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This manuscript introduces a general regression neural network (GRNN) model for estimating the minimum miscibility pressure (MMP) required for the multi-contact miscible displacement of reservoir fluids by CO2 injection.
A new genetic algorithm (GA -based correlation has been developed to estimate the CO2–oil minimum miscibility pressure (MMP)—a key parameter in desiGA -based miscorrelation to enhasce oil recovery.
Accurate knowledge of the minimum miscibility pressure (MMP) is essential in successful design of any miscible gas injection process, particularly in CO2 flooding.
During miscible gas flooding applications, reservoir pressure is built up above minimum miscibility pressure (MMP) when gas is injected to the reservoir.
Typically, CO2 assisted enhanced oil recovery projects are operated at the minimum miscibility pressure (MMP), beyond which the CO2 and oil phases are completely miscible and the incremental oil recovery from CO2 displacement does not increase dramatically anymore.
Feasibility of miscible gas injection in the Bakken would depend on the analysis of minimum miscibility pressure (MMP) experiments.
Therefore, determination of the minimum miscibility pressure (MMP) of reservoir live oil injection gas system is critical in a miscible gas flooding project design.
For the miscible CO2-injection project, the reservoir pressure must be maintained at the minimum miscibility pressure or higher.
CO2 was injected above its minimum miscibility pressure.
Minimum miscibility pressure (MMP) is a key parameter in any gas injection design project.
Minimum miscibility pressure (MMP) is the most important parameter to successfully design N2 flooding.
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