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Enhanced machine learning tree classifiers for lithology identification using Bayesian optimization

Lithology identification is a fundamental activity in oil and gas exploration. The application of artificial intelligence (AI) is currently being adopted as a state-of-the-art means of automating lithology identification. One aspect of this AI approach is the application of population search algorithms to optimise hyperparameters for enhanced prediction performance. For the first time, Bayesian optimization is deployed to determine the optimal learning parameters for more accurate tree and tree ensemble lithology classifiers. The aim is to rely on the ability of Bayesian optimization to consider previous classification results to improve the output of decision and ensemble tree lithology models using well logs as inputs. The proposed Bayesian optimised decision tree (BODT) generated the best classification accuracy of 89.8% as compared to 86.9%, 83.3% and 81.2% for fine, medium and coarse trees. For the ensembled trees, the Bayesian optimised AdaBoost (BO-AdaBoost) classifier generated the highest improved prediction accuracy of 94.2% while Bayesian optimised Bagged (BO-Bagged) and Bayesian optimised RUSBoost (BO-RUSBoost) had a lower accuracy rate of 94.0% and 77.1% respectively. Additionally, the performance of the Bayesian optimised classifiers offered higher reliability when compared with particle swarm optimization-based artificial neural networks (PSO-ANN). Hence, incorporating Bayesian optimization as a hyperparameter search algorithm will improve litholofacies recognition, leading to a higher accuracy rate and thereby provide an improved alternative for intelligent lithology identification.

Ketersediaan
140551.136Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Applied Computing and Geoscience - Open Access
No. Panggil
551.136
Penerbit
Amsterdam : Elsevier.,
Deskripsi Fisik
7 hlm PDF, 4.869 KB
Bahasa
Inggris
ISBN/ISSN
2590-1974
Klasifikasi
551.136
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.16, December 2022
Subjek
Bayesian optimization
Decision tree
Lithology
AdaBoost
Bagged, RUSBoost
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Enhanced machine learning tree classifiers for lithology identification using Bayesian optimization
    Lithology identification is a fundamental activity in oil and gas exploration. The application of artificial intelligence (AI) is currently being adopted as a state-of-the-art means of automating lithology identification. One aspect of this AI approach is the application of population search algorithms to optimise hyperparameters for enhanced prediction performance. For the first time, Bayesian optimization is deployed to determine the optimal learning parameters for more accurate tree and tree ensemble lithology classifiers. The aim is to rely on the ability of Bayesian optimization to consider previous classification results to improve the output of decision and ensemble tree lithology models using well logs as inputs. The proposed Bayesian optimised decision tree (BODT) generated the best classification accuracy of 89.8% as compared to 86.9%, 83.3% and 81.2% for fine, medium and coarse trees. For the ensembled trees, the Bayesian optimised AdaBoost (BO-AdaBoost) classifier generated the highest improved prediction accuracy of 94.2% while Bayesian optimised Bagged (BO-Bagged) and Bayesian optimised RUSBoost (BO-RUSBoost) had a lower accuracy rate of 94.0% and 77.1% respectively. Additionally, the performance of the Bayesian optimised classifiers offered higher reliability when compared with particle swarm optimization-based artificial neural networks (PSO-ANN). Hence, incorporating Bayesian optimization as a hyperparameter search algorithm will improve litholofacies recognition, leading to a higher accuracy rate and thereby provide an improved alternative for intelligent lithology identification.
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