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Manuel Correia : Reservoir Geoengineering : Best Researcher Award 

👨‍🏫Manuel Correia  an Researcher as Reservoir Geoengineer at  UNISIM (CEPETRO / UNICAMP / BRASIL) – Research Group in Numerical Simulation and Management of Petroleum Reservoirs , stands as a distinguished academic and researcher in the domain of Energy. Holding a PhD from Petroleum Engineering at the State University of Campinas ,  their professional journey exemplifies dedication and expertise. 📚

🌐 Professional Profiles:

Work Experience  🌟:

Manuel’s professional journey spans a breadth of roles, primarily in petroleum research groups. As a Reservoir Geoengineer, their contributions to UNISIM’s Petroleum Research Group are substantial. From developing methodologies for integrating naturally fractured carbonate reservoirs to leading projects funded by industry giants like Petrobras and Shell Brazil, their work focuses on 3D geological modeling, uncertainty analysis, and reservoir simulation. Moreover, Manuel’s responsibilities extend to guiding students, writing technical reports, and proposing new initiatives, showcasing their leadership and research acumen.

Education and Training  🎓:

Their academic background is impressive, with a Ph.D. in Petroleum Engineering from the State University of Campinas. Manuel’s doctoral work concentrated on integrating fractured carbonate reservoirs into reservoir simulation, a vital aspect of their current expertise. Prior to this, their Master’s thesis at Aveiro University in Portugal focused on estimating magnetic source depths using aeromagnetic data. Their geological engineering background further bolsters their expertise in this field.

Expertise Snapshot  🧬:

Development and implementation of methodologies for fractured carbonate reservoirs. Project management for Petrobras and Shell Brazil, focusing on field development and simulation. Collaboration with industry leaders like Petrobras and Shell on critical geological heterogeneities in reservoirs. Expertise in 3D geological modeling, uncertainty analysis, and reservoir simulation. Significant academic engagement, guiding students and contributing to examining committees.

Training Offered 💡:

Their expertise extends beyond academic pursuits, evident in the training sessions they’ve conducted. From their role as an assistant teacher at the University of Campinas in Brazil to instructing in Petrobras-Galp Geoengineering Courses, their training experiences span diverse locations and subjects, including rock properties, geoengineering, and more. These sessions, varying in duration and scope, highlight their commitment to sharing knowledge across different domains.

Training Received:

Their quest for learning is ceaseless. Recent training sessions at Total Energies Professe Associés, the University of Campinas, Stanford University (via Coursera), and the Data Science Academy showcase their dedication to continuous improvement. From rock properties and core analysis to machine learning and Python fundamentals for data analysis, they’ve immersed themselves in multifaceted learning experiences. Their pursuit of knowledge extends from on-site programs to comprehensive online courses, reflecting a thirst for diverse knowledge domains.

Achievements and Skills 📚:

Their recognition by CEPETRO at the University of Campinas in the PhD category, coupled with an array of technical competencies ranging from geostatistical modeling (Petrel) to flow simulation (Eclipse, CMG) and programming in Python, illuminates a rich tapestry of skills. Their advanced proficiency in various software tools underscores a versatile skill set, enhancing their capacity to contribute significantly in their field.

Development of a special connection fracture model for reservoir simulation of fractured reservoirs Paper Published in 2019  Cited by 10

Unisim-iii: Benchmark case proposal based on a fractured karst reservoir  Paper Published in 2020  Cited by 13

Two-stage scenario reduction process for an efficient robust optimization Paper Published in 2020 Cited by 4

Using an integrated multidimensional scaling and clustering method to reduce the number of scenarios based on flow-unit models under geological uncertainties Paper Published in 2020 Cited by 8

Developing a workflow to select representative reservoir models combining distance-based clustering and data assimilation for decision making process Paper Published in 2020 Cited by 19

Scenario reduction methodologies under uncertainties for reservoir development purposes: distance-based clustering and metaheuristic algorithm Paper Published in 2021 Cited by 5

Integrated approach to improve simulation models in a deep-water heavy oil field with 4D seismic monitoring Paper Published in 2023 Cited by 1

Integrated approach to improve numerical and geostatistical performance on a naturally fractured carbonate reservoir Published in 2023

Special Connections for Representing Multiscale Heterogeneities in Reservoir Simulation Published in 2023

Manuel Correia’s professional journey demonstrates an unwavering commitment to advancing knowledge in reservoir engineering, evident through their research, collaborations with industry leaders, and academic contributions.



Manuel Correia | Reservoir Geoengineering | Best Researcher Award

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