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Machine learning assisted lithology prediction using geophysical logs: A case study from Cambay basin

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Identification and characterisation of reservoir facies is a prime factor in delimiting the hydrocarbon potential zones of a reservoir for hydrocarbon exploration. The geophysical logs, which are physical parameters of reservoir facies measured in the vicinity of boreholes, play a crucial role in the interpretation of reservoir facies. The present study deals with the identification of the lithology of the Limbodara oil field in the Cambay basin using machine learning (ML) techniques on geophysical logs. The supervised techniques of machine learning, such as support vector machines (SVM), artificial neural networks (ANN), and k-nearest neighbours (kNN), are used as nonlinear classifiers for the identification of lithology from nonlinear geophysical logs. The hyperparameters of the ML model are optimised using the grid search cross-validation (CV) method to increase the performance of the model, as evaluated by confusion matrix, area under receiver operating characteristics curve (AUC), precision, recall, and F1 score. The ML model used five geophysical parameters of two wells with four known distinguished lithologies (Class-A, Class-B, Class-C, and Class-D) for optimisation and training of the model. The optimised and trained model for each lithology for kNN, SVM, and ANN shows an overall correct prediction of true values with 85.4, 87.0, and 88.9%, respectively, from the confusion matrix. Apart from this, the receiver operative characteristics (ROC) also show that the overall area under the curve for each lithology is greater than 90%, and other evaluation parameters such as precision, recall, and F1 score show accuracy greater than 84%, except for the cases of Class C and Class D from SVM and ANN. Thus, the accuracy of each model from evaluation parameters suggests that the combined analysis of different ML models offers to select the optimised ML model for better results and validation to achieve and model the lithology with better precision.


  • A way out for obtaining litholog supplements at uncored section in boreholes

  • Established ML assisted mapping function between wireline logs and lithologs

  • Predicted litholog sequence with secure level of accuracy (>80%)

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Authors are grateful to the Director of the Wadia Institute of Himalayan Geology in Dehradun for granting permission to publish this paper. They are also thankful to the Director, IIT-ISM, for permission to carry out the research work and use available academic resources. The authors are grateful to the Director General of Hydrocarbon (DGH) and Oil and Natural Gas Corporation (ONGC), India, for providing the necessary data to carry out research work and permission to publish the work. KS acknowledges DST-SERB for JC Bose National Fellowship. The authors are thankful to the anonymous reviewers, Editor-in-Chief, Prof. Somnath Dasgupta, and Guest Editor Dr. Upasana Swaroop Banerji for their constructive suggestions to improve the manuscript within the necessary time and effort.

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RP: Written original draft and data curation. BM: Conceptualisation, data curation, software, methodology, editing, and finalising manuscript. UKS: Resources & project management, validation, and editing manuscript. KS: Supervision and editing.

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Correspondence to Bappa Mukherjee.

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Communicated by Upasana S Banerji

This article is part of the Topical Collection: AI/ML in Earth System Sciences

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Prajapati, R., Mukherjee, B., Singh, U.K. et al. Machine learning assisted lithology prediction using geophysical logs: A case study from Cambay basin. J Earth Syst Sci 133, 108 (2024).

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