使用组合方法对气凝粘度的组成建模
Farzaneh Rezaei1, Mohammad Akbari2, Yousef Rafiei1
1Department of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran.
Scientific reports
|June 14, 2023
概括
这项研究开发了准确的模型,以使用温度,压力和组成来预测气体凝结物的粘度,从而消除了难以测量的溶液气油比率 (Rs) 的需要. 集体方法获得了最高的准确性,为水库工程提供了更实用的方法.
科学领域:
- 石油工程是石油工程中的一个.
- 物理化学 物理化学
- 数据科学数据科学数据科学
背景情况:
- 气凝储在井口附近经历液体滴出,因为压力下降到露点以下.
- 准确估计液体粘度对于预测这些水库的生产率至关重要.
- 现有的模型通常依赖于溶液油气比 (Rs),难以准确测量.
研究的目的:
- 开发精确的气体凝聚物粘度预测模型.
- 为了消除对溶液气油比率 (Rs) 作为输入参数的依赖.
- 利用全面的数据集和先进的机器学习技术来改进粘度预测.
主要方法:
- 使用了1370个实验室数据点的综合数据库来测量气凝液粘度.
- 应用并比较了几种智能技术:集体方法,支持向量回归 (SVR),K-近邻 (KNN),辐射基函数 (RBF) 和多层感知器 (MLP).
- 开发了使用温度,压力和凝结物组成作为输入参数的模型,不包括Rs.
主要成果:
- 组合方法表现出最高的准确性,平均绝对百分比相对误差 (AAPRE) 为4.83%.
- 其他模型的准确性各不相同:SVR (4.95%),KNN (5.45%),MLP-BR (6.56%),MLP-LM (7.89%) 和RBF (10.9%).
- 储温度对粘度有负面影响,而C11的摩尔分数则对粘度有积极影响,根据参数相关性分析确定.
结论:
- 通过使用温度,压力和成分,开发了精确的气体冷凝物粘度模型.
- 集体方法提供了一个非常准确和实用的方法,用于预测气体冷凝库中的粘度.
- 识别并报告可疑的实验室数据点,使用杆技术来提高数据质量.
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