Sensitivity Analysis and History Matching of a Gas Condensate Reservoir, A Field Case Study of a Niger Delta Gas Condensate Reservoir

Abstract

Gas condensate reservoir exhibit complex phase behaviour once the pressure falls below the dew point pressure, fluids condense out of the gas, forming a condensate ring in the near wellbore region, which reduces gas well deliverability, and causes a reduction in the recovery factor from the reservoir. The complex phase behaviour of a gas condensate reservoir and compositional variations however, makes long term or future prediction of the reservoir performance extremely intricate. This paper thus, aims to conduct an optimization process that improves productivity from a gas condensate reservoir, and improves future predictability of reservoir performance. Firstly, a sensitivity analysis was conducted on the gas condensate reservoir., by applying the response surface methodology technique. The sensitivity analysis enabled a broader understanding of the simulation model, and identified parameters that were likely to have an effect on the consistency of the model. The next step involved conducting a history match of the reservoir production data recorded in the field against reservoir simulated production data. This involved calibrating the simulation model with actual production data from the reservoir, to ensure a perfect or near perfect representation of the reservoir performance, which was achieved by applying the Designed Exploration and Controlled Evolution (DECE). Once an accurate representation of the reservoir performance was achieved, the model was then used to perform production optimisation. Particle swarm optimisation technique was used in the optimisation of the reservoir, to provide information on the best production methodology for the reservoir that would improve profitability of the production process. A numerical study was conducted using the CMG 2021 compositional simulator, the CMOST AI simulator module, for various experimental studies, to determine proxy solutions that match the actual field history production data from the reservoir, and to predict the future performance of the reservoir. Obtained results showed an improvement in the recovery factor from the reservoir.