Evolving predictive model to determine condensate-to-gas ratio in retrograded condensate gas reservoirs

Abstract

Added values to project economy from condensate sales and gas deliverability loss due to condensate blockage are the distinctive differences between gas condensate and dry gas reservoirs. To estimate the added value, one needs to obtain condensate to gas ratio (CGR); however, this needs special pressure–volume–temperature (PVT) experimental study and field tests. In the absence of experimental studies during early period of field exploration, techniques which correlate such a parameter would be of interest for engineers. In this work, the developed model inspired from a new intelligent scheme known as “least square support vector machine (LSSVM)” to monitor condensate gas ratio (CGR) in retrograde condensate gas reservoirs. The proposed approach is conducted to the laboratorial data from Iranian oil fields and reported in literature has been implemented to mature and test this approach. The generated results from the LSSVM model were compared to the addressed real data and generated results of conventional correlation and fuzzy logic models. Making judgements between the generated outcomes of our model and the another course of action proves that the least square support vector machine model estimate condensate gas ratio more accurately in comparison with the conventional applied approaches. It worth mentioning that, least square support vector machine do not have any conceptual errors like as over-fitting issue while artificial neural networks suffer from many local minima solutions. Outcomes of this research could couple with the commercial production softwares for condensate gas reservoirs for different goals such as production optimization and facilitate design.