Machine-Learning Predictive Model for Semiautomated Monitoring of Solid Content in Water-Based Drilling Fluids; Arabian Journal for Science and Engineering; Vol. 50, no. 7
| Parent link: | Arabian Journal for Science and Engineering.— .— Berlin: Springer Nature Vol. 50, no. 7.— 2025.— P. 5175–5194 |
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| Співавтор: | |
| Інші автори: | , , , , |
| Резюме: | Title screen Accurate and frequent monitoring of the solid content (SC) of drilling fluids is necessary to avoid the issues associated with improper solid particle concentrations. Conventional methods for determining SC, such as retort analysis, lack immediacy and are labor-intensive. This study applies machine learning (ML) techniques to develop SC predictive models using readily available data—Marsh funnel viscosity and fluid density. A dataset of 1290 data records was collected from 17 wells drilled in two oil fields located in southwest Iran. Four ML models—least squares support vector machine (LSSVM), multilayered perceptron neural network, extreme learning machine, and generalized regression neural network—were developed to predict SC from the compiled dataset. Multiple assessment techniques were applied to attentively evaluate the models’ prediction performances and select the best-performing, SC prediction model. The LSSVM model generated the least errors, exhibiting the lowest root-mean-square error values for the training (1.80%) and testing (1.84%) subsets. The narrowest confidence interval, 0.18, achieved by the LSSVM model confirmed its reliability for SC prediction. Leverage analysis revealed minimal influence of outlier data on the LSSVM model's SC prediction performance. The trained LSSVM model was further validated on unseen data from another well drilled in one of the studied oil fields, demonstrating the model’s generalizability for providing credible close-to-real-time SC predictions in the studied fields Текстовый файл AM_Agreement |
| Мова: | Англійська |
| Опубліковано: |
2025
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| Предмети: | |
| Онлайн доступ: | https://doi.org/10.1007/s13369-024-09689-w |
| Формат: | MixedMaterials Електронний ресурс Частина з книги |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=676984 |
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| 200 | 1 | |a Machine-Learning Predictive Model for Semiautomated Monitoring of Solid Content in Water-Based Drilling Fluids |f Shadfar Davoodi, Sergey V. Muravyov, David A. Wood [et al.] | |
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| 330 | |a Accurate and frequent monitoring of the solid content (SC) of drilling fluids is necessary to avoid the issues associated with improper solid particle concentrations. Conventional methods for determining SC, such as retort analysis, lack immediacy and are labor-intensive. This study applies machine learning (ML) techniques to develop SC predictive models using readily available data—Marsh funnel viscosity and fluid density. A dataset of 1290 data records was collected from 17 wells drilled in two oil fields located in southwest Iran. Four ML models—least squares support vector machine (LSSVM), multilayered perceptron neural network, extreme learning machine, and generalized regression neural network—were developed to predict SC from the compiled dataset. Multiple assessment techniques were applied to attentively evaluate the models’ prediction performances and select the best-performing, SC prediction model. The LSSVM model generated the least errors, exhibiting the lowest root-mean-square error values for the training (1.80%) and testing (1.84%) subsets. The narrowest confidence interval, 0.18, achieved by the LSSVM model confirmed its reliability for SC prediction. Leverage analysis revealed minimal influence of outlier data on the LSSVM model's SC prediction performance. The trained LSSVM model was further validated on unseen data from another well drilled in one of the studied oil fields, demonstrating the model’s generalizability for providing credible close-to-real-time SC predictions in the studied fields | ||
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| 461 | 1 | |t Arabian Journal for Science and Engineering |c Berlin |n Springer Nature | |
| 463 | 1 | |t Vol. 50, no. 7 |v P. 5175–5194 |d 2025 | |
| 610 | 1 | |a Drilling fluid | |
| 610 | 1 | |a Least-squares support vector machine | |
| 610 | 1 | |a Semi-automated monitoring | |
| 610 | 1 | |a Machine learning modeling | |
| 610 | 1 | |a Solid content | |
| 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 Muravyov (Murav’ev) |b S. V. |c specialist in the field of control and measurement equipment |c Professor of Tomsk Polytechnic University,Doctor of technical sciences |f 1954- |g Sergey Vasilyevich |9 15440 | |
| 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 | |
| 712 | 0 | 2 | |a National Research Tomsk Polytechnic University |c (2009- ) |9 27197 |4 570 |
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