Application of Advanced Machine Learning Models for Lithological Facies Prediction in a Southern Iranian Oilfield

Document Type : Original Article

Authors

1 Department of AL and Robotics, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

2 Department of Geology, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran

3 Computer Engineering Department, Khavaran Institute of Higher Education, Mashhad, Iran

4 State Key Laboratory of Marine Geology, Tongji University, Shanghai, 200092, China

5 Department of Geological survey & Development, National Iranian South Oil Company, Iran

Abstract

This study aims to employ supervised Advanced machine learning for the classification of lithological facies from geophysical log data in wells without drilling core samples. For this purpose, a dataset from seven wells in a training set from one of the oil fields in southern Iran has been utilized. This dataset includes natural gamma ray (SGR), corrected gamma ray (CGR), bulk density (RHOB), neutron porosity (NPHI), compressional wave slowness (DTSM), and shear wave slowness (DTCO), which directly influence the classification of geomechanical facies. These parameters are employed as independent variables, while lithological facies serve as the dependent variable for classification. This dataset pertains to depths ranging from 3000 to 4000 meters in the Ilam and Sarvak fractured limestone formations (Bangestan Limestone) of the subsurface. As the title suggests in this article, Initially, through artificial intelligence clustering methods and laboratory studies, these formations were categorized into five distinct lithological facies After this stage, eight supervised machine learning methods were employed, including Regression Logistic, K Neighbors Classifier, Decision Tree Classifier, Random Forest Classifier, Gaussian NB, Gradient Boosting Classifier, Extra Trees Classifier, and Support Vector Machine (SVM), to predict lithological facies in wells without existing classifications. The dataset of these wells underwent training and testing stages with each of these algorithms to construct an appropriate model.
As a result, facies labels were predicted. The performance of the models was evaluated using multiple metrics including Accuracy, Precision, F1-Score, and Recall through confusion matrices and ROC curves. The Extra Trees Classifier, Gradient Boosting Classifier, and K Neighbors Classifier showed superior results among these methods. Finally, the model's performance in predicting lithological facies of unseen or out-of-sample wells was presented.

Keywords

References

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Journal of Petroleum Geomechanics
Volume 6, Issue 4
August 2023
Pages 81-98

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