The accurate and in-time prediction of gas condensates dew point pressure (P_Dew) 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 time-consuming 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.

凝析油露点压力（P _{Dew ）准确及时预测}) 对于流体表征、储层动态计算、凝析气藏开发规划以及生产系统设计和优化的技术和经济观点具有重要意义。尽管实验室测试提供了最准确和最可靠的结果，但这是一个昂贵且耗时的过程，有时会伴随一些错误。基于人工智能的方法已成为工程不同方面的有前途的工具。本研究在对凝析气数据样本进行深入分析后，研究了不同智能建模的应用。收集了721个数据样本的数据库，并采用不同的智能方法方法进行建模。使用委员会机器智能系统 (CMIS) 将三种不同数据挖掘方法的结果结合起来，以期获得更准确的结果。三种不同的算术、几何和谐波方法用于开发 CMIS 模型。得出的结论是，谐波 CMIS 通过平均绝对相对偏差（AARD ) 和R ²值分别为 3.456% 和 0.9702。这种新颖的 CMIS 模型可以成功地超越所有已开发的初始模型。此外，文献综述表明，考虑到预测准确性和数据覆盖率，所提出的模型可以优于先前发布的模型，包括人工智能、状态方程和基于相关性的方法。