This work presents the results of induced polarization obtained at the region of Ourilândia do Norte, PA. The 2D investigations of IP aimed to locate a disseminated copper sulfide deposit at the northwest of Sector Rio Maria, in the Carajás Mineral Province. In the aquisition of data, dipole-dipole and pole-dipole arrays were used, both with a spacing of 10 meters between the electrodes and with 10 levels of depth. The chargeability results made possible to delineate the mineralized zone located at the investigated area, which will direct the copper extraction.

There must be an adequate integration of well and seismic data to create a reliable facies model. However, heterogeneous rocks, lack of data and inaccurate information accumulate uncertainties for model characterization. Here we seek to simulate a scenario where there are few wells and only one seismic data to create the facies model. The main goal is to estimate the predominant lithology distribution with fluid information. This strategy can help in new wells' locations and reducing the risk of false discoveries. The Norne field located on the Norwegian Sea will be used as an example for this methodology. Machine learning techniques have been commonly used for automation and task optimization processes. We show that these methods can achieve high performance when receiving proper processed data. The summarized workflow can be described in two stages: electrofacies classification on eight different wells and use of these facies as labels for classification on the field's seismic volume. The results indicate accuracy close to 90% both in well and seismic scenarios. The facies model with lithofluid information is still compared with the reservoir simulator from the same period to show the high similarity.