Fault Diagnosis in Gas Lift System Using PDF Data

Abstract

Fault detection and isolation in the gas lift system were implemented assuming the gas lift variables are stochastic. Injection valve coefficient (Civ), production choke coefficient (Cpc), annulus pressure (Pa), and wellhead pressure (Pwh) were observed to show variations with faults presence. By simulating these gas lift variables as stochastic, the probability density function (PDF) data were used to generate decision functions for both the detection and isolation of the gas lift valve faults. The scheme accurately detected and isolated faults in the injection valve coefficient (Civ) and production choke coefficient (Cpc). The result of this diagnosis will aid the proper implementation of fault tolerant control in the gas lift system which will lead to its optimal operation.