A robust hybrid near-real-time model for prediction of drilling fluids filtration; Engineering with Computers; Vol. 41
| Parent link: | Engineering with Computers.— .— Berlin: Springer Nature Vol. 41.— 2025.— 26 p. |
|---|---|
| Altri autori: | , , , , |
| Riassunto: | Title screen Efficient drilling operations demand careful preparation, especially to ensure the optimal filtration characteristics of drilling fluids to prevent issues like formation damage. Monitoring changes in fluid filtration volume (FV) is critical for maintaining wellbore stability, preventing rock damage, and lowering drilling costs. This study employs deep learning (DL) models, specifically Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) hybridized with the grey wolf optimizer (GWO), to accurately predict FV using daily measurements of fluid density (FD) and Marsh funnel viscosity (MFV). A field dataset of 1260 records from 17 wells in southwest Iran is analyzed to explore the relationship between fluid properties and their impact on FV. Results reveal that the LSTM-GWO model outperforms other algorithms, achieving the lowest root mean squared error (RMSE) of 1.0950 mL for FV prediction on test data. In comparison, LSTM and CNN models record higher RMSE values of 1.9963 mL and 2.2862 mL, respectively, whereas a hybrid CNN-GWO model achieves an RMSE of 1.3551 mL. Sensitivity analysis indicates that the LSTM-GWO model is efficient at capturing relationships between inputs and FV, suggesting an ability to learn complex non-linear patterns from the input data. Further analyses confirmed the robustness of the hybrid models, revealing that they exhibited greater resilience compared to basic DL models. The proposed hybrid DL approach presents a promising methodology for accurate and near-real-time drilling fluid FV prediction, addressing current limitations in traditional empirical and/or analytical methods Текстовый файл AM_Agreement |
| Lingua: | inglese |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | https://doi.org/10.1007/s00366-025-02113-3 |
| Natura: | Elettronico Capitolo di libro |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=679533 |
MARC
| LEADER | 00000naa0a2200000 4500 | ||
|---|---|---|---|
| 001 | 679533 | ||
| 005 | 20250407142323.0 | ||
| 090 | |a 679533 | ||
| 100 | |a 20250407d2025 k||y0rusy50 ba | ||
| 101 | 0 | |a eng | |
| 102 | |a DE | ||
| 135 | |a drcn ---uucaa | ||
| 181 | 0 | |a i |b e | |
| 182 | 0 | |a b | |
| 183 | 0 | |a cr |2 RDAcarrier | |
| 200 | 1 | |a A robust hybrid near-real-time model for prediction of drilling fluids filtration |f Shadfar Davoodi, Mohammed Al-Shargabi, David A. Wood [et al.] | |
| 203 | |a Текст |b визуальный |c электронный | ||
| 283 | |a online_resource |2 RDAcarrier | ||
| 300 | |a Title screen | ||
| 320 | |a References: 71 tit | ||
| 330 | |a Efficient drilling operations demand careful preparation, especially to ensure the optimal filtration characteristics of drilling fluids to prevent issues like formation damage. Monitoring changes in fluid filtration volume (FV) is critical for maintaining wellbore stability, preventing rock damage, and lowering drilling costs. This study employs deep learning (DL) models, specifically Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) hybridized with the grey wolf optimizer (GWO), to accurately predict FV using daily measurements of fluid density (FD) and Marsh funnel viscosity (MFV). A field dataset of 1260 records from 17 wells in southwest Iran is analyzed to explore the relationship between fluid properties and their impact on FV. Results reveal that the LSTM-GWO model outperforms other algorithms, achieving the lowest root mean squared error (RMSE) of 1.0950 mL for FV prediction on test data. In comparison, LSTM and CNN models record higher RMSE values of 1.9963 mL and 2.2862 mL, respectively, whereas a hybrid CNN-GWO model achieves an RMSE of 1.3551 mL. Sensitivity analysis indicates that the LSTM-GWO model is efficient at capturing relationships between inputs and FV, suggesting an ability to learn complex non-linear patterns from the input data. Further analyses confirmed the robustness of the hybrid models, revealing that they exhibited greater resilience compared to basic DL models. The proposed hybrid DL approach presents a promising methodology for accurate and near-real-time drilling fluid FV prediction, addressing current limitations in traditional empirical and/or analytical methods | ||
| 336 | |a Текстовый файл | ||
| 371 | 0 | |a AM_Agreement | |
| 461 | 1 | |t Engineering with Computers |c Berlin |n Springer Nature | |
| 463 | 1 | |t Vol. 41 |v 26 p. |d 2025 | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a Filtration volume | |
| 610 | 1 | |a Hybrid deep-learning | |
| 610 | 1 | |a Fluid density | |
| 610 | 1 | |a Marsh funnel viscosity | |
| 610 | 1 | |a Long short-term memory | |
| 610 | 1 | |a Grey wolf optimizer | |
| 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 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 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 | |
| 801 | 0 | |a RU |b 63413507 |c 20250407 | |
| 850 | |a 63413507 | ||
| 856 | 4 | |u https://doi.org/10.1007/s00366-025-02113-3 |z https://doi.org/10.1007/s00366-025-02113-3 | |
| 942 | |c CF | ||