A parametric study of machine learning techniques in petroleum reservoir permeability prediction by integrating seismic attributes and wireline data

One of the recipes for the big data and artificial intelligence paradigms is multi-dimensional data integration for improved decision making in petroleum reservoir characterization. Various machine learning (ML) techniques have been applied. However, there is still ample room for improvement. This paper carries out a rigorous parametric study to investigate the comparative performance of common and sophisticated ML techniques in the estimation of the permeability of a carbonate reservoir in the Middle East. The study integrates seismic attributes and wireline data for improved permeability prediction. The effects of tuning hyperparameters on the performance of the techniques are also studied. The techniques are evaluated on two versions of the seismic-log integrated data: globally-averaged and depth-matched. The results show that using the depth-matched dataset gives marginal improvement on the permeability prediction as reflected in the higher correlation coefficient and lower errors than the globally-averaged version. The outcome of this study will assist users of ML techniques to make informed choices on the appropriate ML techniques to use in petroleum reservoir characterization for more accurate predictions and better decision making especially when faced with limited and sparse data.