Applications of Artificial Intelligence for Static Poisson’s Ratio Prediction While Drilling

Table 1

Empirical correlations for static Poisson’s ratio prediction.

Authors	Equations	Remarks
Christaras et al. [6]		A correlation between νst and νdyn was proposed using 8 samples from several rock kinds (i.e., gypsum, phonolite, basalts, granite, limestone, and andesite); The correlation coefficient (R) of this model in the specified rock types is 0.737
Feng et al. [7]		The same approach was followed to obtain a linear correlation between νst and νdyn using 18 samples from sandstone and siltstone rocks; The empirical coefficients vary with the porosities; The coefficient of determination (R^2) is 0.92 and 0.7 for modelling and testing samples, respectively
Wang et al. [8]		VP and VS were used to develop two correlations for νst prediction at different rocks; The empirical parameters change with different rock types; The R values range from 0.467–0.834 and 0.668–0.914 for the two models, respectively