Prediction of water-in-oil emulsion drilling fluids rheological properties based on GPR-Bagging ensemble learning

被引:1
|
作者
Deng, Song [1 ]
Huo, Bingzhao [1 ]
Xu, Shoukun [1 ]
Peng, Mingguo [1 ]
Yan, Xiaopeng [1 ]
Li, Chaowei [1 ]
Wang, Jiangshuai [1 ]
Hao, Hongda [1 ]
Shi, Yadong [1 ]
机构
[1] Changzhou Univ, Changzhou 213164, Peoples R China
基金
中国国家自然科学基金;
关键词
Gaussian process regression; Bagging ensemble algorithm; Emulsion drilling fluids; Drilling fluid rheology; VISCOSITY; BEHAVIOR;
D O I
10.1016/j.colsurfa.2024.133336
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
In the field of petroleum engineering drilling, accurately and real-time determining the rheological properties of drilling fluids is crucial for controlling drilling operations. Traditional methods for measuring the rheological properties of drilling fluids are time-consuming and inefficient, hindering the optimization of real-time drilling fluid performance and impeding on-site decision-making for engineers. The development of artificial intelligence technology has improved the efficiency of measuring drilling fluid rheological properties. Predictive models for the rheological properties of water-based and all-oil-based drilling fluids have been established. However, these models perform well only on specific types of drilling fluids. Water-in-oil emulsion drilling fluids, while maintaining the advantages of all-oil-based drilling fluids, exhibit good environmental adaptability and are widely used in shale oil horizontal wells. This article comprehensively considers the impact of temperature and drilling fluid components (water % by vol, oil % by vol, solid % by vol) on drilling fluid performance. By using the GPRBagging improved ensemble algorithm, a predictive model for rheological parameters suitable for water-in-oil emulsion drilling fluids is established. Experimental results indicate that the new model accurately predicts the rheological properties of water-in-oil emulsion drilling fluids. The determination coefficients (R2) for the predictive models of apparent viscosity (AV)and plastic viscosity (PV) of drilling fluids both exceed 0.96, and the R2 for the behavior index (n) predictive model exceeds 0.90. When compared to existing mathematical and intelligent models, the new model demonstrates superior performance. The GPR-Bagging model enhances the measurement efficiency of rheological properties of water -in -oil emulsion drilling fluids, holding significant importance for promoting the automation of drilling fluid systems and drilling processes.
引用
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页数:10
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