Determination of Optimum Rate in a Condensate Well with a Case of a Wellbore Liquid Loading

Condensate reservoirs are mostly pressure sensitive and keeping the pressure above the dew point pressure in the reservoir is critical to avoid condensate banking in the reservoir. If it occurs, production is highly inhibited and the well may ultimately quit on production under liquid loading. Fluid ratios are important in the management of condensate wells and most critical is the Gas Liquid Ratio (GLR). There is a certain GLR that below it, there will be a liquid loading in the wellbore that could quit the well. Each fluid rate goes with a GLR and the point where there is a reversal of the GLR or CGR trends may present a case of loading scenario and that is taken as the determination reference point. When a condensate well shows an improvement of water cut as the choke bean size is reduced does not necessarily signify a healthy situation and neither a one-point higher water cut with increase in choke bean size mean a water coning situation. When a liquid loading well is beaned up, there is early signs of water coning in the production data but this is just a wellbore production and the BS&W improves as the production rate is further increased. Further investigation is necessary to separate the challenge of water conning from the challenge of too low Gas rate which causes the loading of the liquids in the wellbore. That is the operating envelop to manage condensate well rates: rates too low with a possibility of a liquid loading and rates too high that depicts a case of water conning when water is close to the perforation. This band must be completely exploited to turn the production curve in the positive. This paper provides a strategy to recover a condensate well production with a challenge of liquid loading using a case study. The degree of the severity of the liquid loading can be represented using a power law model with the gradient being the level of severity of the loading. The production improvement is greater than nβ percent where n is the quadratic model number 2 and β is the product of the graphical and Lagrangian-Quadratic alpha parameters. The optimum rate can be determined using the Lagrange Multiplier optimization method to effectively extend the production life of the well.