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Petrophysical modelling is important in reservoir characterisation. Classic geostatistical approaches have been widely used to generate 3D petrophysical properties. However, many manual interactions are required in classic approaches because they are based on the simple stationarity assumption. Various machine-learning (ML) algorithms have been developed to reduce the cycles of petrophysical modelling.
In this paper, we apply a ML petrophysical modelling algorithm combining random forests and Kriging to the Groningen gas field. Four scenarios are evaluated: (1) using different quality of well data as input, (2) using different geometrical variables as secondary variables, (3) using different seismic attributes as secondary variables, (4) upscaled gamma and density properties with a nonlinear relationship. The conclusions are (1) bad-quality well data can seriously impact the results, (2) inclusion of more geometrical variables can dramatically improve the results, (3) the impact of seismic attributes on the results heavily depends on the correlation between seismic attributes and input wells, (4) generated gamma and density properties can automatically reproduce the nonlinear relationship in wells. This case study is helpful both for better use of this algorithm in future studies and for providing a reference for evaluating other ML petrophysical modelling algorithms based on the Groningen dataset.