The main goal was to identify and classify the environmental sensibility level, taking into account the tide conditions and season. It is the most complete and detailed study about this subject in brazil for now.

Environmental sensibility map from the ecosystem located in the South coast of Bahia. The study has as a result, 22 maps, consolidating several coast types, as mangrove forests, beaches, river margins and others. In case of emergency scenario in the region, these information are available to the Society.

The general aim is to develop a methodology that helps understand the coast process and define the mangrove forests vulnerability on the Pará-Maranhão and Foz do Amazonas basins (the biggest mangrove forest area on the planet).

A Project with a various activities, as highlights: field work to describe the area, remote sense to describe the area, driers release to confirm the hydrodynamic modelling results and the development of a regional drift oil simulation tool.

Construction of a test structure to evaluate the performance of Submersible Centrifugal Pump operating with ultra viscous oil.
– Modeling of test results;
– Characterization of heavy oil and oil-water emulsions study;
– Heavy oil and water separation study;
– Rheological study and mathematical model for restarting heavy oil flow at low temperatures;
– Development of a data acquisition and control system to automate Submersible Centrifugal Pump operation.

Develop and experimentally validate a methodology to analyze, using computational fluid dynamics, of the preservation procedure of heavy oil production lines through diesel injection in offshore conditions.

Develop methodology and computational tools for the generation of three-dimensional geomechanical models based on the integration of 3D seismic data information (with wide azimuth coverage) and wells.

Develop study and methodology based on seismic attributes (geometric and spectral) to calculate directions and relative magnitudes of horizontal pressure.

This project aims to research machine learning methods to support the work of seismic interpretation. Thus, we have as secondary goals:
– Attribute analysis and groupings with machine learning;
– Automation of horizon detection;
– Analysis of regions with and without wells;
– Use of knowledge extracted from wells already drilled in the analysis of seismic data from areas not explored yet.

Calculate rock properties by digital technique from tomographic images extracted from Atlanta and Oliva Fields, with the main goals:

– Study the relationship of the morphological properties of grain and pores with the rock petrophysical properties;

– Calculate the porous media compressibility by determining the petrophysical properties at various stages of a physical compression and applying the necessary corrections (tomographic images are obtained for various phases of compression).

Develop a computational tool for analysis of transhipment systems, with the main goals:
– Study safer and more economical transport options than the use of helicopters, through offshore vessels;
– Carry out a case study of a real field using the developed tool. This tool, besides allowing the sizing of the transhipment system, has a great potential of its use in situ to predict the system behavior and evaluate the operational safety.