Abstract Dewpoint pressure is an important parameter for reservoir management and characterization. Gas condensate reservoirs experience significant reduction in productivity when initial reservoir pressure decreases below the dewpoint pressure. As such, an effective and efficient methods for prediction of this thermodynamic quantity are crucial for operational plans. In this article, a hybrid artificial intelligence model, based on adaptive neuro-fuzzy approach, for the prediction of gas condensate dewpoint pressure is presented. The proposed model combines the learning ability of artificial neural network and the capability of rule-based fuzzy inference system. First, fuzzy subtractive clustering technique is applied to a set of measured input/output data to identify an initial system based on extracted set of rules. The generated system is then trained using adaptive neuro-fuzzy inference system after which model validation and testing were performed. The performance of the proposed model is compared with existing methods. The results show that our proposed model outperforms the previous and existing methods with 99% accuracy and with the least root mean square error of 2.188 for some selected fluid sample.

中文翻译：

---

用于预测凝析气藏露点压力的自适应神经模糊方法

摘要 露点压力是储层管理和表征的重要参数。当初始储层压力降低到低于露点压力时，凝析气储层的产能会显着降低。因此，一种有效且高效的热力学量预测方法对于操作计划至关重要。在本文中，提出了一种基于自适应神经模糊方法的混合人工智能模型，用于预测气体凝析油露点压力。该模型结合了人工神经网络的学习能力和基于规则的模糊推理系统的能力。首先，模糊减法聚类技术应用于一组测量的输入/输出数据，以根据提取的规则集识别初始系统。然后使用自适应神经模糊推理系统训练生成的系统，然后执行模型验证和测试。将所提出模型的性能与现有方法进行比较。结果表明，对于某些选定的流体样本，我们提出的模型以 99% 的准确率和 2.188 的最小均方根误差优于以前和现有的方法。