Comparison between an analytical method and two numerical methods on a given electrostatic potential determination problem

Creosteanu, Andreea; Gavrila, Gheorghe; Creosteanu, Laurentiu

doi:10.1109/antem.2012.6262310

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…The most thorough and exact-solution approaches are the analytical methods; yet they are difficult to apply to complicated problems [3]. One of the drawbacks of this method in sand production prediction is that it ignores the variation of stress in different directions in a material and assumes symmetrical geometry as boundary conditions [4].…”

Section: Introductionmentioning

confidence: 99%

Predicting Onset of Sand Production in Oil Wells using Machine Learning

Ngochindo,

Joseph

2024

JERR

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Sand production in oil wells is a significant challenge that negatively impacts productivity and compromise equipment integrity. This study explores the application of Optimized Support Vector Machine (SVM) binary classification algorithm to predict the onset of sand production in oil wells. A dataset from 63 oil wells was utilized, and class labels were determined based on the bulk and shear modulus product. The model development incorporated geological and mechanical parameters that could influence sand detachment such as: Young’s modulus, Poisson’s ratio, minimum and maximum horizontal stresses, overburden pressure, pore pressure, depth, fracture gradient, and formation strength. Instances above the threshold of 8E+11 were classified as indicative of no sand production, while those below were considered potential sand production scenarios. The SVM model demonstrated remarkable accuracy in predicting sand production onset, trained and tested rigorously with field data. The model's accuracy was evaluated using statistical parameters, such as: accuracy (ACC), sensitivity (SE), specificity (SP), and Matthew's Correlation Coefficient (MCC). From the results, the model achieved a score of 1 across all parameters, indicating high reliability and accuracy in sand production prediction. The practical implications of this model are significant, offering assistance to completion engineers in making proactive decisions regarding sand control strategies. Furthermore, the integration of this model into oil and gas industry processes can optimize operational efficiency by foreseeing potential sand production events, hence, preventing production impairment and ensuring loss prevention.

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Section: Introductionmentioning

confidence: 99%

Predicting Onset of Sand Production in Oil Wells using Machine Learning

Ngochindo,

Joseph

2024

JERR

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“…DC-link inverter is advantageous in renewable energy applications like grid tying of solar PV system and wind energy systems and also in adjustable speed motor systems due to its simplicity in both power stage topology and control strategy [1][2][3].There are three major techniques used for solving a problem. They are experimental, analytical and numerical methods [4]. It takes a lot of time and money for experimental method.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Solution Methods to Power Electronic Interface Modeling for Renewable Energy Applications

Randhir

Umashankar

Vijayakumar

et al. 2013

AMR

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This paper presents the comparison between various numerical methods namely Euler's Method, Midpoint Method, 2nd order Runge Kutta Method and 4th order Runge Kutta Method with the analytical method to solve a power electronic system in both single phase and three phase configuration using decoupled methodology. The values of source current, load current and DC link voltage are obtained for each method using Matlab software and compared with each other. Also, the error in each numerical method with respect to analytical method is calculated and tabulated. These power electronic models could be an excellent research platform for testing the renewable energy systems without going for full scale or scaled down models.

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Analytical investigation of gas production from methane hydrates and the associated heat and mass transfer upon thermal stimulation employing a coaxial wellbore

Roostaie

Leonenko

2020

Energy Conversion and Management

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In this study, a radial 2D analytical approach has been developed to couple the wellbore heating process and the associated methane hydrate dissociation in the reservoir. A coaxial wellbore is assumed as the heat source where both conduction and convection heat transfers are considered. It consists of an inner tube and an outer structure of casing, gravel, and cement layers. In the reservoir, a similarity solution employing a moving boundary separating the dissociated and undissociated zones is employed to build the analytical solution. Two different operating schemes for water supply into wellbore heat source have been studied: i) from the inner tube; and ii) from the annulus section of the wellbore. Temperature distribution along the wellbore, temperature and pressure distributions in the reservoir, hydrate dissociation rate, and energy efficiency considering various initial and boundary conditions and reservoir properties are evaluated. The two different operating schemes have almost the same results with slightly higher gas production in the case of hot water entry into annulus, which is in direct contact with the reservoir. Increasing the inlet water temperature or decreasing the wellbore pressure increases gas production. Applying them simultaneously results in a greater gas production and energy efficiency. Some of the reservoir's properties, such as porosity, thermal diffusivity, thermal conductivity, and reservoir thickness, have direct relation with the dissociation rate, but the reservoir's permeability and gas viscosity have almost no impact on the process. The wellbore parameters, such as flow rate of hot water, inlet temperature, and wellbore radius except the inner tube radius, have direct impact on the wellbore mean temperature and the associated results in the dissociation process.

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Comparison between an analytical method and two numerical methods on a given electrostatic potential determination problem

Cited by 4 publications

References 2 publications

Predicting Onset of Sand Production in Oil Wells using Machine Learning

Predicting Onset of Sand Production in Oil Wells using Machine Learning

Comparative Analysis of Solution Methods to Power Electronic Interface Modeling for Renewable Energy Applications

Analytical investigation of gas production from methane hydrates and the associated heat and mass transfer upon thermal stimulation employing a coaxial wellbore

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