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A Comparison of Machine Learning Approaches for Prediction of Permeability using Well Log Data in the Hydrocarbon Reservoirs

Mohsen Talebkeikhah, Zahra Sadeghtabaghi, Mehdi Shabani

Abstract


Permeability is a vital parameter in reservoir engineering that affects production directly. Since this parameter's significance is obvious, finding a way to an accurate determination of permeability is essential as well. In this paper, the permeability of two notable carbonate reservoirs (Ilam and Sarvak) in the southwest of Iran was predicted by several different methods, and the level of accuracy in all models was compared. For this purpose, Multi-Layer Perceptron Neural Network (MLP), Radial Basis Function Neural Network (RBF), Support Vector Regression (SVR), decision tree (DT), and random forest (RF) methods were chosen. The full set of real well-logging data was investigated by random forest, and five of them were selected as the potent variables. At depth, computed gamma-ray log (CGR), spectral gamma-ray log (SGR), neutron porosity log (NPHI), and density log (RHOB) were considered efficacious variables and used as input data, while permeability was considered output. It should be noted that permeability values are derived from core analysis. Statistical parameters like the coefficient of determination (R2), root mean square error (RMSE), and standard deviation (SD) were determined for the train, test, and total sets. Based on statistical and graphical results, the SVM and DT models perform more accurately than others. RMSE, SD and R2values of SVM and DT models are 0.38, 1.63, 0.97 and 0.44, 2.89, and 0.96 respectively. The results of the best-proposed models in this paper were then compared with the outcome of the empirical equation for permeability prediction. The comparison indicates that artificial intelligence methods perform more accurately than traditional methods for permeability estimation, such as proposed equations.

 

Doi: 10.28991/HEF-2021-02-02-01

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Keywords


Permeability; Well Logging; Machine Learning; Data Mining; Ilam Formation; Sarvak Formation.

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DOI: 10.28991/HEF-2021-02-02-01

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