A novel data-driven model for real-time prediction of static Young's modulus applying mud-logging data
| Parent link: | Earth Science Informatics.— .— New York: Springer Nature Vol. 17.— 2024.— 23 p. |
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| Weitere Verfasser: | , , , , , |
| Zusammenfassung: | Title screen Effective drilling planning relies on understanding the rock mechanical properties, typically estimated from petrophysical data. Real-time estimation of these properties, especially static Young's modulus (Esta), is crucial for geomechanical modeling, wellbore stability, and cost-effective decision-making. In this study, predictive models of Esta were developed using mudlogging data from two vertically drilled wells (A and B) in the same field. Esta was estimated from petrophysical data across the studied depth range in both wells using a field-specific equation. Outlier data were identified and removed by evaluating the cross plot of mechanical specific energy and drilling rate for Well A. The data from Well A were then randomly divided into training and testing sets. The algorithms, multi-layer perceptron neural networks, random forests, Gaussian process regression (GPR), and support vector regression, were adjusted and applied to the training data. The resulting models were evaluated on the test data. The GPR model demonstrated the lowest RMSE values in both the training (0.0075 GPa) and testing (0.4577 GPa) phases, indicating superior performance. To further assess the models, the overfitting index and scoring techniques were employed, revealing that the GPR model exhibited the lowest overfitting value and outperformed the other models. Consequently, the GPR model was selected as the best-performing model and was analyzed using Shapley additive explanation to evaluate the influence of each input feature on the output. This analysis indicated that depth had the greatest effect, while rotation speed had the least impact on the model's output. The application of the GPR model to predict Esta in Well B demonstrated its high generalization capability. Therefore, it can be confidently stated that with additional data, this model could be effectively applied to similar depth ranges in other wells within the field. The study introduces innovations by applying GPR to predict Esta from mudlogging data, addressing outlier impact on predictions, and developing a real-time Esta prediction model for drilling. Текстовый файл AM_Agreement |
| Sprache: | Englisch |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://doi.org/10.1007/s12145-024-01474-5 |
| Format: | Elektronisch Buchkapitel |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=675038 |
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| 200 | 1 | |a A novel data-driven model for real-time prediction of static Young's modulus applying mud-logging data |f Shadfar Davoodi, Mohammad Mehrad, David A. Wood [et al.] | |
| 203 | |a Текст |c электронный |b визуальный | ||
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| 300 | |a Title screen | ||
| 330 | |a Effective drilling planning relies on understanding the rock mechanical properties, typically estimated from petrophysical data. Real-time estimation of these properties, especially static Young's modulus (Esta), is crucial for geomechanical modeling, wellbore stability, and cost-effective decision-making. In this study, predictive models of Esta were developed using mudlogging data from two vertically drilled wells (A and B) in the same field. Esta was estimated from petrophysical data across the studied depth range in both wells using a field-specific equation. Outlier data were identified and removed by evaluating the cross plot of mechanical specific energy and drilling rate for Well A. The data from Well A were then randomly divided into training and testing sets. The algorithms, multi-layer perceptron neural networks, random forests, Gaussian process regression (GPR), and support vector regression, were adjusted and applied to the training data. The resulting models were evaluated on the test data. The GPR model demonstrated the lowest RMSE values in both the training (0.0075 GPa) and testing (0.4577 GPa) phases, indicating superior performance. To further assess the models, the overfitting index and scoring techniques were employed, revealing that the GPR model exhibited the lowest overfitting value and outperformed the other models. Consequently, the GPR model was selected as the best-performing model and was analyzed using Shapley additive explanation to evaluate the influence of each input feature on the output. This analysis indicated that depth had the greatest effect, while rotation speed had the least impact on the model's output. The application of the GPR model to predict Esta in Well B demonstrated its high generalization capability. Therefore, it can be confidently stated that with additional data, this model could be effectively applied to similar depth ranges in other wells within the field. The study introduces innovations by applying GPR to predict Esta from mudlogging data, addressing outlier impact on predictions, and developing a real-time Esta prediction model for drilling. | ||
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| 461 | 1 | |t Earth Science Informatics |c New York |n Springer Nature | |
| 463 | 1 | |t Vol. 17 |v 23 p. |d 2024 | |
| 610 | 1 | |a Young's modulus | |
| 610 | 1 | |a Real-time prediction model | |
| 610 | 1 | |a Machine learning | |
| 610 | 1 | |a Mud logging data | |
| 610 | 1 | |a Gaussian process regression | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 701 | 1 | |a Davoodi |b Sh. |c specialist in the field of petroleum engineering |c Research Engineer of Tomsk Polytechnic University |f 1990- |g Shadfar |9 22200 | |
| 701 | 1 | |a Mehrad |b M. |g Mohammad | |
| 701 | 1 | |a Wood |b D. A. |g David | |
| 701 | 1 | |a Al-Shargabi |b M. A. T. S. |c specialist in the field of petroleum engineering |c Engineer of Tomsk Polytechnic University |f 1993- |g Mokhammed Abdulsalam Takha Sallam |9 22768 | |
| 701 | 1 | |a Eremyan |b G. A. |c specialist in the field of petroleum engineering |c Research Engineer, Tomsk Polytechnic University |f 1989- |g Grachik Araikovich |9 22149 | |
| 701 | 1 | |a Shulgina |b T. M. |c mathematician, specialist in the field of petroleum engineering |c Engineer, Associate Professor of Tomsk Polytechnic University, Candidate of technical sciences |f 1984- |g Tamara Mikhaylovna |y Tomsk |9 88666 | |
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