Modeling and Optimization of Desalting Process in Oil Industry

Modeling and Optimization;Chemical Engineering

Alshehri, Ali

University of Waterloo

关键词: Modeling and Optimization;    Chemical Engineering;   

Others  :  https://uwspace.uwaterloo.ca/bitstream/10012/4782/1/Alshehri_Ali.pdf

瑞士|英语

来源: UWSPACE Waterloo Institutional Repository

【 摘 要 】

Throughout a very long piping network crude oil in Saudi Arabia is sent to Gas Oil SeparationPlant called GOSP. The main objectives of the GOSP are:- Separation of the associated gas through pressure drop in two series stages one to 120psig and the other to 50 psig.- Separation of water by gravity separators called High Pressure Production Trap (HPPT),Dehydrator, Desalter and Water Oil Separator (WOSEP).- Reducing salt concentration to less than 10 PTB utilizing wash water and demulsifier.During the desalting process, the challenge is to overcome the existence of an emulsion layer atthe interface between oil and water. In petroleum industry normally emulsions encountered aresome kind of water droplets dispersed in a continuous phase of oil. In crude oil emulsions,emulsifying agents are present at the oil-water interface, hindering this coalescence process.Such agents include scale and clay particles, added chemicals or indigenous crude oilcomponents like asphaltenes, resins, waxes and naphthenic acids.Many techniques made available to gas oil separation plant operators to minimize the effect oftight emulsions. These techniques include injection of demulsifier, increasing oil temperature,gravity separation in large vessels with high retention time as well as electrostatic voltage. Fromexperience and studies these variables have been already optimized to a good extent; however,from the believe that knowledge never stop, this study is conducted targeting enhancing thedemulsifier control and optimizing the wash water rate.The objective of this study is to design an Artificial Neural Network (ANN) trained on data setto cover wide operating range of all parameters effecting demulsifier dosage. This network willbe used to work as a control black box inside the controller in which all effecting parameters areinputs and the demulsifier dosage is the controller output. Testing this control scheme showed aneffective reduction in demulsifier consumption rate compared to the existing linear method.Results also, showed that the existing control strategy is highly conservative to prevent the saltfrom exceeding the limit. The generated function from the ANN was used also to optimize theamount of fresh water added to wash the salty crude oil. Finally, another ANN was developed togenerate an online estimate of the salt content in the produced oil.