It is economically and technically essential to promptly and accurately estimate the dew point pressure (DPP) of gas condensate to, for example, characterize fluids, evaluate the performance of reservoirs, plan and develop reservoirs for gas condensates, and design/optimize a production system. Indeed, it is difficult to experimentally explore the DPP. Furthermore, experimental tests are time-consuming and complicated. Therefore, it is required to develop an accurate, reliable DPP estimation framework. This paper introduces artificial neural network (ANN) models coupled with optimization algorithms, including a genetic algorithm (GA) and particle swarm optimization (PSO), for DPP estimation. A total of 721 data points were employed to train and test the algorithm. In addition, the outlier data were identified and excluded. The root-mean-squared error (RMSE) and the coefficient of determination (R²) were calculated to be 230.42 and 0.982 for the PSO-ANN model and 0.0022 and 0.997 for the GA-ANN model, respectively. The model estimates were found to be in good agreement with the experimental dataset. Therefore, it can be said that the proposed method is efficient and effective.

中文翻译：

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应用优化的 ANN 模型估计凝析油的露点压力

快速准确地估计凝析气露点压力 (DPP) 在经济和技术上至关重要，例如，表征流体、评估储层性能、规划和开发凝析油储层以及设计/优化生产系统. 事实上，很难通过实验探索 DPP。此外，实验测试既费时又复杂。因此，需要开发一个准确、可靠的 DPP 估计框架。本文介绍了人工神经网络 (ANN) 模型与优化算法相结合，包括遗传算法 (GA) 和粒子群优化 (PSO)，用于 DPP 估计。总共使用了 721 个数据点来训练和测试算法。此外，异常数据被识别和排除。R ² ) 对于 PSO-ANN 模型计算为 230.42 和 0.982，对于 GA-ANN 模型分别计算为 0.0022 和 0.997。发现模型估计与实验数据集非常吻合。因此，可以说所提出的方法是高效且有效的。