http://hdl.handle.net/10174/37667 Thu, 28 May 2026 10:40:24 GMT 2026-05-28T10:40:24Z http://hdl.handle.net/10174/42049 Title: Fracture Network Characterization in a geological complex considered for carbon dioxide (CO₂) injection in the offshore of the Lusitanian Basin Authors: Barata, Madalena; Caeiro, Maria Helena; Carneiro, Júlio; Pereira, Pedro; Martins, José Miguel; Ribeiro, Carlos Abstract: This study investigates fracture network characterization in a geologically complex offshore sector of the Lusitanian Basin to evaluate its suitability for long-term carbon dioxide (CO₂) storage in deep saline aquifers (DSA). As Carbon Capture and Storage (CCS) becomes increasingly important for climate change mitigation, ensuring caprock integrity is essential to prevent CO₂ leakage and maintain storage security. Using the 3D Cabo Mondego seismic dataset as an analogue, the study performed fracture interpretation and statistical characterization of fracture systems within the sealing formation. Key fracture attributes—including length, height, orientation (strike and dip), aspect ratio, and fracture intensity—were analysed to assess fracture distribution and their potential influence on storage performance. The characterization framework enabled evaluation of possible leakage pathways and supported assessment of the feasibility and integrity of geological CO₂ storage under existing data constraints. Conducted within the scope of the PilotSTRATEGY project, this work contributes to improving understanding of fracture behaviour in deep saline aquifers and provides important input for modelling the long-term evolution and containment of injected CO₂ plumes in European storage sites. Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/10174/42049 2025-01-01T00:00:00Z http://hdl.handle.net/10174/42036 Title: Optimization Under Geological Uncertainties for CO2 Injection in CCUS: A Case Study from the Lusitanian Basin Authors: Khudhur, Karwan; Pereira, Pedro; Carneiro, Júlio; Goldman, Matthew; Blin, Gwendoline; Santos, Mário; Casacão, João Abstract: This study presents a workflow for optimizing CO₂ injection under geological uncertainty for Carbon Capture, Utilization, and Storage (CCUS), using the Q4-TV1 prospect in the offshore Lusitanian Basin (Portugal) as a case study. The approach integrates a high-resolution 3D static geological model with dynamic reservoir simulation through the Big Loop™ framework and Bayesian optimization techniques. The objective was to maximize total CO₂ injection over a 30-year operational period while ensuring long-term containment and minimizing leakage risks associated with intersecting faults and a legacy well. Geological uncertainty was represented through 16 variable subsurface parameters, including porosity–permeability relationships and fault behaviour. A total of 948 simulation scenarios were evaluated, with unsuitable scenarios excluded where plume migration approached critical geological features. The selected optimal scenario identified a well location and perforation interval that achieved an estimated maximum injection capacity of approximately 24 million tonnes of CO₂, with a probabilistic median (P50) of 8.8 million tonnes. Extended simulations over 1,000 years demonstrated sustained plume containment away from faults and the legacy well. Sensitivity analysis showed that injection performance was primarily controlled by bottom-hole pressure targets and perforation depth. The results demonstrate that combining stochastic geological modelling with Bayesian optimization provides a robust and scalable framework for improving the safety, efficiency, and reliability of CO₂ storage projects under uncertainty. Sat, 31 May 2025 23:00:00 GMT http://hdl.handle.net/10174/42036 2025-05-31T23:00:00Z http://hdl.handle.net/10174/41699 Title: Coupled Fire-Atmosphere modelling: Some Findings and current challenges based on Portuguese case studies Authors: Salgado, Rui; Couto, Flavio Tiago; Campos, Cátia; Santos, Filippe L. M.; Baggio, Roberta; Filippi, Jean-Baptiste Abstract: High-resolution atmospheric models and their coupling with fire propagation models are powerful tools for better understanding the behaviour of rural fires and their effects on the atmosphere. Portugal is one of the European countries with most burned area and numerous ignitions. In 2017, Portugal was affected by several megafires with burned areas larger than 10 000 hectares, some of which led to the formation of convective clouds: pyro-cumulus (pyroCu) and pyro-cumulonimbus (pyroCb). These phenomena can significantly influence the evolution of fire fronts by altering surface winds and raising spread rates, creating extra difficulties for firefighting and increasing burned areas. These rural fires of 2017 were the starting point for studying the atmospheric environment that favours ignitions and fire spread and the effects of fires on the atmosphere, particularly the generation of pyroconvection. In this study, Pedrogão Grande (June 17) and the Quiaios (October 15) mega-fires are chosen as case studies for numerical simulations with the MesoNH atmospheric model coupled with the ForeFire fire propagation model. The simulations show the development of pyroCu and pyroCb clouds produced by intense convective updrafts due the heat fluxes generated generated during combustion. The simulations have improved our understanding of the evolution of the fire environment and the role played by downbursts originating from pyroCb clouds and provided insights about numerical modelling of pyroconvective clouds using Meso-NH/ForeFire simulations. Finally, we use the results obtained in this work to illustrate the current state-of-the art of coupled fire-atmosphere modelling, its limitations and challenges. Sun, 18 May 2025 23:00:00 GMT http://hdl.handle.net/10174/41699 2025-05-18T23:00:00Z http://hdl.handle.net/10174/41697 Title: Live fuel moisture content estimation using remote sensing and numerical modelling approach Authors: Santos, Filippe L. M.; Couto, Flavio Tiago; Monteiro, Maria José; Ribeiro, Nuno Almeida; Le Moigne, Patrick; Salgado, Rui Abstract: Climate change has led to an increase in wildfires, particularly on the Iberian Peninsula. In recent years, Portugal suffered several devastating wildfires, such as in 2003, 2005, and 2017. In 2024, the situation repeated itself, with several large-scale wildfires occurring in the Central region during September. Wildfires are directly related to the availability of combustible material, weather conditions, and ignition factors. Currently, land use and occupation management is a way to reduce wildfires. Identifying areas with higher fuel availability is essential for wildfire prevention in this context. This work aims to improve the live fuel moisture content (LFMC) representation across mainland Portugal, using remote sensing and numerical modelling through a machine learning approach. First, a product was developed to estimate LFMC for Portugal based on remote sensing, through satellite imagery, and machine learning (LFMC-RS). Next, numerical simulations were performed with the MESO-NH (research) and AROME (operational) models, non-hydrostatic mesoscale atmospheric models, producing forcing files to initialize the SURFEX surface model. All output variables from the SURFEX model were utilized as predictors in a machine learning classifier to estimate the LFMC (LFMC-SFX). These results are useful for understanding the FMC spatiotemporal variability in Portugal The research received financial support from the Foundation for Science and Technology, I.P. (FCT) through the PyroC.pt initiative (Ref. PCIF/MPG/0175/2019) along with a PhD Grant (2022.11960.BD). This work also was cofounded by the European Union through the European Regional Development Fund (FEDER) in the framework of the Interreg VI-A España-Portugal (POCTEP) 2021–2027, FIREPOCTEP+ (0139_FIREPOCTEP_MAS_6_E). Sun, 18 May 2025 23:00:00 GMT http://hdl.handle.net/10174/41697 2025-05-18T23:00:00Z