Analysis of porosity, stratigraphy, and structural delineation of a Brazilian carbonate field by machine learning techniques: A case study

The upscaling of well logs has many challenges, especially for carbonate rocks. Primary among them is the suitable choice of seismic attributes to be integrated with well information, whose random combination can produce artifacts of rock properties. To solve this problem, we have developed an alternative hybrid method to estimate well-log data from seismic attributes, associating the seismic attributes choices with the genetic algorithm and artificial neural network multilayer perceptron ability to predict neutron porosity. Thirty-seven seismic attributes were extracted along 12 wellbores from an Albian offshore carbonate reservoir of the Campos Basin. From these attributes, three were selected: 3D mix, structure-oriented median-filtered amplitude, and acoustic impedance. From this set of seismic data, we used the first two attributes to estimate neutron porosity in the reservoir seismic area. As a result, we obtained a 3D map of well-log information at the seismic scale. In the 3D map, it is possible to identify the main structural and architectural elements of the field. These results corroborate the interpretation of bioconstruction, lagoons, and carbonate shoals, and the delimitation of a tidal channel at the top of a reservior, using neutron porosity.