Real-Time Prediction of Plastic Viscosity and Apparent Viscosity for Oil-Based Drilling Fluids Using a Committee Machine with Intelligent Systems

被引:0
作者
Mohamed Riad Youcefi
Ahmed Hadjadj
Abdelak Bentriou
Farouk Said Boukredera
机构
[1] Université M’Hamed Bougara - Boumerdès,Laboratoire de Fiabilité des Equipements Pétroliers et Matériaux, Faculté des hydrocarbures et de la chimie
来源
Arabian Journal for Science and Engineering | 2022年 / 47卷
关键词
Drilling muds; Apparent viscosity; Plastic viscosity; Committee machine intelligent system; RBFNN; MLP;
D O I
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中图分类号
学科分类号
摘要
The prediction of drilling mud rheological properties is a crucial topic with significant importance in analyzing frictional pressure loss and modeling the hole cleaning. Based on Marsh viscosity, mud density, and solid percent, this paper implements a committee machine intelligent system (CMIS) to predict apparent viscosity (AV) and plastic viscosity (PV) of oil-based mud. The established CMIS combines radial basis function neural network (RBFNN) and multilayer perceptron (MLP) via a quadratic model. Levenberg–Marquardt algorithm was applied to optimize the MLP, while differential evolution, genetic algorithm, artificial bee colony, and particle swarm optimization were used to optimize the RBFNN. A databank of 440 and 486 data points for AV and PV, respectively, gathered from various Algerian fields was considered to build the proposed models. Statistical and graphical assessment criteria were employed for investigating the performance of the proposed CMIS. The obtained results reveal that the developed CMIS models exhibit high performance in predicting AV and PV, with an overall average absolute relative deviation (AARD %) of 2.5485 and 4.1009 for AV and PV, respectively, and a coefficient of determination (R2) of 0.9806 and 0.9753 for AV and PV, respectively. A comparison of the CMIS-AV with Pitt's and Almahdawi's models demonstrates its higher prediction capability than these previously published correlations.
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页码:11145 / 11158
页数:13
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