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An advanced ensemble modeling approach for predicting carbonate reservoir porosity from seismic attributes

Topór Tomasz, Sowiżdżał Krzysztof

Geology, Geophysics and Environment

2023

Vol. 49, no. 3

245--260

This study uses a machine learning (ML) ensemble modeling approach to predict porosity from multiple seismic attributes in one of the most promising Main Dolomite hydrocarbon reservoirs in NW Poland. The presented workflow tests five different model types of varying complexity: K-nearest neighbors (KNN), random forests (RF), extreme gradient boosting (XGB), support vector machine (SVM), single layer neural network with multilayer perceptron (MLP). The selected models are additionally run with different configurations originating from the pre-processing stage, including Yeo–Johnson transformation (YJ) and principal component analysis (PCA). The race ANOVA method across resample data is used to tune the best hyperparameters for each model. The model candidates and the role of different pre-processors are evaluated based on standard ML metrics – coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE). The model stacking is performed on five model candidates: two KNN, two XGB, and one SVM PCA with a marginal role. The results of the ensemble model showed superior accuracy over single learners, with all metrics (R2 0.890, RMSE 0.0252, MAE 0.168). It also turned out to be almost three times better than the neural net (NN) results obtained from commercial software on the same testing set (R2 0.318, RMSE 0.0628, MAE 0.0487). The spatial distribution of porosity from the ensemble model indicated areas of good reservoir properties that overlap with hydrocarbon production fields. This observation completes the evaluation of the ensemble technique results from model metrics. Overall, the proposed solution is a promising tool for better porosity prediction and understanding of heterogeneous carbonate reservoirs from multiple seismic attributes.

Examples of new models applied in selected simulation systems with respect to database

Ignaszak Z., Hajkowski J., Popielarski P.

Archives of Foundry Engineering

2013

Vol. 13, iss. 1

45--50

The tolerance of damage rule progressively meets the approval in the design casting parts procedures. Therefore, there were appeared the new challenges and expectations for permanent development of process virtualization in the mechanical engineering industry. Virtualization is increasingly developed on the stage of product design and materials technologies optimization. Increasing expectations of design and process engineers regarding the practical effectiveness of applied simulation systems with new proposed up-grades modules is observed. The purpose is to obtain simulation tools allowing the most possible realistic prognosis of the casting structure, including indication, with the highest possible probability, places in the casting that are endangered with the possibility of shrinkage– and gas porosity formation. This 3D map of discontinuities and structure transformed in local mechanical characteristics are used to calculate the local stresses and safety factors. The needs of tolerance of damage and new approach to evaluate the quality of such prognosis must be defined. These problems of validation of new models/modules used to predict the shrinkage– and gas porosity including the chosen structure parameters in the example of AlSi7 alloy are discussed in the paper.