Abstract
The accurate and in-time prediction of gas condensates dew point pressure (PDew) is of great importance regarding the technical and economic points of view for fluid characterization, reservoir performance calculations, planning the development of gas condensate reservoirs, and design and optimization of production systems. Although the laboratory tests provide the most accurate and reliable results, it is an expensive and timeconsuming process sometimes associated with some errors. Artificial intelligence-based methods have emerged as promising tools in different aspects of engineering. In this study, after a thorough analysis of the gas condensate data samples, the application of different intelligent modeling is investigated. A databank of 721 data samples is gathered, and different intelligent methods approaches are used for modeling. The results of three different data mining methods are combined using Committee Machine Intelligent Systems (CMIS) in an attempt to receive more accurate results. Three different methods of arithmetic, geometric, and harmonic approaches are utilized to develop the CMIS model. It was concluded that the harmonic CMIS yields the best predictions by average absolute relative deviation (AARD) and R2 values of 3.456% and 0.9702, respectively. This novel CMIS model could successfully outperform all the developed initial models. Additionally, a literature review showed that the proposed model could outperform the previously published models including artificial intelligence, equation of state, and correlation-based method considering both prediction accuracy and data coverage.
NSTL
Elsevier