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Oil and gas production in FPSOs (floating, production, storage, and offloading) faces a dual challenge: meeting variation in energy demand while decreasing its negative environmental impact. The present paper integrates thermodynamic analysis of oil and gas processing plants and screening analysis to determine the most important operational parameters to lower energy demand and increase efficiency and production. Therefore, the main goals of this work are to identify the contribution of total effect of the operating parameters in an FPSO with CCUS (carbon capture, utilization and storage). Twenty-seven thermodynamic and structural design variables are selected as input parameters for the sensitivity analyses. Four machine learning-based screening analysis algorithms (smooth spline-analysis of variance (SS-ANOVA), PAWN, gradient boosting machine (GBM), and Morris are adapted to achieve the following objectives: 1) overall power consumption of FPSO, 2) CO2 removal efficiency of CCS, 3) power consumption of CCS, and 4) total oil production. The results showed that the optimal operating pressure parameters of CCS significantly reduces the energy consumption and exergy destruction of the key main and utility plants. Further, the total power consumption, CCS efficiency, and CCS power consumption are much more sensitive to CO2 content of fluid reservoir than GOR, whereas the total oil production is influenced only by the GOR content. Last, for scenarios with high CO2 or GOR content, the effect of design variable interactions is decisive in changing the separation efficiency and/or the compression unit performance.

Oil and gas industries have high carbon dioxide (CO2) emissions, which is a great environmental concern. Monoethanolamine (MEA) is widely used as a solvent in CO2 capture and storage (CCS) systems. The challenge is that MEA–CCS itself is an energy-intensive process that requires optimum configuration and operation, and numerous design parameters and heat demands must be considered. Thus, the current work evaluates the energy distributions and CO2 removal efficiency of a CCS installed in floating production storage and offloading units under different operating conditions of a power- and heat-generation hub. The optimization procedures are implemented using highly accurate surrogate models for the following responses: 1) overall power consumption of CCS, 2) CCS separation performance, and 3) CCS heating and cooling demands. The input variables considered in the present research include the following: 1) the exhaust gas compositions and mass flow rate, 2) the operating pressure and temperature parameters of CCS and the injection compression unit, 3) the structural parameters of absorber and stripper columns, and 4) MEA solution parameters. The optimum CCS configuration significantly reduces the total heating and cooling demands by 62.77% (7 × 106 kW) and the overall power consumption by 8.65 % (1.8 MW), and it increases the CCS separation performance by 4.46% (97.46%) and mitigates the CO2 emissions of proper CCS by 1.02 t/h compared with conventional operating conditions.

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