A Novel Application of Ensemble Methods with Data Resampling Techniques for Drill Bit Selection in the Oil and Gas Industry
Selection of the most suitable drill bit type is an important task for drillers when planning for new oil and gas wells. With the advancement of intelligent predictive models, the automated selection of drill bit type is possible using earlier drilled offset wells’ data. However, real-field well dat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/2/432 |
| _version_ | 1827600859579547648 |
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| author | Saurabh Tewari Umakant Dhar Dwivedi Susham Biswas |
| author_facet | Saurabh Tewari Umakant Dhar Dwivedi Susham Biswas |
| author_sort | Saurabh Tewari |
| collection | DOAJ |
| description | Selection of the most suitable drill bit type is an important task for drillers when planning for new oil and gas wells. With the advancement of intelligent predictive models, the automated selection of drill bit type is possible using earlier drilled offset wells’ data. However, real-field well data samples naturally involve an unequal distribution of data points that results in the formation of a complex imbalance multi-class classification problem during drill bit selection. In this analysis, Ensemble methods, namely Adaboost and Random Forest, have been combined with the data re-sampling techniques to provide a new approach for handling the complex drill bit selection process. Additionally, four popular machine learning techniques namely, K-nearest neighbors, naïve Bayes, multilayer perceptron, and support vector machine, are also evaluated to understand the performance degrading effects of imbalanced drilling data obtained from Norwegian wells. The comparison of results shows that the random forest with bootstrap class weighting technique has given the most impressive performance for bit type selection with testing accuracy ranges from 92% to 99%, and <i>G-mean</i> (0.84–0.97) in critical to normal experimental scenarios. This study provides an approach to automate the drill bit selection process over any field, which will minimize human error, time, and drilling cost. |
| first_indexed | 2024-03-09T04:44:05Z |
| format | Article |
| id | doaj.art-87b5cfed831640ee8067f48cf0ef7c66 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T04:44:05Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-87b5cfed831640ee8067f48cf0ef7c662023-12-03T13:17:49ZengMDPI AGEnergies1996-10732021-01-0114243210.3390/en14020432A Novel Application of Ensemble Methods with Data Resampling Techniques for Drill Bit Selection in the Oil and Gas IndustrySaurabh Tewari0Umakant Dhar Dwivedi1Susham Biswas2Machine Learning Laboratory, Rajiv Gandhi Institute of Petroleum Technology, Amethi 229304, IndiaMachine Learning Laboratory, Rajiv Gandhi Institute of Petroleum Technology, Amethi 229304, IndiaMachine Learning Laboratory, Rajiv Gandhi Institute of Petroleum Technology, Amethi 229304, IndiaSelection of the most suitable drill bit type is an important task for drillers when planning for new oil and gas wells. With the advancement of intelligent predictive models, the automated selection of drill bit type is possible using earlier drilled offset wells’ data. However, real-field well data samples naturally involve an unequal distribution of data points that results in the formation of a complex imbalance multi-class classification problem during drill bit selection. In this analysis, Ensemble methods, namely Adaboost and Random Forest, have been combined with the data re-sampling techniques to provide a new approach for handling the complex drill bit selection process. Additionally, four popular machine learning techniques namely, K-nearest neighbors, naïve Bayes, multilayer perceptron, and support vector machine, are also evaluated to understand the performance degrading effects of imbalanced drilling data obtained from Norwegian wells. The comparison of results shows that the random forest with bootstrap class weighting technique has given the most impressive performance for bit type selection with testing accuracy ranges from 92% to 99%, and <i>G-mean</i> (0.84–0.97) in critical to normal experimental scenarios. This study provides an approach to automate the drill bit selection process over any field, which will minimize human error, time, and drilling cost.https://www.mdpi.com/1996-1073/14/2/432drill bits selectionimbalanced dataensemble methodspetroleum data analytics |
| spellingShingle | Saurabh Tewari Umakant Dhar Dwivedi Susham Biswas A Novel Application of Ensemble Methods with Data Resampling Techniques for Drill Bit Selection in the Oil and Gas Industry Energies drill bits selection imbalanced data ensemble methods petroleum data analytics |
| title | A Novel Application of Ensemble Methods with Data Resampling Techniques for Drill Bit Selection in the Oil and Gas Industry |
| title_full | A Novel Application of Ensemble Methods with Data Resampling Techniques for Drill Bit Selection in the Oil and Gas Industry |
| title_fullStr | A Novel Application of Ensemble Methods with Data Resampling Techniques for Drill Bit Selection in the Oil and Gas Industry |
| title_full_unstemmed | A Novel Application of Ensemble Methods with Data Resampling Techniques for Drill Bit Selection in the Oil and Gas Industry |
| title_short | A Novel Application of Ensemble Methods with Data Resampling Techniques for Drill Bit Selection in the Oil and Gas Industry |
| title_sort | novel application of ensemble methods with data resampling techniques for drill bit selection in the oil and gas industry |
| topic | drill bits selection imbalanced data ensemble methods petroleum data analytics |
| url | https://www.mdpi.com/1996-1073/14/2/432 |
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