Real-time evaluation of the dynamic Young’s modulus for composite formations based on the drilling parameters using different machine learning algorithms
The dynamic Young’s modulus (Edyn) is a parameter needed for optimizing different aspects related to oil well designing. Currently, Edyn is determined from the knowledge of the formation bulk density, in addition to the shear and compressional velocities, which are not always available. This study i...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Earth Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2022.1034704/full |
| _version_ | 1817975451379826688 |
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| author | Ahmed Abdulhamid Mahmoud Hany Gamal Salaheldin Elkatatny Weiqing Chen |
| author_facet | Ahmed Abdulhamid Mahmoud Hany Gamal Salaheldin Elkatatny Weiqing Chen |
| author_sort | Ahmed Abdulhamid Mahmoud |
| collection | DOAJ |
| description | The dynamic Young’s modulus (Edyn) is a parameter needed for optimizing different aspects related to oil well designing. Currently, Edyn is determined from the knowledge of the formation bulk density, in addition to the shear and compressional velocities, which are not always available. This study introduces three machine learning (ML) models, namely, random forest (RF), adaptive neuro-fuzzy inference system with subtractive clustering (ANFIS-SC), and support vector regression (SVR), for estimation of the Edyn from only the real-time available drilling parameters. The ML models were learned on 2054 datasets collected from Well-A and then tested and validated on 871 and 2912 datasets from Well-B and Well-C, respectively. The results showed that the three optimized ML models accurately predicted the Edyn in the three oil wells considered in this study. The optimized SVR model outperformed both the RF and ANFIS-SC models in evaluating the Edyn in all three wells. For the validation data, the Edyn was assessed accurately with low average absolute percentage errors of 3.64%, 6.74%, and 1.03% using the optimized RF, ANFIS-SC, and SVR models, respectively. |
| first_indexed | 2024-04-13T21:50:21Z |
| format | Article |
| id | doaj.art-b3436d77fc58490ba759dd343a1dc6eb |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-04-13T21:50:21Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-b3436d77fc58490ba759dd343a1dc6eb2022-12-22T02:28:26ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632022-11-011010.3389/feart.2022.10347041034704Real-time evaluation of the dynamic Young’s modulus for composite formations based on the drilling parameters using different machine learning algorithmsAhmed Abdulhamid Mahmoud0Hany Gamal1Salaheldin Elkatatny2Weiqing Chen3Petroleum Engineering Department, College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaPetroleum Engineering Department, College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaPetroleum Engineering Department, College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaCenter for Integrative Petroleum Research, College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaThe dynamic Young’s modulus (Edyn) is a parameter needed for optimizing different aspects related to oil well designing. Currently, Edyn is determined from the knowledge of the formation bulk density, in addition to the shear and compressional velocities, which are not always available. This study introduces three machine learning (ML) models, namely, random forest (RF), adaptive neuro-fuzzy inference system with subtractive clustering (ANFIS-SC), and support vector regression (SVR), for estimation of the Edyn from only the real-time available drilling parameters. The ML models were learned on 2054 datasets collected from Well-A and then tested and validated on 871 and 2912 datasets from Well-B and Well-C, respectively. The results showed that the three optimized ML models accurately predicted the Edyn in the three oil wells considered in this study. The optimized SVR model outperformed both the RF and ANFIS-SC models in evaluating the Edyn in all three wells. For the validation data, the Edyn was assessed accurately with low average absolute percentage errors of 3.64%, 6.74%, and 1.03% using the optimized RF, ANFIS-SC, and SVR models, respectively.https://www.frontiersin.org/articles/10.3389/feart.2022.1034704/fulldynamic Young’s modulusdrilling parametersmachine learning modelsreal-time predictioncomposite formation |
| spellingShingle | Ahmed Abdulhamid Mahmoud Hany Gamal Salaheldin Elkatatny Weiqing Chen Real-time evaluation of the dynamic Young’s modulus for composite formations based on the drilling parameters using different machine learning algorithms Frontiers in Earth Science dynamic Young’s modulus drilling parameters machine learning models real-time prediction composite formation |
| title | Real-time evaluation of the dynamic Young’s modulus for composite formations based on the drilling parameters using different machine learning algorithms |
| title_full | Real-time evaluation of the dynamic Young’s modulus for composite formations based on the drilling parameters using different machine learning algorithms |
| title_fullStr | Real-time evaluation of the dynamic Young’s modulus for composite formations based on the drilling parameters using different machine learning algorithms |
| title_full_unstemmed | Real-time evaluation of the dynamic Young’s modulus for composite formations based on the drilling parameters using different machine learning algorithms |
| title_short | Real-time evaluation of the dynamic Young’s modulus for composite formations based on the drilling parameters using different machine learning algorithms |
| title_sort | real time evaluation of the dynamic young s modulus for composite formations based on the drilling parameters using different machine learning algorithms |
| topic | dynamic Young’s modulus drilling parameters machine learning models real-time prediction composite formation |
| url | https://www.frontiersin.org/articles/10.3389/feart.2022.1034704/full |
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