Heavy oil recovery by combined solvent and hot water (CS-HW) injection: Experimental, numerical and data mining-based analysis
In this study, a hybrid EOR process is developed and optimized in a two-well configuration for heavy-oil recovery which combines solvent injection using different solvents such as carbon dioxide (CO2), methane (CH4) and propane (C3H8) with a moderate reservoir heating by hot-water flooding (HWF) as a solution to enhanced heavy oil recovery (EHOR), reduce energy consumption, and improved solvent retrieval efficiency. The combined injection of solvent and hot water offers several advantages including reduction of energy consumption compared to steam-based thermal EOR methods, and reduction of solvent volume required to reduce the viscosity of heavy oil. The proposed hybrid EOR method of combined solvent and hot water (CS-HW) injection outperformed the sole injection of solvent, conventional water flooding (WF) and hot-water flooding (HWF) by sustaining the foamy oil flow and effectively delaying water and gas breakthrough times. A total of 21 laboratory tests including water flooding (WF), hot-water flooding (HWF), solvent injection, combined solvent and hot water (CS-HW) injection in a two-well configuration were designed and conducted. More specifically, the design parameters such as injection rate (qinj) and temperature (Tinj), solvent composition and slug size were optimized with the objective of maximizing heavy oil recovery. In order to study the numerical simulation of CS-HW injection and other laboratory tests presented in this research, CMG-STARS module was used. Sensitivity analysis was performed on the effective parameters of CS-HW injection process to obtain the best history-match between the experimental data and the simulation model. Furthermore, a new computational approach for predicting the performance of hot-water flooding (HWF) in unconsolidated heavy oil reservoirs was presented. The proposed model predicts the changes in the oil–water viscosity ratio (μo/μw) by estimating the reservoir temperature distribution through porous media. Then, the dimensionless and normalized variables were redefined to forecast water fractional flow as a function of temperature and water saturation. Moreover, the proposed approach predicts the cumulative heavy oil production and recovery factor more accurately and with less required input data and runtime compared to CMG-STARS (computer modeling group), etc. Estimated results were validated using laboratory experimental data and numerical simulation outputs. The relative errors between oil recovery factors obtained from computational approach and experimental data were measured to be about 5.3%, 1.7% and 1.2% and 2.5% for injection temperatures of 40, 60, 80 and 100 °C, respectively. Finally, this thesis provides a novel data mining-based analysis by using artificial neural network (ANN) methodology to develop a high-performance neural simulation tool for predicting the efficiency of CS-HW injection process. In order to train, test and validate the model the experimental and simulation data obtained in this study together with available data in the literature were fed to the machine learning technique to develop a CSHW recovery performance predictive model. The proposed intelligent predictive model is expected to help petroleum engineers as an alternative model to predict the efficiency of CS-HW injection process, where other models have limitations and their input parameters are often not easily accessible.
Networked Digital Library of Theses and Dissertations
Faculty of Graduate Studies and Research, University of Regina