Porosity is an important petrophysical property of reservoir rocks to qualify oil and gas accumulations. Porosity can be estimated from two sources, core analysis and wireline logs. The core analysis is performed in the laboratory and provides a discrete direct measurement, while the processing and interpretation of wireline logs give an indirect porosity information, but continuous along the borehole depth. From wireline logs, porosity may be calculated using the density log. However, lack of matrix density of the reservoir may produce unrealistic porosity values. This work aims to make the integration of core analysis and density log using a backpropagation artificial neural network to map density values in porosity values from core analysis. The porosity calculation using this artificial neural network, which has as input the density log allows a lower cost process to acquire this important petrophysical information. The applicability of this methodology is verified using porosity values from conventional core analysis from one borehole drilled in Namorado oil field, Basin of Campos, Brazil. For the case appraised here, the artificial neural network exhibits compatible results.
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