On the prediction of filtration volume of drilling fluids containing different types of nanoparticles by ELM and PSO-LSSVM based models

Lekomtsev, Aleksander; Keykhosravi, Amin; Moghaddam, Mehdi Bahari; Daneshfar, Reza; Rezvanjou, Omid

doi:10.1016/j.petlm.2021.04.002

Cited by 14 publications

(4 citation statements)

References 35 publications

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“…The regularization parameter and kernel parameter affect the performance of LSSVM, and the determination of the values of these two parameters by empirical or grid methods suffers from subjectivity and arbitrariness, making the results inaccurate. Particle swarm optimization (PSO) is an evolutionary algorithm with fast convergence speed, a simple solution process, and global search capability, which can effectively optimize the above two parameters [ 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

A New Method for Erosion Prediction of 90° Elbow Based on Non-Axisymmetric Ultrasonic-Guided Wave and the PSO–LSSVM Algorithm

Wang

Zhou

et al. 2023

Sensors

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The non-axisymmetric exciting guided wave can detect the thinning section of the elbow, and the time domain energy value of the signal collected at the outer arch position of the receiving end displays a downward trend as the remaining thickness of the erosion area decreases. To address the difficulty in detecting the erosion degree of the elbow with high accuracy, this paper uses the linear frequency modulation (LFM) signal to excite a non-axisymmetric guided wave that propagates in the 90° elbow and collects signals through four PZT receivers. To predict the erosion degree, the corresponding relationship between the energy value of the four signals after fractional Fourier filtering and the degree of elbow erosion is established through the particle swarm optimization (PSO)–least squares support vector machine (LSSVM) algorithm. The results show that the method proposed has an average accuracy rate of 98.1864%, 94.7167%, 99.119%, and 99.9593% for predicting the erosion degree of four elbow samples, and 94.0039%. and 81.2976% for two new erosion degrees, which are higher than the nonlinear regression model, LSSVM algorithm, and BP neural network algorithm. This study has guiding significance for real-time monitoring of elbow erosion.

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Section: Introductionmentioning

confidence: 99%

A New Method for Erosion Prediction of 90° Elbow Based on Non-Axisymmetric Ultrasonic-Guided Wave and the PSO–LSSVM Algorithm

Wang

Zhou

et al. 2023

Sensors

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“…In the past few years, the application of attractive methods of modeling and data analysis to facilitate the solution of complex problems has attracted the attention of many researchers and scholars [35][36][37][38][39][40][41]. Machine learning algorithms have been extensively employed in a variety of disciplines throughout the last decade [42][43][44][45][46][47]. Neuronal networks' capacity to simulate nearly every function steadily and effectively is a significant factor in their fast development and wide range of applications.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Modeling in Predicting Liquid Density of the Refrigerant Systems Using Least-Squares Support Vector Machine Approach

Cai

Zhang²,

et al. 2022

International Journal of Chemical Engineering

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A robust machine learning algorithm known as the least-squares support vector machine (LSSVM) model was used to predict the liquid densities of 48 different refrigerant systems. Hence, a massive dataset was gathered using the reports published previously. The proposed model was evaluated via various analyses. Based on the statistical analysis results, the actual values predicted by this model have high accuracy, and the calculated values of RMSE, MRE, STD, and R2 were 0.0116, 0.158, 0.1070, and 0.999, respectively. Moreover, sensitivity analysis was done on the efficient input parameters, and it was found that CF2H2 has the most positive effect on the output parameter (with a relevancy factor of +50.19). Furthermore, for checking the real data accuracy, the technique of leverage was considered, the results of which revealed that most of the considered data are reliable. The power and accuracy of this simple model in predicting liquid densities of different refrigerant systems are high; therefore, it is an appropriate alternative for laboratory data.

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“…They are challenging topics because of technical, economic, and environmental concerns [1]. Water-based drilling fluids are the most commonly used in the drilling industry, as opposed to the more expensive oil-or synthetic-based drilling fluids that cause disposal, safety, health, and environmental problems [2]. Water, active and inactive solids, and chemical additives comprise most water-based drilling fluids [3].…”

Section: Introductionmentioning

confidence: 99%

Improved Water-Based Mud Rheological Properties and Shale-Inhibition Behavior by Using Aluminum Oxide and Iron Oxide Nanoparticles

Abbood¹,

Shakir²

2022

ETJ

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Nanoparticles reduced filter loss up 41 and 32% by Fe2O3 and Al2O3, respectively. • Reduction (COF) between pipe and wellbore up 48 and 34% by Al2O3 and Fe2O3, respectively. • Wellbore swelling reduction up 50 and 30% by Al2O3 and Fe2O3, respectively. Nanotechnology can be used to develop drilling fluid additives that can improve the drilling fluid's properties. Using two types of nanoparticle (NP) additives in water-based drilling fluids have been studied in this paper. Three major drilling mud systems, namely potassium chloride (KCl) as a basic mud, KCl/aluminum oxide (Al2O3) NPs, and KCl/iron (Fe2O3) NPs, were prepared and studied for enhancement of rheological properties and shale inhibition. It was found that the drilling mud contained NPs in concentrations of 0.25, 0. 5, 0.75, and 1 g. Al2O3 and Fe2O3 NPs added to KCl/polymer mud systems resulted in a 50% and 30% change in shale volume, respectively. The results demonstrated that incorporating NPs into the KCL mud system enhanced shale inhibition. Adding NPs to the KCL-WBM increased yield point, plastic viscosity, and gel strength.The COF of KCL-polymer was reduced by 48% and 34% when added Al2O3 and Fe2O3 NPs at 0.5 and 0.75g, respectively. When Al2O3 and Fe2O3 NPs were used, particularly at 1g, the amount of mud filtration decreased from 13.1ml to 8.8 ml and 8.4 ml, respectively. Overall, it was found that adding Al2O3 and Fe2O3 NPs to the KCl-WBM can improve rheological, swelling, and filtration properties as well as lubrication.

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On the prediction of filtration volume of drilling fluids containing different types of nanoparticles by ELM and PSO-LSSVM based models

Cited by 14 publications

References 35 publications

A New Method for Erosion Prediction of 90° Elbow Based on Non-Axisymmetric Ultrasonic-Guided Wave and the PSO–LSSVM Algorithm

A New Method for Erosion Prediction of 90° Elbow Based on Non-Axisymmetric Ultrasonic-Guided Wave and the PSO–LSSVM Algorithm

Comprehensive Modeling in Predicting Liquid Density of the Refrigerant Systems Using Least-Squares Support Vector Machine Approach

Improved Water-Based Mud Rheological Properties and Shale-Inhibition Behavior by Using Aluminum Oxide and Iron Oxide Nanoparticles

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