Comparative analysis between different artificial based models for predicting static Poisson’s ratio of sandstone formations

The mechanical behavior of the rocks can greatly assist in optimizing the drilling operation and well completion design. This behavior can be expressed in terms of Young’s modulus and Poisson’s ratio. Reliable Poisson’s ratio values can be estimated experimentally from core measurements however this method consumes time and economically ineffective. This study involved the development of two models using neural networks (ANN) and fuzzy logic to estimate static Poisson’s ratio (PR_static) of sandstone rocks based on the conventional well-log data including bulk density and sonic log data. The models are developed using 692 of actual data core data and the corresponding logging data. The models are optimized after several runs of the different combinations of the available tuning parameters. The results showed that the neural network model outperformed the model developed using the fuzzy logic tool and yielded a great match with correlation coefficient (R) of 0.98 and AAPE of 1.5% between the predicted and measured PR_static values. The developed ANN-based model is then validated using unseen data from another well within the field to estimate PR_static over a certain interval. The validation process results showed a significant agreement with correlation coefficient (R) of 0.95 between the predicted PR_static values and the actual measured ones. The results demonstrated the ability of the developed model to provide a continuous profile of static Poisson’s ratio (PR_static) whenever the petrophysical logging data are available.