Integrated Gas-Llood-Through Dewatering for Offshore Reservoir Optimization

Thesis Abstract

Offshore oil reservoirs increasingly exhibit gas-lift and water influx challenges that compromise production efficiency and reservoir management, necessitating integrated dewatering strategies to sustain deliverability while minimizing energy consumption. This study addresses the gap in operational guidance for simultaneously managing gas-lift efficiency and water-cut through dewatering interventions, with a focus on offshore carbonate and sandstone reservoirs subjected to dynamic gas migration and scaling tendencies. The aim is to design, implement, and evaluate a holistic dewatering framework—Integrated Gas-Llood-Through Dewatering (IGLTD)—that optimizes gas-lift performance, reduces free and residual water production, and enhances reservoir pressure management through coordinated surface and subsurface control. Specific objectives are to (i) quantify the interdependencies between gas-lift gas-to-water ratio, production pressure, and water cut under varying reservoir drainage scenarios; (ii) develop a mathematical model linking dewatering parameters (gas lift optimization, selective water- production throttling, and downhole dewatering devices) to oil recovery and overall energy utilization; (iii) implement a field-scale pilot on a 5-well offshore cluster to evaluate operational feasibility, control strategies, and cost implications; (iv) validate the model through history matching using production data from at least 24 months of telemetry and well-logging data; and (v) formulate a decision-support framework for field-development planning under uncertainty. A mixed-methods research design is employed, combining quantitative reservoir engineering analytics with qualitative process optimization assessments. The population comprises offshore reservoirs experiencing simultaneous gas lift- and water-cut challenges within a major offshore field in the Gulf of Mexico. A representative sample of 5 production clusters (comprising 15 wells total) is selected for the pilot deployment, with continuous- monitoring data collected over 18 months. Data collection instruments include downhole pressure/temperature gauges, gas-lift valve telemetry, produced-fluid samples for carbon dioxide and hydrocarbon composition analysis, water-influx rate sensors, and surface facility energy meters. Instrument validity is established through calibration against established field-validated baselines and cross-checked by independent third-party metering. Reliability is ensured via redundant sensor pathways and regular maintenance, with data integrity verified through automated anomaly detection and quality-control protocols. Analytical methods entail regression-based optimization to identify key drivers of dewatering performance, time-series analyses to discern transient responses, and multivariate process control to optimize gas-lift gas, water rate, and surface handling. The mathematical framework includes a coupled reservoir-wland surface system model with mass and momentum balances, reservoir simulators calibrated to historical production, and a superimposed optimization algorithm (nonlinear programming) to identify optimal gas-lift settings and dewatering device configurations. The study employs ANOVA to compare performance across pilot wells and sensitivity analyses to assess parameter uncertainty. Theoretical underpinnings draw on the Sustainability-Performance Theory for energy efficiency in offshore operations and the Black-Oil Pseudo-Pressure framework to capture gas-lift dynamics, complemented by the Diffusion of Innovation theory to interpret adoption of the integrated dewatering approach among field engineers. A conceptual model illustrates the feedback loop among gas-lift control, dewatering device operation, and reservoir response. Key expected findings include (i) quantifiable gains in net oil recovery and reductions in produced water rate, (ii) evidence of reduced energy intensity per barrel via optimized gas-lift scheduling and targeted downhole dewatering, (iii) validated predictive model capable of history matching 24–30 months of field data with acceptable RMSE (<12%) for oil production and water cut trajectories, (iv) practical guidelines for implementing IG LTD in offshore environments, including control-system architecture, instrumentation requirements, and risk mitigation strategies, and (v) a cost-benefit framework demonstrating payback periods under different crude-price scenarios. The study contributes to knowledge by offering a novel, integrative design framework that links gas-lift optimization, downhole dewatering, and surface processing to reservoir performance, supported by empirical field validation and a transferable decision-support toolkit for offshore operators. The main conclusion posits that coordinated gas-lift and dewatering control can significantly enhance offshore reservoir performance under water influx, with substantial energy savings and improved asset profitability. Recommendations include expanding pilot deployments to multi-field transients, integrating machine-learning-based predictive maintenance for sensors, and incorporating uncertainty-aware optimization in field-development planning.