High Resolution Petroleum Systems Modeling
in the Northern Campos Basin,
Brazil: Prediction of Hydrocarbon Composition and Phase Behavior
Robert G. Tscherny1, Juliano M. Macedo2, B.
Wygrala3, and M. R. Mello4
1 IES Integrated Exploration Systems, Aachen, Germany
2 Petroinsight, Rio de Janeiro, Brazil
3 Integrated Exploration Systems, Aachen, Germany
4 HRT & Petroleum, Rio de Janeiro, Brazil
Petroleum Systems Modeling
(PSM) tools can predict hydrocarbon
composition, phase behavior and properties (e.g. API gravities and GOR's). The
economic risk related to these predictions is a key aspect of PSM and therefore
of great importance during the decision-making process. 3-phase, n-component
petroleum migration
modeling
methods - in which phase and property calculations
are made using flash calculations - are an essential requirement for improved
predictions of petroleum phases and properties. Simple phase/component models,
e.g. black-oil models cannot accurately handle the wide range of temperature-,
pressure- and component mixture ratios that occur in Petroleum Systems. Recent
advances in the development of kinetic parameters comprehend the Petroleum Type
Organofacies Concept in which the organofacies are directly related to major
petroleum types. This new concept is an extension of the Organic Facies Concept
where the kinetic description (abundance and composition) is solely related to
the depositional setting. Therefore, the implementation of the Petroleum Type
Organofacies Concept facilitates modern PSM-tools to predict hydrocarbon
composition and phase behavior more precisely. A 3D Exploration model of the
northern Campos (including geological, geochemical and geophysical data) is used
to illustrate, on the one hand, data requirements and, on the other hand, the
applied workflow for hydrocarbon composition prediction and phase behavior
related quality assessment. The Campos Basin provides the ideal framework for
this integrative approach since numerous sets of geochemical data allow insights
into the degree of maturation, mixture, source type. This will provide useful
calibration with compositional models and “quality” refers to the degree of
confidence of the actual data.
AAPG Search and Discovery Article #90039©2005 AAPG Calgary, Alberta, June 16-19, 2005