An Approach for the Classification of Rock Types Using Machine Learning of Core and Log Data

Yihan Xing, Huiting Yang and Wei Yu ()
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Yihan Xing: School of Statistics, Capital University of Economics and Business, Beijing 100070, China
Huiting Yang: School of Geosciences &Technology, Southwest Petroleum University, Chengdu 610500, China
Wei Yu: SimTech LLC, Houston, TX 77494, USA

Sustainability, 2023, vol. 15, issue 11, 1-15

Abstract: Classifying rocks based on core data is the most common method used by geologists. However, due to factors such as drilling costs, it is impossible to obtain core samples from all wells, which poses challenges for the accurate identification of rocks. In this study, the authors demonstrated the application of an explainable machine-learning workflow using core and log data to identify rock types. The rock type is determined utilizing the flow zone index (FZI) method using core data first, and then based on the collection, collation, and cleaning of well log data, four supervised learning techniques were used to correlate well log data with rock types, and learning and prediction models were constructed. The optimal machine learning algorithm for the classification of rocks is selected based on a 10-fold cross-test and a comparison of AUC (area under curve) values. The accuracy rate of the results indicates that the proposed method can greatly improve the accuracy of the classification of rocks. SHapley Additive exPlanations (SHAP) was used to rank the importance of the various well logs used as input variables for the prediction of rock types and provides both local and global sensitivities, enabling the interpretation of prediction models and solving the “black box” problem with associated machine learning algorithms. The results of this study demonstrated that the proposed method can reliably predict rock types based on well log data and can solve hard problems in geological research. Furthermore, the method can provide consistent well log interpretation arising from the lack of core data while providing a powerful tool for well trajectory optimization. Finally, the system can aid with the selection of intervals to be completed and/or perforated.

Keywords: rock type; flow zone index; supervised learning; SHAP value; AUC value (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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