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. [Received: 7 September 2014; Accepted: 30 September 2015]
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