A robust hybrid near-real-time model for prediction of drilling fluids filtration; Engineering with Computers; Vol. 41

A robust hybrid near-real-time model for prediction of drilling fluids filtration; Engineering with Computers; Vol. 41

Bibliographic Details
Parent link:Engineering with Computers.— .— Berlin: Springer Nature
Vol. 41.— 2025.— 26 p.
Other Authors: Davoodi Sh. Shadfar, Al-Shargabi M. A. T. S. Mokhammed Abdulsalam Takha Sallam, Wood D. A. David, Mehrad M. Mohammad, Rukavishnikov V. S. Valery Sergeevich
Summary: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
Language:English
Published: 2025
Subjects:
электронный ресурс
труды учёных ТПУ
Filtration volume
Hybrid deep-learning
Fluid density
Marsh funnel viscosity
Long short-term memory
Grey wolf optimizer
Online Access:https://doi.org/10.1007/s00366-025-02113-3
Format: Electronic Book Chapter
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=679533

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