Lithofacies prediction from conventional well logs using geological information, wavelet transform, and decision tree approach in a carbonate reservoir in southeastern Brazil

This work proposes a workflow through numerical simulations to determine the link between lithofacies and electrofacies. The correspondence between them was studied using the gamma-ray, resistivity, neutron porosity, bulk density, and transit time geophysical well logs. The lithofacies information, derived from conventional cores analysis, was used as the target in the process. On the other hand, the classification of lithofacies using logs has been performed by several authors using a wide variety of approaches. Thus, records from boreholes A10 (reference) and A03 (blind test) of a carbonate reservoir in Campos Basin, Southeastern Brazil, were used to develop this study. The cross-plot between the bulk density and gamma-ray logs suggests five electrofacies, which coincide with the five lithofacies proposed in the literature. Throughout the work, Pearson's correlation coefficient (R) and the mean square error (MSE) were used as statistical parameters to assess the goodness of fit. The gamma-ray electrofacies were better adjusted to the lithofacies of each well (R = 0.74 and 0.65, MSE = 0.63 and 1.36), which is recognized by publications that show this log with the best performance in identifications of this type. After that, the wavelet transforms of all normalized logs showed only a good correlation between lithofacies and the transformed signal to the gamma-ray log (R = 0.83 and 0.64, MSE = 0.01 and 0.04). This result also indicates that the electrofacies derived from the normalized and wavelet-transformed gamma ray log exhibited better results than those resulting directly from the simple gamma ray log in both wells compared to the lithofacies. Both results also show that gamma-ray electrofacies are the best input to the simulation process. The decision tree approach modeling then performed the estimates only with the normalized and wavelet-transformed gamma ray log, revealing that this method worked reasonably well in both reference and blind test wells (R = 0.79 and 0 0.63, MSE = 0.02 and 0.06). Outcomes were better with all normalized and wavelet transformed logs for both wells (R = 0.93 and 0.95, MSE = 0.006 and 0.007), with the blind test well having better R but a slightly higher MSE. So, this approach allows to derive lithofacies from electrofacies when a series of steps are taken: a) select the logs that have the most relevant lithological information; b) normalize lithology and logs on the same basis; c) transform the logs by wavelet, and d) use these results as input to the inverse process with the decision tree algorithm.