Development of a time-dependent economic method with start time consideration to optimise gas-lift allocation and scheduling

S. Omid H. Miresmaeili, Peyman Pourafshary, Farhang Jalali Farahani

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The gas lift allocation optimisation is an important operational problem. In this paper, we present a method to optimise the lift gas allocation profile and determine the best time to start the gas-lift operation for each well. To tackle the nonlinear optimisation, an estimation of distribution algorithm (EDA) is employed based on Gaussian Bayesian networks and Gaussian kernels and the results are compared with those obtained by particle swarm optimisation (PSO) and genetic algorithms (GAs). Gas-lift performance for all the wells along with estimated cumulative production data are correlated over time to develop a model to show the field production behaviour as a function of the gas injection rates and initiation parameters. The developed model is coupled with an economic model to maximise the net present value of the gas-lift process for the field.

Original languageEnglish
Pages (from-to)41-59
Number of pages19
JournalInternational Journal of Oil, Gas and Coal Technology
Volume13
Issue number1
DOIs
Publication statusPublished - Jan 1 2016
Externally publishedYes

Fingerprint

Gas lifts
Scheduling
Economics
Bayesian networks
Particle swarm optimization (PSO)
Genetic algorithms

Keywords

  • Economic optimisation
  • EDA
  • Estimation of distribution algorithm
  • Gas-lift allocation
  • Gas-lift start time
  • Gaussian network

ASJC Scopus subject areas

  • Energy(all)

Cite this

Development of a time-dependent economic method with start time consideration to optimise gas-lift allocation and scheduling. / Miresmaeili, S. Omid H.; Pourafshary, Peyman; Farahani, Farhang Jalali.

In: International Journal of Oil, Gas and Coal Technology, Vol. 13, No. 1, 01.01.2016, p. 41-59.

Research output: Contribution to journalArticle

@article{a4a19f5f04cb468db23381ca72cd84af,
title = "Development of a time-dependent economic method with start time consideration to optimise gas-lift allocation and scheduling",
abstract = "The gas lift allocation optimisation is an important operational problem. In this paper, we present a method to optimise the lift gas allocation profile and determine the best time to start the gas-lift operation for each well. To tackle the nonlinear optimisation, an estimation of distribution algorithm (EDA) is employed based on Gaussian Bayesian networks and Gaussian kernels and the results are compared with those obtained by particle swarm optimisation (PSO) and genetic algorithms (GAs). Gas-lift performance for all the wells along with estimated cumulative production data are correlated over time to develop a model to show the field production behaviour as a function of the gas injection rates and initiation parameters. The developed model is coupled with an economic model to maximise the net present value of the gas-lift process for the field.",
keywords = "Economic optimisation, EDA, Estimation of distribution algorithm, Gas-lift allocation, Gas-lift start time, Gaussian network",
author = "Miresmaeili, {S. Omid H.} and Peyman Pourafshary and Farahani, {Farhang Jalali}",
year = "2016",
month = "1",
day = "1",
doi = "10.1504/IJOGCT.2016.078045",
language = "English",
volume = "13",
pages = "41--59",
journal = "International Journal of Oil, Gas and Coal Technology",
issn = "1753-3309",
publisher = "Inderscience Enterprises Ltd",
number = "1",

}

TY - JOUR

T1 - Development of a time-dependent economic method with start time consideration to optimise gas-lift allocation and scheduling

AU - Miresmaeili, S. Omid H.

AU - Pourafshary, Peyman

AU - Farahani, Farhang Jalali

PY - 2016/1/1

Y1 - 2016/1/1

N2 - The gas lift allocation optimisation is an important operational problem. In this paper, we present a method to optimise the lift gas allocation profile and determine the best time to start the gas-lift operation for each well. To tackle the nonlinear optimisation, an estimation of distribution algorithm (EDA) is employed based on Gaussian Bayesian networks and Gaussian kernels and the results are compared with those obtained by particle swarm optimisation (PSO) and genetic algorithms (GAs). Gas-lift performance for all the wells along with estimated cumulative production data are correlated over time to develop a model to show the field production behaviour as a function of the gas injection rates and initiation parameters. The developed model is coupled with an economic model to maximise the net present value of the gas-lift process for the field.

AB - The gas lift allocation optimisation is an important operational problem. In this paper, we present a method to optimise the lift gas allocation profile and determine the best time to start the gas-lift operation for each well. To tackle the nonlinear optimisation, an estimation of distribution algorithm (EDA) is employed based on Gaussian Bayesian networks and Gaussian kernels and the results are compared with those obtained by particle swarm optimisation (PSO) and genetic algorithms (GAs). Gas-lift performance for all the wells along with estimated cumulative production data are correlated over time to develop a model to show the field production behaviour as a function of the gas injection rates and initiation parameters. The developed model is coupled with an economic model to maximise the net present value of the gas-lift process for the field.

KW - Economic optimisation

KW - EDA

KW - Estimation of distribution algorithm

KW - Gas-lift allocation

KW - Gas-lift start time

KW - Gaussian network

UR - http://www.scopus.com/inward/record.url?scp=84982912112&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84982912112&partnerID=8YFLogxK

U2 - 10.1504/IJOGCT.2016.078045

DO - 10.1504/IJOGCT.2016.078045

M3 - Article

AN - SCOPUS:84982912112

VL - 13

SP - 41

EP - 59

JO - International Journal of Oil, Gas and Coal Technology

JF - International Journal of Oil, Gas and Coal Technology

SN - 1753-3309

IS - 1

ER -