首页|Application of ensemble machine learning methods for kerogen type estimation from petrophysical well logs
Application of ensemble machine learning methods for kerogen type estimation from petrophysical well logs
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The current study is the first report of estimating kerogen type from petrophysical well logs implementing various machine learning techniques. The methodology is explained through a case study from the Perth Basin, Western Australia. Firstly, the statistical relationships between the petrophysical data (including gamma ray (GR), sonic (DTCO), neutron (NEUT) and density (RHOB) logs) and the Rock-Eval derived hydrogen index (HI) and oxygen index (OI) are investigated. Afterwards, the various machine learning (ML) techniques, including radial basis function (RBF) and multi-layer perceptron (MLP) artificial neural networks, random forest (RF), support vector machine (SVM) and decision tree (DT), are applied to estimate the hydrogen and oxygen index values. Additionally, the MLP network is optimized by the grey wolf optimization (GWO), genetic algorithm (GA) and particle swarm optimization (PSO). The outputs of the various ML approaches are integrated, employing both simple averaging and weighted averaging committee machines. Three statistical parameters of root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R) are employed to evaluate and compare the performance of the machine learning ensembles. In general, employing optimization algorithms on the MLP network reinforces the network's performance for the hydrogen index (HI) estimation by increasing overall R and decreasing RMSE and MAE for test data. Likewise, RMSE and MAE values of test data for the oxygen index (OI) decrease using all three optimizers, although R values decrease as well. However, GWO is the most efficient optimizer in diminishing RMSE and MAE and rising R values of test data for the hydrogen index estimation. This algorithm also provides the minimum RMSE and MAE for the oxygen index (OI) estimation. Ultimately, among the proposed intelligent approaches, the weighted averaging committee machine (WACM) provides the maximum correlation coefficient (R) and minimum errors (RMSE and MAE) for both hydrogen index (HI) and oxygen index (OI) estimating. The estimated oxygen and hydrogen indices values are then successfully employed to predict the kerogen types based on the van Krevelen diagram.
Kerogen type estimationWell logsMachine learning methodsCommittee machineOptimization van Krevelen
Majid Safaei-Farouji、Ali Kadkhodaie
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School of Geology, College of Science, University of Tehran, Tehran, Iran
Earth Sciences Department, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran
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