Assessment of Enhanced Oil Recovery in a Mature Field: Case Study of Statoil’s Gullfaks Field

Thesis Abstract

This study investigates the effectiveness and optimization of enhanced oil recovery (EOR) practices in a mature offshore field, focusing on Statoil’s Gullfaks field in the North Sea, with the aim of improving ultimate oil recovery while maintaining energy efficiency and economic viability. The problem addressed is the decline in reservoir productivity in mature fields and the need for integrated EOR strategies that reconcile reservoir engineering, geophysical monitoring, and economic incentives under complex subsurface conditions. The objectives are to evaluate current EOR methods applied at Gullfaks (CO2- and polymer-based flooding, thermal options where applicable), quantify incremental oil recovery from implemented EOR projects, identify operational constraints, and develop a decision-support framework for selecting cost-effective EOR pathways under uncertainty. The study hypothesizes that integrated reservoir management combining real-time reservoir surveillance, adaptive resource allocation, and robust uncertainty quantification yields superior recovery and reduced overshoot in operating costs compared with conventional cyclic or single-technology EOR approaches. The research adopts a mixed-methods design combining quantitative reservoir performance analysis with qualitative stakeholder insights. The population comprises offshore production data and project files from Gullfaks assets between 2010 and 2023, including operator logs, production history, and EOR pilot reports. A stratified random sample of 30 production wells and 6 EOR pilot projects is selected, supplemented by 12 expert interviews with reservoir engineers, operations managers, and geoscientists. Data collection instruments include production data extraction templates, GIS-based reservoir property compilations, stage-wise oil recovery estimates, and semi-structured interview guides validated by a pilot test. Instrument validity is ensured through triangulation of production data with sensor-logged reservoir pressure, temperature, and injectivity data; reliability is addressed via inter-rater coding checks for qualitative transcripts and test-retest of data extraction workflows. The analysis employs a multi-stage approach (i) time-series econometric methods, including autoregressive integrated moving average (ARIMA) models and regression analyses to quantify incremental recovery attributable to EOR phases; (ii) design of experiments and ANOVA to compare performance across EOR modalities and reservoir segments; (iii) reservoir simulation calibration using history-mmatching with CMG and ECLIPSE simulators to project alternative scenarios; (iv) uncertainty quantification through Monte Carlo simulation to assess risk-adjusted recovery and project economics; (v) thematic analysis of interview data to capture organizational and operational drivers of EOR success, with coding conducted in NVivo and cross-validated with stakeholders. Expected findings include quantified incremental oil recovery attributable to each EOR technology, projected field-wide ultimate recovery under different EOR deployment strategies, and identification of key bottlenecks such as injectivity limitations, sweep efficiency, and facility constraints. The study anticipates that performance gains correlate strongly with reservoir surveillance density, adaptive injection strategies, and integration of geophysical monitoring with real-time model updates, while recognizing diminishing returns beyond certain deployment thresholds. The contribution to knowledge lies in (a) delivering a field-specific, empirically calibrated framework for selecting and sequencing EOR options in mature offshore reservoirs, (b) extending the understanding of how organizational factors and asset integration influence EOR outcomes in the Gullfaks context, and (c) providing a transferable decision-support toolkit combining quantitative analytics with qualitative insights for similar mature fields. The main conclusion asserts that optimized, data-driven EOR portfolios, anchored in continuous reservoir learning and cross-disciplinary collaboration, can meaningfully extend Gullfaks’ productive life with favorable economics under volatile oil prices. Recommendations include establishing a centralized reservoir analytics hub for real-time data fusion, prioritizing pilot projects that maximize deliverability improvements in high-permeability streaks, reinforcing injectivity management practices, and incorporating risk-adjusted optimization in annual field development plans. Suggestions for further research involve exploring co-optimization of EOR and carbon capture initiatives, refining surrogate models for rapid scenario testing, and extending the framework to other Statoil offshore assets with analogous reservoir characteristics.