Assessment of Enhanced Oil Recovery Techniques in Heterogeneous Reservoirs

Thesis Abstract

The effective recovery of oil from heterogeneous reservoirs remains a significant challenge in petroleum engineering, owing to the complex and varying geological properties that influence fluid flow and sweep efficiency. This study seeks to comprehensively assess the performance of various enhanced oil recovery (EOR) techniques—namely chemical flooding, thermal methods, and gas injection—in heterogeneous reservoir conditions, with the aim of identifying the most suitable approaches for maximizing hydrocarbons extraction while minimizing operational costs and environmental impacts. The specific objectives include evaluating the effectiveness of these EOR methods through numerical simulation, analyzing the influence of reservoir heterogeneity on recovery efficiency, and developing a decision framework for selecting optimal EOR strategies in heterogeneous formations. The research adopts a quantitative methodology, comprising a mixed-methods approach that integrates numerical simulation using Eclipse EOR software with field data analysis. The primary population for the study consists of data from twenty-five oil fields exhibiting varying degrees of heterogeneity, selected from a regional oil company’s operational database. A purposive sampling technique is applied to streamline the selection of reservoirs with comprehensive core, well-log, and production data. Data collection instruments include well logs, core samples, production history records, and laboratory experimental results. These data are corroborated with field-scale experimental data and previous studies to ensure robustness. The validity and reliability of the simulation tools are confirmed through calibration with historical production data and sensitivity analyses, employing techniques such as parameter tuning and statistical validation via analysis of variance (ANOVA). Data analysis involves the application of advanced numerical modeling to simulate oil recovery under different EOR scenarios across various heterogeneity cases, with a particular focus on permeability contrast, reservoir layering, and fluid properties. Regression analysis and response surface methodology (RSM) are employed to quantify the effect of heterogeneity parameters on recovery efficiency and residual oil saturation. To further interpret the influence of reservoir heterogeneity on EOR performance, the study utilizes geostatistical techniques and uncertainty quantification through Monte Carlo simulations. A comparative performance assessment pinpoints the conditions under which each EOR method delivers optimal results, validated against field case studies where available. The expected findings suggest that chemical flooding—particularly polymer injection—provides substantial increments in oil recovery in moderate-to-high heterogeneity reservoirs due to improved mobility control. Thermal methods, such as steam flooding, are anticipated to demonstrate increased effectiveness in heavy oil reservoirs with relatively uniform permeability, while gas injection techniques like CO2 flooding are predicted to perform optimally in reservoirs with significant permeability contrasts and low residual oil saturation. The study also hypothesizes that reservoir heterogeneity significantly influences EOR efficiency, with specific thresholds identified for transition points where one technique surpasses others in effectiveness. This research contributes novel insights into the interaction between reservoir heterogeneity and EOR effectiveness, enriching current theoretical models with empirical data specific to regional heterogeneity profiles. It advances the application of integrated numerical simulations and statistical analyses in optimizing EOR strategies, providing a decision-support framework for reservoir engineers and field managers. Ultimately, the study offers a set of practical guidelines and model-based tools for selecting suitable EOR methods tailored to heterogeneous reservoir characteristics, thereby enhancing oil recovery while reducing environmental footprint and operational costs. The main conclusion emphasizes that tailored EOR strategies, driven by detailed reservoir heterogeneity assessments and robust modeling, are vital for maximizing recovery in challenging reservoirs. The study recommends adopting adaptive EOR implementation guided by geostatistical and simulation-based evaluations, alongside further research into the synergistic effects of combined EOR techniques in heterogeneous settings. Future investigations should explore real-time monitoring and adaptive control of EOR processes employing machine learning algorithms for dynamic reservoir management.