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Ī seismic attribute is any direct or indirect information obtained from the seismic data through mathematical calculation and/or logical reasoning.

Integrated quantitative interpretation is used to estimate reservoir properties, obtained through seismic amplitudes and seismic attributes. In particular, the integration of well logs with seismic data is important in order to obtain some models with better vertical and horizontal resolutions, since well logs have a very restricted area and a better vertical resolution when compared to seismic data however, seismic data presents a better horizontal resolution and covers a larger area. For this reason, different types of data are used, such as geophysical well logs, and seismic, petrophysical, in addition to geological models, in order to predict reservoir properties such as porosity, lithology, and fluid saturation. Accurate characterization reduces the risk of drilling a dry well, as well as exploration and development costs. Reservoir characterization has become increasingly important to hydrocarbon exploration. Although isolated peaks of maximum porosity are observed, spatial patterns depicted in the model are associated with geological features such as different porosity types and cementation degree. Porosity values increase from southwest to the northeast portion, and lower values are found at depths related to the traced horizons. The correlation coefficient and the error of the estimated values with respect to the actual values extracted along the wells are equal to 0.90 and 2.86%, respectively. In the second main stage, predictions of reservoir porosity values were obtained, as well as a 3D model, through the designed ANN. The estimated porosity values range from 5 to 30%. In the first main stage of the study, horizons were traced by following continuous seismic events on seismic sections, along depths between top and base of the reservoir. The reservoir is composed of Albian carbonates. We have calculated and interpreted a 3D porosity model of a reservoir through the integration of 3D seismic data with geophysical well logs using an artificial neural network (ANN).