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Oil Quality Prediction in Deep Waters: Gulf of Guinea Applications

Previous HitGillesNext Hit Sermondadaz1, Jacques Bickert1, Denis Levaché1, Previous HitGillesTop Nicolas1, Jean Michel Gaulier2, and Arnaud Lenail-Chouteau3
1Fluids & Organic Geochemistry, Total Exploration & Production, Pau, France
2Total Exploration & Production Angola, Luanda, Angola
3Total Exploration & Production Congo, Pointe-Noire, Congo

Fluid quality prediction is a key parameter to rank, select and launch exploration and appraisal projects.

This topic is critical in deep offshore environment where down hole viscosities and productivities strongly impact the economics.
Several phenomena can lead, by alteration, to a specific fluid composition. They are individually known but their modelling is still difficult and their relative contributions are still questionable.

Basic questions are still disputed (biodegradation kinetics compared to reservoir charging, impact of secondary gas produced by biodegradation, etc…).
Several approaches have been tested to better predict oil quality (API, GOR and specially viscosity):
- Prediction method using analogues
- Rules of thumb coming from data bases
- Prediction by use of temperature impact
- Prediction method using modelling
- Prediction method using data base and statistical tools.
It is obvious that a single dimension approach is inadequate when a statistical tool is used. So, two solutions can be proposed to improve the accuracy of the oil quality prediction: a statistical approach based on a neuronal network method or a combined approach based on:
- An adequate data base
- A preliminary analysis and identification of main phenomena and parameters
- A statistical approach
This last method is commonly used in Total in the Lower Congo Basin for Tertiary reservoired fluids in Exploration and Appraisal phases.
Several examples from Congo and Angola will be presented to illustrate the Total approach in term of fluid prediction.

AAPG International Conference and Exhibition, Cape Town, South Africa 2008 © AAPG Search and Discovery