Artificial neural networks to support petrographic classification of carbonate-siliciclastic rocks using well logs and textural information

• Petrographic classification is based on a mineralogical, textural and elastic data set.
• The WEKA tool allowed us to evaluate the data set and the relevance of attributes.
• A back-propagation training algorithm was applied in this study.
• The ANN results achieved good agreement with the petrographic class prediction.
• The methodology is helpful to support decisions and perform rock characterization.

Petrographic class identification is of great importance to petroleum reservoir characterization and wellbore economic viability analysis, and is usually performed using core or geophysical log analysis. The coring process is costly, and well log analysis requires highly specific knowledge. Thus, great interest has arisen in new methods for predicting the lithological and textural properties of a wide area from a small number of samples. The artificial neural network (ANN) is a computational method based on human brain function and is efficient in recognizing previously trained patterns. This paper demonstrates petrographic classification of carbonate-siliciclastic rocks using a back-propagation neural network algorithm supported by elastic, mineralogical, and textural information from a well data set located in the South Provence Basin, in the southwest of France. The accuracy of the testing suggests that an ANN application offers an auxiliary tool for petrographic classification based on well data, specifically for prediction intervals in wells that have not been sampled or wells adjacent to sampled wells.