Abstract

Abstract —The Albian carbonates of the Quissama Formation in the Campos Basin, southeastern Brazil, are important oil reservoirs. They make part of a carbonate platform that formed along the eastern coast of Brazil and the western coast of Africa during the Albian, which resulted in the opening of the South Atlantic Ocean. Subsequently, this reservoir was subjected to different postdepositional diagenetic processes. The present study utilized geophysical well logs to estimate the porosity of this reservoir, based on density, neutron porosity, and sonic logs. The estimates do not show good results when compared with the laboratory measurements. Then, exploring the fact that these logs are obtained with different physical principles, a multiple linear regression and an artificial neural network with Bayesian stochastic approach were applied, which resulted in a better porosity estimate. As porosity is a petrophysical parameter considered significant in the characterization of reservoirs, it was used, hereafter, to estimate permeability and water saturation of the reservoir, applying empirical equations. From there, it was not enough just to estimate the porosity, but was necessary to know what type it is. For this purpose, the concepts of the electrical formation factor, cementation coefficient, tortuosity, and anisotropy were used. With them, the zones with primary intergranular and interparticle porosity as well as secondary porosity, such as fractures, fissures, and vugs, were mapped. It was concluded that, with studies of this type, it is also possible to identify the connected and nonconnected porosities, which permits estimation of the effective porosity along the well.

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