  With recent advances in artificial intelligence, machine learning (ML)
approaches have become an attractive tool in petroleum engineering,
particularly for reservoir characterizations. A key reservoir property is
hydrocarbon recovery factor (RF) whose accurate estimation would provide
decisive insights to drilling and production strategies. Therefore, this study
aims to estimate the hydrocarbon RF for exploration from various reservoir
characteristics, such as porosity, permeability, pressure, and water saturation
via the ML. We applied three regression-based models including the extreme
gradient boosting (XGBoost), support vector machine (SVM), and stepwise
multiple linear regression (MLR) and various combinations of three databases to
construct ML models and estimate the oil and/or gas RF. Using two databases and
the cross-validation method, we evaluated the performance of the ML models. In
each iteration 90 and 10% of the data were respectively used to train and test
the models. The third independent database was then used to further assess the
constructed models. For both oil and gas RFs, we found that the XGBoost model
estimated the RF for the train and test datasets more accurately than the SVM
and MLR models. However, the performance of all the models were unsatisfactory
for the independent databases. Results demonstrated that the ML algorithms were
highly dependent and sensitive to the databases based on which they were
trained. Statistical tests revealed that such unsatisfactory performances were
because the distributions of input features and target variables in the train
datasets were significantly different from those in the independent databases
(p-value < 0.05).
