Gas Lift Network Optimization using Sequential Quadratic Programming in Mature Niger Delta Oilfield

Mature fields with declining reservoir pressure require artificial lift interventions to sustain production. This study evaluates gas lift optimization using the GAP network modeling platform integrated with Sequential Quadratic Programming (SQP) to maximize field oil output. Four wells were analyzed under separate and shared flowline configurations, considering multiphase flow, well inflow performance, and surface network hydraulics. Optimization scenarios dynamically allocated lift gas based on each well’s marginal oil-per-unit-gas response, while fixed-rate cases served as benchmarks. Results indicate that SQP-based optimization increases total field oil production by up to 5.8% under gas-limited conditions (5 MMscfd total lift gas) and maintains a 1–3% gain at higher gas availability (10 MMscfd). Optimized allocation reduced overall gas consumption for the same production levels, achieving savings of up to 15% in cumulative injection compared to uniform allocation. The separate flowline network consistently outperformed the shared configuration, yielding lower wellhead backpressure, improved drawdown, and higher oil rates, with differences up to 2.3% at mid-range gas volumes. Sensitivity analysis further highlights the impact of surface network parameters: increasing manifold diameter enhances flow efficiency, while elevated separator pressures and excessive manifold lengths introduce backpressure losses that reduce production. The study demonstrates that integrated SQP-based gas-lift optimization enables targeted gas distribution, maximizes field-level oil recovery, improves artificial lift efficiency, and informs operational decisions regarding network design and gas injection strategies. These findings provide a scalable methodology for optimizing gas lift in low-pressure, multiphase production systems.