Application of machine learning to determine the shear stress and filtration loss properties of nano-based drilling fluid
Publikationen: Beitrag in Fachzeitschrift › Artikel › Forschung › (peer-reviewed)
Standard
in: Journal of Petroleum Exploration and Production Technology, Jahrgang 13.2023, Nr. April, 04.2023, S. 1031-1052.
Publikationen: Beitrag in Fachzeitschrift › Artikel › Forschung › (peer-reviewed)
Harvard
APA
Vancouver
Author
Bibtex - Download
}
RIS (suitable for import to EndNote) - Download
TY - JOUR
T1 - Application of machine learning to determine the shear stress and filtration loss properties of nano-based drilling fluid
AU - Cai Ning, Yee
AU - Ridha, Syahrir
AU - Umer Ilyas, Suhaib
AU - Krishna, Shwetank
AU - Dzulkarnain, Iskandar
AU - Abdurraham, Muslim
N1 - Publisher Copyright: © 2022, The Author(s).
PY - 2023/4
Y1 - 2023/4
N2 - A detailed understanding of the drilling fluid rheology and filtration properties is essential to assuring reduced fluid loss during the transport process. As per literature review, silica nanoparticle is an exceptional additive to enhance drilling fluid rheology and filtration properties enhancement. However, a correlation based on nano-SiO2-water-based drilling fluid that can quantify the rheology and filtration properties of nanofluids is not available. Thus, two data-driven machine learning approaches are proposed for prediction, i.e. artificial-neural-network and least-square-support-vector-machine (LSSVM). Parameters involved for the prediction of shear stress are SiO2 concentration, temperature, and shear rate, whereas SiO2 nanoparticle concentration, temperature, and time are the inputs to simulate filtration volume. A feed-forward multilayer perceptron is constructed and optimised using the Levenberg–Marquardt learning algorithm. The parameters for the LSSVM are optimised using Couple Simulated Annealing. The performance of each model is evaluated based on several statistical parameters. The predicted results achieved R2 (coefficient of determination) value higher than 0.99 and MAE (mean absolute error) and MAPE (mean absolute percentage error) value below 7% for both the models. The developed models are further validated with experimental data that reveals an excellent agreement between predicted and experimental data.
AB - A detailed understanding of the drilling fluid rheology and filtration properties is essential to assuring reduced fluid loss during the transport process. As per literature review, silica nanoparticle is an exceptional additive to enhance drilling fluid rheology and filtration properties enhancement. However, a correlation based on nano-SiO2-water-based drilling fluid that can quantify the rheology and filtration properties of nanofluids is not available. Thus, two data-driven machine learning approaches are proposed for prediction, i.e. artificial-neural-network and least-square-support-vector-machine (LSSVM). Parameters involved for the prediction of shear stress are SiO2 concentration, temperature, and shear rate, whereas SiO2 nanoparticle concentration, temperature, and time are the inputs to simulate filtration volume. A feed-forward multilayer perceptron is constructed and optimised using the Levenberg–Marquardt learning algorithm. The parameters for the LSSVM are optimised using Couple Simulated Annealing. The performance of each model is evaluated based on several statistical parameters. The predicted results achieved R2 (coefficient of determination) value higher than 0.99 and MAE (mean absolute error) and MAPE (mean absolute percentage error) value below 7% for both the models. The developed models are further validated with experimental data that reveals an excellent agreement between predicted and experimental data.
UR - http://www.scopus.com/inward/record.url?scp=85144155278&partnerID=8YFLogxK
U2 - 10.1007/s13202-022-01589-9
DO - 10.1007/s13202-022-01589-9
M3 - Article
VL - 13.2023
SP - 1031
EP - 1052
JO - Journal of Petroleum Exploration and Production Technology
JF - Journal of Petroleum Exploration and Production Technology
SN - 2190-0558
IS - April
ER -