Accuracy assessment of various supervised machine learning algorithms in litho-facies classification from seismic data in the Penobscot field, Scotian Basin

Frontiers in Earth Science

Earth Science

Kamal Abdelrahman¹  Peter Andráš²  Ahmed M. Eldosouky³  Akash Chandra⁴  Suresh Konka⁴  Satya Narayan⁴ 

[1] Department of Geology and Geophysics, College of Science, King Saud University, Riyadh, Saudi Arabia;Faculty of Natural Sciences, Matej Bel University in Banska Bystrica, Banska Bystrica, Slovakia;Geology Department, Faculty of Science, Suez University, Suez, Egypt;Oil and Natural Gas Corporation (ONGC), Dehradun, India;

关键词: machine learning;    litho-facies classification;    validation;    hydrocarbon exploration;    Penobscot field;   

DOI  :  10.3389/feart.2023.1150954

 received in 2023-01-25, accepted in 2023-04-25,  发布年份 2023

来源: Frontiers

【 摘 要 】

Litho-facies classification is an essential task in characterizing the complex reservoirs in petroleum exploration and subsequent field development. The lithofacies classification at borehole locations is detailed but lacks in providing larger coverage areas. The acquired 3D seismic data provides global coverage for studying the reservoir facies heterogeneities in the study area. This study applies six supervised machine learning techniques (Random Forest, Support Vector Machine, Artificial Neural Network, Adaptive Boosting, Xtreme Gradient Boosting, and Multilayer Perceptron) to 3D post-stack seismic data to accurately estimate different litho-facies in inter-well regions and compares their performance. Initially, the efficacy of the said models was critically examined via the confusion matrix (accuracy and misclass) and evaluation matrix (precision, recall, F1-score) on the test data. It was found that all the machine learning models performed best in classifying the shale facies (87%–94%) followed by the sand (65%–79%) and carbonate facies (60%–78%) in the Penobscot field, Scotian Basin. On an overall accuracy scale, we found the multilayer perceptron method the best-performing tool, whereas the adaptive boosting method was the least-performing tool in classifying all three litho-facies in the current analysis. While other methods also performed moderately good for the classification of all three litho-facies. The predicted litho-facies using seismic attributes matched well with the log data interpreted facies on the borehole locations. It indicates that the facies estimated in inter-well regions are accurate and reliable. Furthermore, we validated the estimated results with the other seismic attributes to ascertain the accuracy and reliability of the predicted litho-facies between the borehole locations. This study recommends machine learning applications for litho-facies classification to reduce the risk associated with reservoir characterization.

【 授权许可】

Unknown   
Copyright © 2023 Narayan, Konka, Chandra, Abdelrahman, Andráš and Eldosouky.