Reservoir characterization by multiattribute analysis: the orito field case

In order to characterize the Caballos formation reservoir in the Orito field in the Putumayo basin - Colombia, a multiattribute analysis was applied to a 50 km2 seismic volume along with 16 boreholes. Some properties of the reservoir were reliably estimated and very accurate when compared with well...

Full description

Autores:
Guerrero, Jairo G.
Vargas, Carlos Alberto
Montes, Luis
Tipo de recurso:
Article of journal
Fecha de publicación:
2010
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/33828
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/33828
http://bdigital.unal.edu.co/23908/
http://bdigital.unal.edu.co/23908/2/
Palabra clave:
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
Description
Summary:In order to characterize the Caballos formation reservoir in the Orito field in the Putumayo basin - Colombia, a multiattribute analysis was applied to a 50 km2 seismic volume along with 16 boreholes. Some properties of the reservoir were reliably estimated and very accurate when compared with well data. The porosity, permeability and volume of shale were calculated in the seismic volume by at least second order multivariate polynomial. A good correlation between porosity and acoustic impedance was observed by means of crossplot analysis performed on properties measured and estimated in cores or borehole logs as well as on properties calculated in the seismic volume. The estimated property values were well behaved according to the rocks physics analysis. With the property maps generated and the geological environments of the reservoir a new interpretation of the Caballos formation was established. High correlation coefficients and low estimated errors point out competence to calculate these three reservoir properties in places far from the influence of the wells. The multiple equation system was established through weighted hierarchical grouping of attributes and their coefficients calculated applying the inverse generalized matrix method.