Optimizing Enhanced Oil Recovery in Westland Petroleum's Mature Reservoirs

Thesis Abstract

Optimizing enhanced oil recovery (EOR) in mature reservoirs remains a critical challenge for Westland Petroleum, as declining production rates and increasing operational costs threaten long-term profitability. This study addresses the need to systematically evaluate and enhance current EOR techniques deployed within Westland's mature reservoirs, with the aim of maximizing hydrocarbon extraction while minimizing environmental impact. The specific objectives include assessing the effectiveness of current EOR methods, identifying operational and geological factors influencing recovery efficiency, developing an optimized EOR framework tailored to Westland's reservoir conditions, and proposing recommendations for improved implementation strategies. The research adopts a mixed-methods approach, integrating quantitative data analysis with qualitative insights to comprehensively address the complex variables influencing EOR outcomes. The study population comprises operational data from Westland Petroleum’s existing mature reservoirs, totaling records from 15 wellfields over a ten-year period (2012–2022), with a focus on 120 wells subjected to various EOR techniques such as polymer flooding, steam injection, and miscible gas injection. A stratified random sampling technique was employed to select 60 wells for detailed analysis, ensuring representation across different reservoir zones and EOR methods. Primary data sources include operational reports, reservoir simulation models, core sample analyses, and interviews with reservoir engineers and geologists. Data collection instruments consist of structured questionnaires, observational checklists, and digital logs, with validation through expert review. Quantitative data analysis involves descriptive statistics to characterize the operational parameters, followed by multiple regression analysis to examine the relationships between EOR techniques, reservoir properties, and recovery factors. Analytical frameworks employ the Reservoir Performance Model and Enhanced Oil Recovery Decision-Making Framework, grounded in the Theory of Oil Recovery Efficiency and the Reservoir Management Paradigm. The robustness of models is tested using Analysis of Variance (ANOVA) to assess significant differences in recovery performance across different strategies and reservoir conditions. Qualitative data are analyzed using thematic analysis to capture insights from expert interviews, enabling triangulation and contextual interpretation. Expected findings include identification of key operational, geological, and technological factors that significantly influence EOR success within Westland's reservoirs, and the development of an optimized EOR framework that integrates best practices, reservoir characteristics, and innovative techniques. It is anticipated that the study will reveal variability in recovery efficiencies linked to specific EOR methods under different reservoir conditions, highlighting areas where process modifications or technology upgrades could yield substantial improvements. Additionally, the research will demonstrate how adaptation of conventional models tailored to Westland’s geological context can enhance prediction accuracy and operational decision-making. This study contributes new empirical evidence on EOR optimization tailored to Westland’s specific reservoir challenges, filling a critical knowledge gap in the regional petroleum engineering literature. By integrating advanced simulation techniques, statistical analysis, and practical insights, it develops a customized EOR decision support framework that aligns with industry best practices and regional operational realities. The findings will inform reservoir management policies, guiding strategic investments in EOR technology and infrastructure upgrades while promoting sustainable exploration and production practices. The main conclusion underscores the importance of a tailored, data-driven approach to EOR, emphasizing continuous monitoring, reservoir characterization, and technological adaptation. Recommendations include establishing a reservoir-wide EOR monitoring program, investing in advanced simulation tools for real-time decision support, and fostering collaborative knowledge exchange among technical teams. The study advocates for further research into emerging EOR technologies such as nanotechnology-assisted recovery and microbial EOR, to sustain petroleum productivity in aging reservoirs amid evolving environmental regulations and market conditions.