1887

Abstract

Summary

Facies characterization is important to distinctly define rocks of interest and to build a better understanding of the depositional environments encountered at wellbore. The conventional approach of facies analysis by human interpreters involves a time-consuming process. In addition, lack of experience and difference in interpretational approach often leads to inconsistencies that may affect the overall geological modeling. To solve these problems, we introduce three commonly used machine learning algorithms for automated lithofacies classification -Decision Trees, Random Forest and the Support Vector Machine. In this study, we apply these supervised classification algorithms on a set of wireline logs from different wells and evaluate the efficiency of each algorithm for facies classification under different constraints.

While machine learning proves to be a more time-efficient and consistent solution, the performance and accuracy varies with the algorithm and the preconditioning of the data. Support Vector Machine outperforms the Random Forest and Decision Trees when the training dataset is limited. It is also inferred that the models are more efficient with fewer predictive classes and less complexity. Also conditioning of the training data to provide equal weightage to all the predictive classes are of equal importance to create a robust and unbiased model.

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/content/papers/10.3997/2214-4609.202032090
2020-11-30
2024-03-28
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References

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