A Novel Approach to Predict the Performance of All Types of Reservoir Fluids from Heavy Oils to Dry Gases

Abstract

Abstract This study presents a new approach to predict the production performance of wells producing all types of reservoir fluids, including volatile oils and gas condensates. The developed approach combines the general material balance equation, inflow performance relationship, pseudo pressure calculation, and instantaneous gas-oil ratio equation. Using this approach, an integrated model has been developed to simulate the behavior of oil, gas, and gas condensate reservoirs. Furthermore, a genetic optimization algorithm was used with the integrated model for history matching to estimate the original oil/gas in place, productivity index, and saturation and pressure change with time. The developed model can also be used for production forecasting and reserve estimation. Moreover, it can predict well flowing bottomhole pressure, fluid rates, and reservoir pressure over the reservoir life using only the initial reservoir pressure and some production history. It should be highlighted that the developed model considers several drive mechanisms, including gas cap, depletion drive, and rock and fluid expansion. The integrated model was validated using fourteen reservoir simulation cases with different fluid types (three cases: black oil reservoir, five cases: volatile oil reservoir, five cases: retrograde gas condensate reservoir, and another case: wet gas reservoir). A field application was then used to demonstrate the model's ability to handle actual field data and predict reservoir performance. The results demonstrated a high level of accuracy and agreement between the results of the simulation cases and those of the integrated model. The errors between the results of the simulation cases and the results of the developed model in all cases were less than 1%. Moreover, the developed model will save time and effort considering that the interface of the developed model is user-friendly and the required input data is limited compared to the numerical simulation applications. The novelty of this study is that it replaces previous forecasting efforts that apply to only limited ranges of reservoir fluids with a new approach that applies to the full range of reservoir fluids, including gas condensates and volatile oils.