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.

Reservoirs are fundamental infrastructures for the management of water resources. Constructions around them can negatively impact their quality. Such unauthorized constructions can be monitored by land cover mapping (LCM) remote sensing (RS) images. In recent years, deep learning (DL) has attracted considerable attention as a method for LCM the RS imagery and has achieved remarkable success. In this paper, we develop a new approach based on DL and image processing techniques for man-made object segmentation around the reservoirs. In order to segment man-made objects around the reservoirs in an end-to-end procedure, segmenting reservoirs and identifying the region of interest (RoI) around them are essential. In the proposed two-phase workflow, the reservoir is initially segmented using a DL model. A post-processing stage is proposed to remove errors such as floating vegetation. Next, the RoI around the reservoir (RoIaR) is identified using the proposed image processing techniques. Finally, the man-made objects in the RoIaR are segmented using a DL architecture. To illustrate the proposed approach, our task of interest is segmenting man-made objects around some of the most important reservoirs in Brazil. Therefore, we trained the proposed workflow using collected Google Earth (GE) images of eight reservoirs in Brazil over two different years. The U-Net-based and SegNet-based architectures are trained to segment the reservoirs. To segment man-made objects in the RoIaR, we trained and evaluated four possible architectures, U-Net, FPN, LinkNet, and PSPNet. Although the collected data has a high diversity (for example, they belong to different states, seasons, resolutions, etc.), we achieved good performances in both phases. The highest achieved F1-score for the test sets of phase-1 and phase-2 semantic segmentation stages are 96.53% and 90.32%, respectively. Furthermore, applying the proposed post-processing to the output of reservoir segmentation improves the precision in all studied reservoirs except two cases. We validated the prepared workflow with a reservoir dataset outside the training reservoirs. The F1-scores of the phase-1 semantic segmentation stage, post-processing stage, and phase-2 semantic segmentation stage are 92.54%, 94.68%, and 88.11%, respectively, which show high generalization ability of the prepared workflow.

Identifying new constructions in large cities can be done simply, quickly, and at low cost by applying image processing techniques on time-series remote sensing (RS) images and producing land cover maps. In recent years, object-based (OB) image classification has attracted significant attention as a method for land cover mapping. This method consists of two steps: segmentation and classification. In this research, we will develop a new approach based on image processing techniques to be utilized in the OB classification method for the analysis of urban growth. In this approach, we propose a multi-phase segmentation for the segmentation step and a rule-based method for the classification step. Besides speeding up the process of OB classification, the accuracy of the final preliminary results is another advantage of the proposed approach. Moreover, for collecting RS images, a two-zoom level data collection is adopted using an open source RGB RS database. An important application of analyzing RS images is the detection of non-authorized communities formation around water reservoirs. Therefore, in our preliminary experiments, we selected three different regions around Guarapiranga reservoir in Sao Paulo, Brazil, for collecting our RS images.

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.

I am also thankful to Professor Jurandir Itizo Yanagihara from PME/Poli for working in collaboration and for permission to use his laboratory. I would like to thank CAPES and FAPESP for their support during my Ph.D.Words can not express my gratitude to my Mother. I am deeply indebted to her and also to my father and sister, who I could not terminate this journey without them. Thanks and apology from the depth of my heart to my little son, Mostafa, for spending the hours that was owned to him on working on my thesis. Finally, my profound thanks go to my spouse, Ali, for his encouragement, understanding, and consistent support.