The Experts below are selected from a list of 5640 Experts worldwide ranked by ideXlab platform
M.a. Rahman - One of the best experts on this subject based on the ideXlab platform.
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A novel sensorless IPM motor drive for electric Submersible Pumps
2017 IEEE International Electric Machines and Drives Conference (IEMDC), 2017Co-Authors: S Fazle M Rabbi, M. Constantine, M.a. RahmanAbstract:Electric Submersible Pumps (ESPs) are widely deployed for oil and gas production from deep downhole reservoirs. ESPs are located inside a well from hundreds to several thousands of feet below the surface. A variable speed drive (VSD) is situated on the surface platform, and power is supplied to the ESP from the VSD through long downhole cables that run down the well. Due to the long cable length, position sensor based speed controllers for traditional IPM drives are impractical for ESPs. In this paper, a novel sensorless controller is proposed for stable and efficient operation of IPM motor driven ESPs. The proposed controller applies a back-emf estimation algorithm to determine the rotor position. A novel sensorless V/f controller coupled with the back-emf estimation algorithm is designed to start and stabilize the IPM-ESP drive at a low speed. A new transition algorithm is developed to switch from sensorless V/f control state to vector control for improving efficiency and power factor. The developed sensorless control technique is investigated on a 3-phase, 6-pole, 575V, 10-HP Submersible IPM-ESP drive system; and the performance results are presented in this paper.
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modeling and performance evaluation of a hysteresis ipm motor drive for electric Submersible Pumps
European Conference on Cognitive Ergonomics, 2015Co-Authors: S Fazle M Rabbi, M.a. Rahman, M M Sarker, Stephen ButtAbstract:This paper presents modeling and analysis of a hysteresis interior permanent magnet (IPM) motor drive for electric Submersible Pumps. A hysteresis IPM motor can self-start without the need of additional position sensors and complex control techniques. It does not have any slip power losses in the rotor at steady state which results in less heat dissipation and low electrical losses. When used in an electric Submersible pump (ESP) for oil production, it has the ability to automatically adapt itself to the changes in well conditions. In this paper, a bond graph model of a hysteresis IPM motor ESP drive is used to predict the effect of rotor dynamics on the transient behavior of the Submersible motor drive. Experimental investigations have been also carried out for a laboratory prototype 5HP hysteresis IPM motor drive. Due to increased efficiency and simplified controller requirements, the hysteresis IPM motor is proposed as a replacement for the standard induction motor currently used for downhole ESPs in offshore oil recovery plants.
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analysis of a hysteresis ipm motor drive for electric Submersible Pumps in harsh atlantic offshore environments
ASME 2015 34th International Conference on Ocean Offshore and Arctic Engineering, 2015Co-Authors: S F Rabbi, Stephen Butt, Mejbahul Sarker, D G Rideout, M.a. RahmanAbstract:This paper presents the analysis of a hysteresis interior permanent magnet (IPM) motor drive for electric Submersible Pumps. A hysteresis IPM motor is a self-starting solid rotor hybrid synchronous motor. Its rotor has a cylindrical ring made of composite materials with high degree of hysteresis energy. The rare earth permanent magnets are buried inside the hysteresis ring. A hysteresis IPM motor can self-start without the need of additional position sensors and complex control techniques. It does not have any slip power losses in the rotor at steady state which results in less heat dissipation and low electrical losses. When used in an electric Submersible pump (ESP) for oil production, it has the ability to automatically adapt itself to the changes in well conditions. In this paper, a bond graph model of a hysteresis IPM motor ESP drive is used to predict the effect of pump shaft geometry on transient behaviour of the drive during start-up. Simulation results show that the hysteresis IPM motor drive has high efficiency, and is better able to maintain its speed during changes in load. Due to increased efficiency and simplified controller requirements, the hysteresis IPM motor is proposed as a replacement for the standard induction motor currently used for downhole ESPs. This is expected to improve ESP performance and reliability which are critical requirements for use in harsh offshore environments such as Atlantic Canada.Copyright © 2015 by ASME
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Modeling and operation of an interior permanent magnet motor drive for electric Submersible Pumps
2014 Oceans - St. John's, 2014Co-Authors: S Fazle M Rabbi, M.a. Rahman, S. D. ButtAbstract:Electric Submersible Pumps (ESP) are widely used in artificial lift devices in offshore oil and gas. Induction motor drives are the current standard for ESPs. This paper presents the design, analysis and operation of energy efficient, compact and cost effective interior permanent magnet (IPM) motors for applications in ESP drive systems. The design of an original prototype IPM motor with straight-magnets orientation is presented in this paper. The performance results of a laboratory prototype 3-phase 4-pole 208V IPM motor for ESP drives are also presented and analyzed.
Karolos M Grigoriadis - One of the best experts on this subject based on the ideXlab platform.
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modelling of petroleum multiphase flow in electrical Submersible Pumps with shallow artificial neural networks
Ships and Offshore Structures, 2020Co-Authors: Morteza Mohammadzaheri, Reza Tafreshi, Zurwa Khan, Mojatba Ghodsi, Mathew Franchek, Karolos M GrigoriadisAbstract:This paper first investigates existing empirical models which predict head or pressure increase of two-phase petroleum fluids in electrical Submersible Pumps (ESPs); then, proposes an alternative m...
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modelling of electrical Submersible Pumps for petroleum multiphase fluids an intelligent approach supported by a critical review and experimental results
The Journal of Engineering Research, 2019Co-Authors: Morteza Mohammadzaheri, Reza Tafreshi, Zurwa Khan, Mojatba Ghodsi, Mathew Franchek, Hamidreza Ziaiefar, Karolos M GrigoriadisAbstract:This paper initially reviews existing empirical models which predict head or pressure increase of two-phase petroleum fluids in electrical Submersible Pumps (ESPs), then, proposes an alternative model, a fully connected cascade (FCC in short) artificial neural network to serve the same purpose. Empirical models of ESP are extensively in use; while analytical models are yet to be vastly employed in practice due to their complexity, reliance on over-simplified assumptions or lack of accuracy. The proposed FCC is trained and cross-validated with the same data used in developing a number of empirical models; however, the developed model presents higher accuracy than the aforementioned empirical models. The mean of absolute prediction error of the FCC for the experimental data not used in its training, is 68% less than the most accurate existing empirical model.
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an intelligent approach to optimize multiphase subsea oil fields lifted by electrical Submersible Pumps
Journal of Computational Science, 2016Co-Authors: Morteza Mohammadzaheri, Reza Tafreshi, Zurwa Khan, Matthew A Franchek, Karolos M GrigoriadisAbstract:Abstract This paper aims to introduce a method to maximize the profit of subsea petroleum fields lifted by electrical Submersible Pumps (ESPs). Unlike similar previous research which dealt with single-phase fluids, the reservoir is assumed to have oil, water and gas. Two major steps are taken in this research. First, algorithms including artificial neural networks (more specifically, multi-layer perceptrons) are developed to estimate head and brake horse power ( BHP ) of ESPs for gaseous fluids. These algorithms are essential to estimate the profit of the petroleum field. Second, an evolutionary algorithm is proposed and verified to maximize the profit. The proposed algorithm includes a newly devised stage that particularly facilitates solving heavily constrained problems. Finally, the methodology is employed to solve several sample problems.
Morteza Mohammadzaheri - One of the best experts on this subject based on the ideXlab platform.
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modelling of petroleum multiphase flow in electrical Submersible Pumps with shallow artificial neural networks
Ships and Offshore Structures, 2020Co-Authors: Morteza Mohammadzaheri, Reza Tafreshi, Zurwa Khan, Mojatba Ghodsi, Mathew Franchek, Karolos M GrigoriadisAbstract:This paper first investigates existing empirical models which predict head or pressure increase of two-phase petroleum fluids in electrical Submersible Pumps (ESPs); then, proposes an alternative m...
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modelling of electrical Submersible Pumps for petroleum multiphase fluids an intelligent approach supported by a critical review and experimental results
The Journal of Engineering Research, 2019Co-Authors: Morteza Mohammadzaheri, Reza Tafreshi, Zurwa Khan, Mojatba Ghodsi, Mathew Franchek, Hamidreza Ziaiefar, Karolos M GrigoriadisAbstract:This paper initially reviews existing empirical models which predict head or pressure increase of two-phase petroleum fluids in electrical Submersible Pumps (ESPs), then, proposes an alternative model, a fully connected cascade (FCC in short) artificial neural network to serve the same purpose. Empirical models of ESP are extensively in use; while analytical models are yet to be vastly employed in practice due to their complexity, reliance on over-simplified assumptions or lack of accuracy. The proposed FCC is trained and cross-validated with the same data used in developing a number of empirical models; however, the developed model presents higher accuracy than the aforementioned empirical models. The mean of absolute prediction error of the FCC for the experimental data not used in its training, is 68% less than the most accurate existing empirical model.
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an intelligent approach to optimize multiphase subsea oil fields lifted by electrical Submersible Pumps
Journal of Computational Science, 2016Co-Authors: Morteza Mohammadzaheri, Reza Tafreshi, Zurwa Khan, Matthew A Franchek, Karolos M GrigoriadisAbstract:Abstract This paper aims to introduce a method to maximize the profit of subsea petroleum fields lifted by electrical Submersible Pumps (ESPs). Unlike similar previous research which dealt with single-phase fluids, the reservoir is assumed to have oil, water and gas. Two major steps are taken in this research. First, algorithms including artificial neural networks (more specifically, multi-layer perceptrons) are developed to estimate head and brake horse power ( BHP ) of ESPs for gaseous fluids. These algorithms are essential to estimate the profit of the petroleum field. Second, an evolutionary algorithm is proposed and verified to maximize the profit. The proposed algorithm includes a newly devised stage that particularly facilitates solving heavily constrained problems. Finally, the methodology is employed to solve several sample problems.
Reza Tafreshi - One of the best experts on this subject based on the ideXlab platform.
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modelling of petroleum multiphase flow in electrical Submersible Pumps with shallow artificial neural networks
Ships and Offshore Structures, 2020Co-Authors: Morteza Mohammadzaheri, Reza Tafreshi, Zurwa Khan, Mojatba Ghodsi, Mathew Franchek, Karolos M GrigoriadisAbstract:This paper first investigates existing empirical models which predict head or pressure increase of two-phase petroleum fluids in electrical Submersible Pumps (ESPs); then, proposes an alternative m...
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modelling of electrical Submersible Pumps for petroleum multiphase fluids an intelligent approach supported by a critical review and experimental results
The Journal of Engineering Research, 2019Co-Authors: Morteza Mohammadzaheri, Reza Tafreshi, Zurwa Khan, Mojatba Ghodsi, Mathew Franchek, Hamidreza Ziaiefar, Karolos M GrigoriadisAbstract:This paper initially reviews existing empirical models which predict head or pressure increase of two-phase petroleum fluids in electrical Submersible Pumps (ESPs), then, proposes an alternative model, a fully connected cascade (FCC in short) artificial neural network to serve the same purpose. Empirical models of ESP are extensively in use; while analytical models are yet to be vastly employed in practice due to their complexity, reliance on over-simplified assumptions or lack of accuracy. The proposed FCC is trained and cross-validated with the same data used in developing a number of empirical models; however, the developed model presents higher accuracy than the aforementioned empirical models. The mean of absolute prediction error of the FCC for the experimental data not used in its training, is 68% less than the most accurate existing empirical model.
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an intelligent approach to optimize multiphase subsea oil fields lifted by electrical Submersible Pumps
Journal of Computational Science, 2016Co-Authors: Morteza Mohammadzaheri, Reza Tafreshi, Zurwa Khan, Matthew A Franchek, Karolos M GrigoriadisAbstract:Abstract This paper aims to introduce a method to maximize the profit of subsea petroleum fields lifted by electrical Submersible Pumps (ESPs). Unlike similar previous research which dealt with single-phase fluids, the reservoir is assumed to have oil, water and gas. Two major steps are taken in this research. First, algorithms including artificial neural networks (more specifically, multi-layer perceptrons) are developed to estimate head and brake horse power ( BHP ) of ESPs for gaseous fluids. These algorithms are essential to estimate the profit of the petroleum field. Second, an evolutionary algorithm is proposed and verified to maximize the profit. The proposed algorithm includes a newly devised stage that particularly facilitates solving heavily constrained problems. Finally, the methodology is employed to solve several sample problems.
Zurwa Khan - One of the best experts on this subject based on the ideXlab platform.
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modelling of petroleum multiphase flow in electrical Submersible Pumps with shallow artificial neural networks
Ships and Offshore Structures, 2020Co-Authors: Morteza Mohammadzaheri, Reza Tafreshi, Zurwa Khan, Mojatba Ghodsi, Mathew Franchek, Karolos M GrigoriadisAbstract:This paper first investigates existing empirical models which predict head or pressure increase of two-phase petroleum fluids in electrical Submersible Pumps (ESPs); then, proposes an alternative m...
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modelling of electrical Submersible Pumps for petroleum multiphase fluids an intelligent approach supported by a critical review and experimental results
The Journal of Engineering Research, 2019Co-Authors: Morteza Mohammadzaheri, Reza Tafreshi, Zurwa Khan, Mojatba Ghodsi, Mathew Franchek, Hamidreza Ziaiefar, Karolos M GrigoriadisAbstract:This paper initially reviews existing empirical models which predict head or pressure increase of two-phase petroleum fluids in electrical Submersible Pumps (ESPs), then, proposes an alternative model, a fully connected cascade (FCC in short) artificial neural network to serve the same purpose. Empirical models of ESP are extensively in use; while analytical models are yet to be vastly employed in practice due to their complexity, reliance on over-simplified assumptions or lack of accuracy. The proposed FCC is trained and cross-validated with the same data used in developing a number of empirical models; however, the developed model presents higher accuracy than the aforementioned empirical models. The mean of absolute prediction error of the FCC for the experimental data not used in its training, is 68% less than the most accurate existing empirical model.
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an intelligent approach to optimize multiphase subsea oil fields lifted by electrical Submersible Pumps
Journal of Computational Science, 2016Co-Authors: Morteza Mohammadzaheri, Reza Tafreshi, Zurwa Khan, Matthew A Franchek, Karolos M GrigoriadisAbstract:Abstract This paper aims to introduce a method to maximize the profit of subsea petroleum fields lifted by electrical Submersible Pumps (ESPs). Unlike similar previous research which dealt with single-phase fluids, the reservoir is assumed to have oil, water and gas. Two major steps are taken in this research. First, algorithms including artificial neural networks (more specifically, multi-layer perceptrons) are developed to estimate head and brake horse power ( BHP ) of ESPs for gaseous fluids. These algorithms are essential to estimate the profit of the petroleum field. Second, an evolutionary algorithm is proposed and verified to maximize the profit. The proposed algorithm includes a newly devised stage that particularly facilitates solving heavily constrained problems. Finally, the methodology is employed to solve several sample problems.
