Robust Machine Learning Predictive Models for Real-Time Determination of Confined Compressive Strength of Rock Using Mudlogging Data; Rock Mechanics and Rock Engineering; Vol. 57
| Parent link: | Rock Mechanics and Rock Engineering.— .— New York: Springer Nature Vol. 57.— 2024.— 27 p. |
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| Autor Corporativo: | |
| Otros Autores: | , , , , , |
| Sumario: | Title screen Mud logging data, which quantify the energy expended in rock breaking during drilling, are routinely acquired during drilling. These data offer a practical means to estimate the geomechanical properties of rocks, particularly confined compressive strength (CCS), avoiding the need for costly and destructive laboratory tests. Thus, in this study, predictive models for CCS were developed utilizing mudlogging data and employing various machine learning (ML) algorithms, including multi-layer perceptron neural network (MLPNN), least squares support vector machine (LSSVM), Gaussian process regression (GPR), and random forest (RF). Data were compiled from reservoir sections of two vertical wells (Well A and Well B) of an oil field in southwest Iran. CCS values were estimated using petrophysical logs and empirical correlations, calibrated with laboratory test results, and organized into a database of input and output features. 80% of Well A data were allocated for training, with 20% reserved for testing. The Tukey method was then used to remove outliers from the training subset, ensuring a high-accuracy and generalizable model. Subsequently, tuned ML algorithms based on the problem condition were applied to develop predictive CCS models using the training data. These models were then rigorously assessed using the test data. The outcomes of these steps revealed that the GPR-based model exhibited the lowest Root Mean Square Error (RMSE, train: 0.8499 MPa and test: 6.3886 MPa) and the highest coefficient of determination (R2, train: 0.9993 and test: 0.9597) among developed ML models. Further scrutiny employing over-fitting index and regression error characteristic curve techniques underscored the high accuracy and the generalizability of the GPR model on unseen data. Application of the GPR model on Well B demonstrated remarkable accuracy (RMSE = 2.3107 MPa) and its real-time predictive capability from minimal mudlogging data variables, providing a reliable and cost-effective solution for CCS prediction in vertical wells. Текстовый файл AM_Agreement |
| Lenguaje: | inglés |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://doi.org/10.1007/s00603-024-03859-w |
| Formato: | Electrónico Capítulo de libro |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=672918 |
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| 200 | 1 | |a Robust Machine Learning Predictive Models for Real-Time Determination of Confined Compressive Strength of Rock Using Mudlogging Data |f T. M. Zamanzadeh, Sh. Davoodi, D. A. Wood [et al.] | |
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| 330 | |a Mud logging data, which quantify the energy expended in rock breaking during drilling, are routinely acquired during drilling. These data offer a practical means to estimate the geomechanical properties of rocks, particularly confined compressive strength (CCS), avoiding the need for costly and destructive laboratory tests. Thus, in this study, predictive models for CCS were developed utilizing mudlogging data and employing various machine learning (ML) algorithms, including multi-layer perceptron neural network (MLPNN), least squares support vector machine (LSSVM), Gaussian process regression (GPR), and random forest (RF). Data were compiled from reservoir sections of two vertical wells (Well A and Well B) of an oil field in southwest Iran. CCS values were estimated using petrophysical logs and empirical correlations, calibrated with laboratory test results, and organized into a database of input and output features. 80% of Well A data were allocated for training, with 20% reserved for testing. The Tukey method was then used to remove outliers from the training subset, ensuring a high-accuracy and generalizable model. Subsequently, tuned ML algorithms based on the problem condition were applied to develop predictive CCS models using the training data. These models were then rigorously assessed using the test data. The outcomes of these steps revealed that the GPR-based model exhibited the lowest Root Mean Square Error (RMSE, train: 0.8499 MPa and test: 6.3886 MPa) and the highest coefficient of determination (R2, train: 0.9993 and test: 0.9597) among developed ML models. Further scrutiny employing over-fitting index and regression error characteristic curve techniques underscored the high accuracy and the generalizability of the GPR model on unseen data. Application of the GPR model on Well B demonstrated remarkable accuracy (RMSE = 2.3107 MPa) and its real-time predictive capability from minimal mudlogging data variables, providing a reliable and cost-effective solution for CCS prediction in vertical wells. | ||
| 336 | |a Текстовый файл | ||
| 371 | 0 | |a AM_Agreement | |
| 461 | 1 | |t Rock Mechanics and Rock Engineering |c New York |n Springer Nature | |
| 463 | 1 | |t Vol. 57 |v 27 p. |d 2024 | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a Confined compressive strength prediction | |
| 610 | 1 | |a Gaussian process regression | |
| 610 | 1 | |a Tukey outlier detection | |
| 610 | 1 | |a Overfitting index | |
| 610 | 1 | |a Shapley additive explanation | |
| 701 | 1 | |a Zamanzadeh |b T. M. |g Talkhouncheh Milad | |
| 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 Wood |b D. A. |g David | |
| 701 | 1 | |a Mehrad |b M. |g Mohammad | |
| 701 | 1 | |a Rukavishnikov |b V. S. |c Director of the Center for Training and Retraining of Oil and Gas Specialists, Associate Professor of Tomsk Polytechnic University, Candidate of Technical Sciences |c Engineer of Tomsk Polytechnic University |f 1984- |g Valery Sergeevich |9 17614 | |
| 701 | 1 | |a Bakhshi |b R. |g Reza | |
| 712 | 0 | 2 | |a Томский политехнический университет |c 1991- |9 26305 |
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