Correlating Optimum Stage Pressure for Sequential Separator Systems

Abstract

Summary A study to find the optimum separators pressures of separation stations has been performed. Stage separation of oil and gas is accomplished with a series of separators operating at sequentially reduced pressures. Liquid is discharged from a higher-pressure separator into the lower-pressure separator. The set of working separator pressures that yields maximum recovery of liquid hydrocarbon from the well fluid is the optimum set of pressures, which is the target of this work. A computer model is used to find the optimum separator pressures. The model employs the Peng-Robinson equation of state (Peng and Robinson 1976) for volatile oil. The application of this model shows significant improvement of all objective functions for many oils (Hassan 2004). Mathematical correlations for estimating optimum set of pressures have been deduced from the model to provide simple and quick tools to find the optimum stages pressures. Many tests have been achieved with many volatile samples at different conditions to prove the validity of these correlations.

Applied Process Simulation-Driven Oil and Gas Separation Plant Optimization Using Surrogate Modeling and Evolutionary Algorithms

“…Campbell and Whinery [3] developed a correlation for the optimal second stage pressure in a three stage separation train with the relative molecular weight of the hydrocarbon mixture and a correlating parameter given as a function of C1-C3 content and molecular weight. In a more recent study, Al-Jawad and Hassan [4,5] developed correlations for separation trains with 2-5 stages, and the correlations provide optimal separator pressure for all separators, except the final stage. The required inputs are separator temperatures, methane and impurity content, and upstream separator pressures.…”

“…Common for [2][3][4][5][6][7][8][9] is the lack of a compression system providing condensate recycle streams i.e., these studies assume a simple straight-through process with the number of controllable variables normally not exceeding 2 to 5.…”

Applied Process Simulation-Driven Oil and Gas Separation Plant Optimization using Surrogate Modeling and Evolutionary Algorithms

“…Campbell and Whinery [3] developed a correlation for the optimal second stage pressure in a three stage separation train with the relative molecular weight of the hydrocarbon mixture and a correlating parameter given as a function of C1-C3 content and molecular weight. In a more recent study by Al-Jawad and Hassan [4,5] developed correlations for separation trains with 2-5 stages, and the correlations provide optimal separator pressure for all separators, except the final stage. The required input are separator temperatures, methane and impurity content, and upstream separator pressures.…”

“…Common for refs. [2][3][4][5][6][7][8][9] is the lack of a compression system providing condensate recycle streams i.e. these studies assume a simple straight through process with the number of controllable variables normally not exceeding 2 to 5.…”

Applied Process Simulation-Driven Oil and Gas Separation Plant Optimization using Surrogate Modeling and Evolutionary Algorithms

“…Common for refs. [2][3][4][5][6][7][8][9] is the lack of a compression system providing condensate recycle streams i.e. these studies assume a simple straight through process with the number of controllable variables normally not exceeding 2 to 5.…”