Hybrid Machine-Learning Model for Accurate Prediction of Filtration Volume in Water-Based Drilling Fluids; Applied Sciences; Vol. 14, iss. 19

Hybrid Machine-Learning Model for Accurate Prediction of Filtration Volume in Water-Based Drilling Fluids; Applied Sciences; Vol. 14, iss. 19

Bibliographic Details
Parent link:Applied Sciences.— .— Basel: MDPI AG
Vol. 14, iss. 19.— 2024.— Article number 9035, 23 p.
Corporate Author: Томский политехнический университет (570)
Other Authors: Davoodi Sh. Shadfar, Al-Shargabi M. A. T. S. Mokhammed Abdulsalam Takha Sallam, Rukavishnikov V. S. Valery Sergeevich, Mekhrad Mokhammad M., Wood David D. A., Al-Rubaii Mohammed M.
Summary:Title screen
Accurately predicting the filtration volume (FV) in drilling fluid (DF) is crucial for avoiding drilling problems such as a stuck pipe and minimizing DF impacts on formations during drilling. Traditional FV measurement relies on human-centric experimental evaluation, which is time-consuming. Recently, machine learning (ML) proved itself as a promising approach for FV prediction. However, existing ML methods require time-consuming input variables, hindering the semi-real-time monitoring of the FV. Therefore, employing radial basis function neural network (RBFNN) and multilayer extreme learning machine (MELM) algorithms integrated with the growth optimizer (GO), predictive hybrid ML (HML) models are developed to reliably predict the FV using only two easy-to-measure input variables: drilling fluid density (FD) and Marsh funnel viscosity (MFV). A 1260-record dataset from seventeen wells drilled in two oil and gas fields (Iran) was used to evaluate the models. Results showed the superior performance of the RBFNN-GO model, achieving a root-mean-square error (RMSE) of 0.6396 mL. Overfitting index (OFI), score, dependency, and Shapley additive explanations (SHAP) analysis confirmed the superior FV prediction performance of the RBFNN-GO model. In addition, the low RMSE (0.3227 mL) of the RBFNN-NGO model on unseen data from a different well within the studied fields confirmed the strong generalizability of this rapid and novel FV prediction method
Текстовый файл
Language:English
Published: 2024
Subjects:
filtration volume
fluid density
hybridized machine learning
growth optimizer
Marsh funnel viscosity
semi real-time filtration monitoring
электронный ресурс
труды учёных ТПУ
Online Access:https://doi.org/10.3390/app14199035
Format: Electronic Book Chapter
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=677205

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