Optimisation of Intelligent Oil Wells using Stochastic Algorithms

Morteza Haghighat Sefat; Khafiz Muradov; Ahmed H Elsheikh; David Roland Davies

Optimisation of Intelligent Oil Wells using Stochastic Algorithms

Morteza Haghighat Sefat, Khafiz Muradov, Ahmed H Elsheikh, David Roland Davies

School of Energy, Geoscience, Infrastructure and Society

Research output: Contribution to conference › Paper

Abstract

Oil production enhancement in intelligent fields is a nonlinear, high-dimension, conditional optimisation problem with a computationally expensive objective function. Stochastic estimated-gradient-based optimisation algorithms have proved to be an effective tool to solve this class of problems. Solutions to this challenging problem using the Simultaneous Perturbation Stochastic Approximation (SPSA) and Ensemble-based Optimisation (EnOpt) algorithms are evaluated and their performance compared for multiple case studies. Optimisation guidelines will also be provided.

Original language	English
Publication status	Published - Jul 2014
Event	20th Conference of the International Federation of Operational Research Societies 2014 - , Spain Duration: 13 Jul 2014 → 18 Jul 2014

Conference

Conference	20th Conference of the International Federation of Operational Research Societies 2014
Country/Territory	Spain
Period	13/07/14 → 18/07/14

Cite this

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title = "Optimisation of Intelligent Oil Wells using Stochastic Algorithms",

abstract = "Oil production enhancement in intelligent fields is a nonlinear, high-dimension, conditional optimisation problem with a computationally expensive objective function. Stochastic estimated-gradient-based optimisation algorithms have proved to be an effective tool to solve this class of problems. Solutions to this challenging problem using the Simultaneous Perturbation Stochastic Approximation (SPSA) and Ensemble-based Optimisation (EnOpt) algorithms are evaluated and their performance compared for multiple case studies. Optimisation guidelines will also be provided.",

author = "\{Haghighat Sefat\}, Morteza and Khafiz Muradov and Elsheikh, \{Ahmed H\} and Davies, \{David Roland\}",

year = "2014",

month = jul,

language = "English",

note = "20th Conference of the International Federation of Operational Research Societies 2014 ; Conference date: 13-07-2014 Through 18-07-2014",

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TY - CONF

T1 - Optimisation of Intelligent Oil Wells using Stochastic Algorithms

AU - Haghighat Sefat, Morteza

AU - Muradov, Khafiz

AU - Elsheikh, Ahmed H

AU - Davies, David Roland

PY - 2014/7

Y1 - 2014/7

N2 - Oil production enhancement in intelligent fields is a nonlinear, high-dimension, conditional optimisation problem with a computationally expensive objective function. Stochastic estimated-gradient-based optimisation algorithms have proved to be an effective tool to solve this class of problems. Solutions to this challenging problem using the Simultaneous Perturbation Stochastic Approximation (SPSA) and Ensemble-based Optimisation (EnOpt) algorithms are evaluated and their performance compared for multiple case studies. Optimisation guidelines will also be provided.

AB - Oil production enhancement in intelligent fields is a nonlinear, high-dimension, conditional optimisation problem with a computationally expensive objective function. Stochastic estimated-gradient-based optimisation algorithms have proved to be an effective tool to solve this class of problems. Solutions to this challenging problem using the Simultaneous Perturbation Stochastic Approximation (SPSA) and Ensemble-based Optimisation (EnOpt) algorithms are evaluated and their performance compared for multiple case studies. Optimisation guidelines will also be provided.

M3 - Paper

T2 - 20th Conference of the International Federation of Operational Research Societies 2014

Y2 - 13 July 2014 through 18 July 2014

ER -

Optimisation of Intelligent Oil Wells using Stochastic Algorithms

Abstract

Conference

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