Academic Journal
A formation-response classifier for borehole deviation using conventional well log data.
| Τίτλος: | A formation-response classifier for borehole deviation using conventional well log data. |
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| Συγγραφείς: | B NK; School of Chemical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India., Mohammed AA; School of Chemical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India. |
| Πηγή: | PloS one [PLoS One] 2026 Oct 09; Vol. 21 (10), pp. e0356220. Date of Electronic Publication: 2026 Oct 09 (Print Publication: 2026). |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: San Francisco, CA : Public Library of Science |
| Ιατρικοί όροι (MeSH): | Machine Learning*, Classification Algorithms ; Bayes Theorem ; Boosting Machine Learning Algorithms ; Multilayer Perceptrons ; Prediction Algorithms ; Logistic Models |
| Περίληψη: | Accurate and timely identification of borehole deviation is essential in drilling, as unexpected trajectory shifts serve as key indicators of localised geomechanical failure, formation anisotropy, and wellbore integrity degradation. This study presents a systematic machine learning (ML) framework for classifying borehole deviation as Normal or deviated, utilising seven petrophysical and geomechanical well-log features from a 301-sample single-well dataset. Five supervised classifiers, K-Nearest Neighbours (KNN), Logistic Regression (LR), Gaussian Naive Bayes (GNB), Multilayer Perceptron (MLP), and Histogram Gradient Boosting (HGB) were evaluated. Preprocessing incorporated standard scaling, median imputation, and class imbalance mitigation using balanced class weights and the Synthetic Minority Oversampling Technique (SMOTE). To eliminate spatial data leakage, models were evaluated within a 10-fold depth-blocked cross-validation framework across five random seeds, yielding 95% confidence intervals for all performance metrics. The HGB model achieved the highest macro-averaged F1-score of 97.88% (±2.05% CI) and an out-of-fold cross-validation accuracy of 98.00%, outperforming KNN (94.39%), MLP (86.04%), GNB (81.04%), and LR (77.03%). Permutation-based feature importance highlighted Delta T, Gamma-ray, and Resistivity as the primary indicators of deviation risk, aligning with geomechanical principles. These findings demonstrate that Histogram Gradient Boosting with leakage-safe preprocessing provides a robust offline proof-of-concept for assessing wellbore trajectory quality before field validation. (Copyright: © 2026 B, Mohammed. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.) |
| Competing Interests: | The authors have declared that no competing interests exist. |
| References: | BMC Bioinformatics. 2013 Mar 22;14:106. (PMID: 23522326) BMC Genomics. 2020 Jan 2;21(1):6. (PMID: 31898477) Sci Rep. 2026 May 4;16(1):. (PMID: 42082596) |
| Entry Date(s): | Date Created: 20261009 Date Completed: 20261009 Latest Revision: 20261011 |
| Update Code: | 20261011 |
| PubMed Central ID: | PMC13652228 |
| DOI: | 10.1371/journal.pone.0356220 |
| PMID: | 42853833 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0356220 |