Simulation and optimization of primary oil and gas processing plant of FPSO operating in pre-salt oil field.

• Ano de defesa: 2018
• Autor(a) principal: Bidgoli, Ali Allahyarzadeh
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Análise de sensibilidade; Hybrid method; Método híbrido; Offshore oil and gas processing platform; Óleo e gás (Tratamento); Optimization; Petróleo (Processamento); Sensitivity analysis; Termodinâmica; Thermodynamic analysis
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/3/3150/tde-13122018-150547/

FPSO (Floating, Production, Storage e Offloading) plants, similarly to other oil and gas offshore processing plants, are known to be an energy-intensive process. Thus, any energy consumption and production optimization procedures can be applied to find optimum operating conditions of the unit, saving money and CO2 emissions from oil and gas processing companies. A primary processing plant of a typical FPSO operating in a Brazilian deep-water oil field on pre-salt areas is modeled and simulated using its real operating data. Three operation conditions of the oil field are presented in this research: (i) Maximum oil/gas content (mode 1), (ii) 50% BSW oil content (mode 2) and (iii) high water/CO2 in oil content (mode 3). In addition, an aero-derivative gas turbine (RB211G62 DLE 60Hz) with offshore application is considered for the heat and generation unit using the real performance data. The impact of eight thermodynamic input parameters on fuel consumption and hydrocarbon liquids recovery of the FPSO unit are investigated by the Smoothing Spline ANOVA (SS-ANOVA) method. From SS-ANOVA, the input parameters that presented the highest impact on fuel consumption and hydrocarbon liquids recovery were selected for an optimization procedure. The software Aspen HYSYS is used as the process simulator for the screening analysis process and for the optimization procedure, that consisted of a Hybrid Algorithm (NSGA-II +SQP method). The objective functions used in the optimization were the minimization of fuel consumption of the processing and utility plants and the maximization of hydrocarbon liquids recovery. From SS-ANOVA, the statistical analysis revealed that the most important parameters affecting the fuel consumption of the plant are: (1) output pressure of the first control valve (P1); (2) output pressure of the second stage of the separation train before mixing with dilution water (P2); (3) input pressure of the third stage of separation train (P3); (4) input pressure of dilution water (P4); (5) output pressure of the main gas compressor (Pc); (6) output petroleum temperature in the first heat exchanger (T1); (7) output petroleum temperature in the second heat exchanger (T2); (8) and dilution water temperature (T3). Four input parameters (P1, P2, P3 and Pc), three input parameters (P3, Pc and T2) and three input parameters (P3, Pc and T2) correspond to 96%, 97% and 97% of the total contribution to fuel consumption for modes 1, 2 and 3, respectively. For hydrocarbon liquids recovery of the plant: Four input parameters (P1,P2,P3 and T2), three input parameters (P3, P2 and T2) and three input parameters (P3, P2 and T2) correspond to 95%, 97% and 98% of the total contribution to hydrocarbon liquids recovery for modes 1, 2 and 3, respectively. The results from the optimized case indicated that the minimization of fuel consumption is achieved by increasing the operating pressure in the third stage of the separation train and by decreasing the operating temperature in the second stage of the separation train for all operation modes. There were a reduction in power demand of 6.4% for mode 1, 10% for mode 2 and 2.9% for mode 3, in comparison to the baseline case. Consequently, the fuel consumption of the plant was decreased by 4.46% for mode 1, 8.34% for mode 2 and 2.43% for mode 3 , when compared to the baseline case. Moreover, the optimization found an improvement in the recovery of the volatile components, in comparison with the baseline cases. Furthermore, the optimum operating condition found by the optimization procedure of hydrocarbon liquids recovery presented an increase of 4.36% for mode 1, 3.79% for mode 2 and 1.75% for mode 3 in hydrocarbon liquids recovery (stabilization and saving), when compared to a conventional operating condition of their baseline.