We apply approaches and methods of computer science to gain a better and new insights into understanding the complex structures and flow patterns in petroleum reservoirs. Oil and gas exploration, development and production (E, D&P) involve complex tasks comprising workflows with pipelined processes that require the processing of a large volume of variables related to multidisciplinary data sources (Figure below). Our research is focused in the interactive modeling, visualization and analytics of the multi-{scale, modal, dimensional} datasets representing the static and dynamic characteristics of petroleum reservoirs across different stages of E, D&P.

The scope of our work has strong interdisciplinary aspects. Ideas connected to interactive modeling, visualization and analytics of petroleum reservoir models and datasets are related to computer science - i.e. scientific and information visualization, computer graphics, visual analytics, interaction design, simulation, high-performance computing -, mathematics and physics, and domain-related - i.e. geophysics, geology, geostatistics, reservoir and production engineering, among others.


Our work is on fundamental and applied research and development of topics associated to three main themes: (1) visual reservoir data modelling & knowledge representation; (2) reservoir data visualization & analytics; and (3) visual reservoir data interaction techniques & technologies.

Our team closely collaborates with researchers, scientists and engineers from both industry and academia representing diverse disciplines. Our group provides a variety of topics and industry case-studies for students within our three main research themes.

Core goals behind the three main research themes and the resulting software prototypes include:

  • Provide visual representations and analytics that reflect and express the available information, the level of uncertainty and visualization requirements for processes at different stages of E, D&P (Figure in the left);
  • Link static and dynamic reservoir modelling, visualization and analytics reflecting on all the E, D&P stages;
  • Improve communication between professionals and stakeholders involved in field development and decision-making;
  • Guide complex processes reflecting and expressing the level of data uncertainty during interactive model building, interpretive visualization and analysis of reservoir datasets;
  • Leverage of existing work processes, expressing the level of uncertainty over a range of features from visual anomalies to detailed interpretations and reservoir dynamics.