The Experts below are selected from a list of 936 Experts worldwide ranked by ideXlab platform

Bing Zhang - One of the best experts on this subject based on the ideXlab platform.

  • improved method of processing downhole pressure data on Smart Wells
    Journal of Natural Gas Science and Engineering, 2016
    Co-Authors: Bing Zhang, Jiyou Xiong, Ningsheng Zhang, Jinlong Wang
    Abstract:

    Abstract New methods are presented in this paper to address the limitations and defects of existing methods of pressure data processing for Smart Wells. An absolute-deviation decision filtering method based on Hampel estimation is utilized to eliminate outliers. The conditional combination of wavelet threshold denoising is optimized through an orthogonal experiment. The data are reduced by using pressure and time thresholds and a derivative method to identify the transients of pressure data in accordance with different stages of pressure change. Results show that the new methods have a good practical value because they can solve problems in pressure data processing for Smart Wells.

Tao Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Smart Wells optimization in oil reservoirs using adjoint gradient model
    International Conference on Software Engineering, 2014
    Co-Authors: Xian Shan, Tao Zhang
    Abstract:

    Smart Wells can effectivelyimprove the reservoir developing by selectively controlling flow production of the valves. In this work, we proposed an adjoint-based gradient method for the Smart well control optimization within the search for optimum Smart Wells inflow control valves configuration. Numerical study showed that the NPV value can be increased obviously by the optimization. The displacement efficiency can also be improved.

Jinlong Wang - One of the best experts on this subject based on the ideXlab platform.

  • improved method of processing downhole pressure data on Smart Wells
    Journal of Natural Gas Science and Engineering, 2016
    Co-Authors: Bing Zhang, Jiyou Xiong, Ningsheng Zhang, Jinlong Wang
    Abstract:

    Abstract New methods are presented in this paper to address the limitations and defects of existing methods of pressure data processing for Smart Wells. An absolute-deviation decision filtering method based on Hampel estimation is utilized to eliminate outliers. The conditional combination of wavelet threshold denoising is optimized through an orthogonal experiment. The data are reduced by using pressure and time thresholds and a derivative method to identify the transients of pressure data in accordance with different stages of pressure change. Results show that the new methods have a good practical value because they can solve problems in pressure data processing for Smart Wells.

Morteza Hassanabadi - One of the best experts on this subject based on the ideXlab platform.

  • optimization of icds port sizes in Smart Wells using particle swarm optimization pso algorithm through neural network modeling
    Journal of Chemical and Petroleum Engineering, 2012
    Co-Authors: Mahdi Nadri Pari, Sayed Mahdiya Motahhari, Morteza Hassanabadi
    Abstract:

    Oil production optimization is one of the main targets of reservoir management. Smart well technology gives the ability of real time oil production optimization. Although this technology has many advantages; optimum adjustment or sizing of corresponding valves is still an issue to be solved. In this research, optimum port sizing of inflow control devices (ICDs) which are passive control valves is focused on by designing a neural network to simulate reservoir behavior and applying Particle Swarm Optimization algorithm to find optimum port size for ICDs. Indeed; this work eliminates the need for lots of expensive and time consuming iterations through reservoir simulator. The objective of the work is to maximize the oil production.

  • optimization of icds port size in Smart Wells using particle swarm optimization pso algorithm through neural network modeling
    Nashrieh Shimi va Mohandesi Shimi Iran, 2012
    Co-Authors: Morteza Hassanabadi, Sayed Mahdiya Motahhari, Mahdi Nadri Pari
    Abstract:

    Oil production optimization is one of the main targets of reservoir management. Smart well technology gives ability of real time oil production optimization. Although this technology has many advantages; optimum adjustment or sizing of corresponding valves is its issue. In this research optimum sizing of ICDs which are passive control valves has been focused on by designing a neural network to simulate reservoir behavior and applying Particle Swarm Optimization (PSW) algorithm to find optimum port size for ICDs. Indeed; this work eliminates the need for lots of expensive and time consuming iteration through reservoir simulator. The achieved objectives of the work were oil production maximization and water production minimization.

Mahdi Nadri Pari - One of the best experts on this subject based on the ideXlab platform.

  • optimization of icds port sizes in Smart Wells using particle swarm optimization pso algorithm through neural network modeling
    Journal of Chemical and Petroleum Engineering, 2012
    Co-Authors: Mahdi Nadri Pari, Sayed Mahdiya Motahhari, Morteza Hassanabadi
    Abstract:

    Oil production optimization is one of the main targets of reservoir management. Smart well technology gives the ability of real time oil production optimization. Although this technology has many advantages; optimum adjustment or sizing of corresponding valves is still an issue to be solved. In this research, optimum port sizing of inflow control devices (ICDs) which are passive control valves is focused on by designing a neural network to simulate reservoir behavior and applying Particle Swarm Optimization algorithm to find optimum port size for ICDs. Indeed; this work eliminates the need for lots of expensive and time consuming iterations through reservoir simulator. The objective of the work is to maximize the oil production.

  • optimization of icds port size in Smart Wells using particle swarm optimization pso algorithm through neural network modeling
    Nashrieh Shimi va Mohandesi Shimi Iran, 2012
    Co-Authors: Morteza Hassanabadi, Sayed Mahdiya Motahhari, Mahdi Nadri Pari
    Abstract:

    Oil production optimization is one of the main targets of reservoir management. Smart well technology gives ability of real time oil production optimization. Although this technology has many advantages; optimum adjustment or sizing of corresponding valves is its issue. In this research optimum sizing of ICDs which are passive control valves has been focused on by designing a neural network to simulate reservoir behavior and applying Particle Swarm Optimization (PSW) algorithm to find optimum port size for ICDs. Indeed; this work eliminates the need for lots of expensive and time consuming iteration through reservoir simulator. The achieved objectives of the work were oil production maximization and water production minimization.