Integrated Waterflood Optimization for Sakina Oilfield, Nigeria: A Case Study

Thesis Abstract

Integrated Waterflood Optimization for Sakina Oilfield, Nigeria A Case Study The Sakina oilfield in Niger Delta Nigeria faces declining production due to reservoir heterogeneity, high water cuts, and limited sweep efficiency in conventional waterflood operations. This study addresses the critical need for an integrated waterflood optimization framework that couples reservoir characterization, buoyancy-driven sweep improvements, and real-time process control to enhance oil recovery while reducing water handling costs. The aim is to develop and validate a robust workflow that delivers sustained incremental oil recovery through optimized injection strategies, surfactant/polymer (SP) enhancement where applicable, and adaptive surveillance of reservoir performance. Specific objectives include (i) delineating reservoir heterogeneity and connectivity using high-resolution geological modelling and dynamic data integration; (ii) evaluating multiple injection scenarios through history-mmatched reservoir simulation to identify optimal waterflood configurations; (iii) implementing a real-time surveillance and optimization loop leveraging production data analytics and machine learning for adaptive control; (iv) quantifying incremental oil recovery, water cut reduction, and associated economic metrics under uncertainty; and (v) providing a scalable framework transferable to similar fields in shallow offshore and onshore Niger Delta formations. A mixed-methods methodology is employed. The population comprises 1,200 well observations, 350 reservoir facies samples, and 60 production/injection wells within Sakina. A stratified random sample of 180 production/injection wells is selected for dynamic data analysis, while 30 representative core samples from 10 zones inform relative permeability and capillary pressure correlations. Data collection instruments include live production telemetry, downhole pressure/temperature gauges, core plug analysis, wireline log suites, and monthly field reports. Reservoir simulation uses a history-matched, three-dimensional dual-porosity, dual-permeability model calibrated against 24 months of production data and nine transient pressure tests. Analytical techniques encompass machine learning–driven pattern recognition for anomaly detection in production data, regression analysis for sensitivity studies, and stochastic optimization for injection policy selection. Theoretical underpinnings draw on Darcy-based multi-phase flow theory, the Volumetric Sweep Efficiency concept, and stability analysis of dynamic control systems, with relevant references to Hughes–Tornqvist probability bounds for uncertainty propagation in forecasted recovery. The study adopts a pragmatic decision framework incorporating risk assessment and techno-economic evaluation (net present value, internal rate of return, and breakeven oil price) under three price scenarios. Key expected findings include (i) identification of aquifer compartmentalization and heterogeneity-driven breakthrough patterns enabling targeted injection zoning; (ii) demonstrable gains in cumulative oil recovery of 8–15% (PIO) relative to baseline primary–secondary recovery after 5 years, with water cuts reduced by 5–12 percentage points through optimized injection profiles; (iii) improved sweep efficiency in low-permeability intervals via polymer functionality and, where feasible, surfactant-assisted mobility control; (iv) a dynamic optimization algorithm capable of updating injection rates and volumes within a 48-hour decision horizon in response to real-time data; (v) a probabilistic economic assessment showing favorable project viability under moderate oil price volatility, with NPV gains robust to ±20% reservoir parameter uncertainty. The study contributes to knowledge by providing a transposable, end-to-end integrated waterflood optimization framework for mature Nigerian oilfields, combining high-resolution geological characterization with data-driven optimization and real-time operational control. It advances methodology for reservoir surveillance, uncertainty quantification, and decision-support in waterflood management, and offers a practical blueprint for scaling to other fields with similar geologic heterogeneity and water handling constraints. The main conclusion posits that an integrated, adaptive waterflood strategy—rooted in granular reservoir understanding and anchored by real-time analytics—can deliver durable oil gains and lower water handling costs in Sakina. Recommendations include broader deployment of intelligent monitoring systems, phased polymer or SP chemical trials in high-pay zones, strengthening training for field engineers in data analytics, and the establishment of a governance framework for continuous optimization across production teams.