Comparative Analysis of Enhanced Oil Recovery Techniques in Conventional and Unconventional Reservoirs

Thesis Abstract

This study addresses the critical need for optimizing oil recovery processes within the context of evolving reservoir complexities by conducting a comparative analysis of enhanced oil recovery (EOR) techniques in conventional and unconventional reservoirs. The persistent decline in global oil reserves and increasing operational challenges necessitate the identification of more effective and tailored EOR methods that maximize recovery efficiency while minimizing economic and environmental costs. The primary aim of this research is to evaluate and compare the performance, efficiency, and suitability of various EOR techniques—namely chemical flooding, thermal methods, gas injection, and microbial EOR—in both conventional reservoirs, characterized by porous sandstone and carbonate formations, and unconventional reservoirs, notably shale and tight sandstone formations. Specific objectives include (1) to examine the mechanisms and efficiency of selected EOR methods in different reservoir types, (2) to assess the influence of reservoir properties such as porosity, permeability, and wettability on EOR performance, (3) to develop a comparative framework for evaluating economic and environmental impacts associated with each technique, and (4) to identify optimal EOR strategies tailored to each reservoir type based on performance metrics. The methodology adopted involves a mixed-methods research design integrating quantitative and qualitative analytical approaches. The study population comprises data from 20 oil fields—10 conventional and 10 unconventional—selected through stratified random sampling from a regional oil basin with comprehensive operational data available from industry partners. Data collection instruments include laboratory core flooding experiments, field operational data obtained through collaboration with oil companies, and semi-structured interviews with reservoir engineers and geoscientists. Data from laboratory tests will be analyzed using multivariate regression analysis to establish correlations between reservoir properties and EOR efficiency, while field data will be subjected to analysis of variance (ANOVA) to delineate significant differences in recovery factors between reservoir types and techniques. Thematic analysis will be employed to interpret qualitative insights from interviews, providing contextual understanding of operational challenges and innovative practices. The study also proposes a comparative framework based on the Theory of Enhanced Oil Recovery Optimization and the Reservoir Engineering Equilibrium Model to interpret the interaction of physical, chemical, and biological processes influencing EOR performance. Advanced analytical tools such as the Response Surface Methodology (RSM) will be utilized to optimize injection parameters for different techniques, and cost-benefit analysis will assess economic viability. Expected findings include a delineation of the differential effectiveness of EOR techniques across conventional and unconventional reservoirs, with chemical flooding and thermal methods demonstrating higher efficiencies in conventional formations, while microbial and gas injection techniques show potential in unconventional settings. The study also anticipates revealing critical reservoir properties that influence EOR success, aiding in the development of tailored recovery strategies. The results will contribute to existing literature by providing empirical evidence and a decision-making framework for selecting suitable EOR methods based on reservoir characteristics. The main conclusion underscores that effective EOR implementation requires a nuanced understanding of reservoir-specific factors and the operational synergy of technique properties. Recommendations include adopting hybrid EOR approaches in complex formations, integrating reservoir diagnostics with real-time monitoring, and developing tailored operational guidelines. Future research directions suggested include the application of machine learning algorithms for predictive modeling and the exploration of environmentally sustainable EOR fluids. Overall, this research aims to bridge the knowledge gap in EOR applicability to unconventional reservoirs, thereby advancing recovery technology, optimizing resource utilization, and promoting sustainable oil production practices in the evolving upstream oil industry.