were employed. Propane and propane/carbon dioxide mixtures were selected as the injection solvents in the visual sand-packed physical models saturated with heavy oil sample from Saskatchewan (Canada) heavy oil. Various injection/production scenarios were followed and it was found that the initial connection path between the injector and producer had a significant impact on the vapour chamber profiles and consequently on the ultimate recovery performance of the VAPEX process.

In this research, an extensive experimental investigation is carried out to evaluate the utilization of CO2 as an injection solvent for VAPEX process. 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 (5650 cP) with a low permeability (6∼9 D) sand pack. Propane, CO2 and propane/CO2 mixture were considered as respective solvents for the experiments, and a total of 6 tests were carried out. The heavy oil production rate and the produced gas-oil ratio were measured periodically. Moreover, separate experiments were carried out at the end of each VAPEX experiment to measure the asphaltene precipitation at various locations of the VAPEX models. To observe the drainage height effect in more details, a comprehensive image analysis was performed 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 highest recovery factor of 75% of original oil in place due to its favourable low vapour pressure and high solubility. Ultimately, the promising recovery performance after introducing CO2 as a carrier gas was observed, the recovery factor of 55% of original oil in place was achieved when the mixture of propane/CO2 was used as the solvent. After conducting asphaltene measurement tests, it was observed that more asphaltene precipitation occurred close to the injection points and at the oil/solvent interface. Furthermore, the image analysis revealed that the highest sweep efficiency was observed to be 0.86 after injecting propane, followed by propane/CO2 mixture, and pure CO2.

The problems associated 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 thermal processes have made solventbased heavy oil recovery methods more attractive than thermal methods. In this study, an extensive experimental investigation was carried out to evaluate the effect of solvent type and drainage height, as the key parameters in vapor extraction, on asphaltene precipitation. Two large visual rectangular sand-packed physical models with heights of 24 and 47 cm were employed to conduct the experimental studies. Propane, methane, and a propane/CO2 mixture were considered as the respective solvents in the experiments. Also, separate experiments were carried out to measure the asphaltene precipitation at different locations in the models. The results show that for almost all of the different solvents used in this study more asphaltene precipitation was observed close to the injection points and at the oil/solvent interface. Comparing the textures of the asphaltene precipitants from different locations in the models, it was found that the precipitants close to the injection points were more brittle, while the precipitants close to the production points were more ductile. After comparing the asphaltene precipitation in the small and large models when various solvents were used, it was observed that in the case of propane injection more asphaltene precipitation was observed at different locations in the physical models.