Intelligent prediction of optimum separation parameters in the multistage crude oil production facilities

Abstract

To obtain the high-quality crude oil from the surface processing plants, oil and gas separation plants parameters need to be optimized, by minimizing the intermediate components, flash from the crude oil during primary and secondary separation processes. The aim of this paper is to present an accurate methodology for predicting optimized separation parameters in the multistage crude oil production unit. The new proposed methodology determines the optimum pressures of separators in different stages of separation and consequently optimizes the operating conditions. A dynamic simulator is used to generate the data set for a designed production facility. Then, an optimization algorithm is used to build an optimum artificial neural network model to predict the optimum operating conditions that will maximize the liquid recovery. The ultimate objective of this work is to have an advisory system for optimizing liquid recovery from the production facilities.