A Statistics-Based Correlation for the Assessment of Water-Based Mud Performance: Yield Point Prediction-Reference-Cited by-同舟云学术

A Statistics-Based Correlation for the Assessment of Water-Based Mud Performance: Yield Point Prediction

Published:2024-08-19 Issue: Volume: Page:
ISSN:2193-567X
Container-title:Arabian Journal for Science and Engineering
language:en
Short-container-title:Arab J Sci Eng

Author:

Alakbari Fahd Saeed^ORCID,Mohyaldinn Mysara Eissa,Mahmood Syed Mohammad,Hassan Anas Mohammed,Ashena Rahman,Al-Fakih Amin

Funder

Universiti Teknologi Petronas

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s13369-024-09415-6.pdf

Reference54 articles.

1. Bourgoyne, A.T.; Millheim, K.K.; Chenevert, M.E.; Young, F.S.: Applied drilling engineering (Vol. 2): society of petroleum engineers Richardson (1991)

2. Song, K.; Wu, Q.; Li, M.; Ren, S.; Dong, L.; Zhang, X.; Lei, T.; Kojima, Y.: Water-based bentonite drilling fluids modified by novel biopolymer for minimizing fluid loss and formation damage. Colloids Surf. A Physicochem. Eng. Asp. 507, 58–66 (2016)

3. Perween, S.; Thakur, N.K.; Beg, M.; Sharma, S.; Ranjan, A.: Enhancing the properties of water based drilling fluid using bismuth ferrite nanoparticles. Colloids Surf. A Physicochem. Eng. Asp. 561, 165–177 (2019)

4. Razi, M.M.; Mazidi, M.; Razi, F.M.; Aligolzadeh, H.; Niazi, S.: Artificial neural network modeling of plastic viscosity, yield point, and apparent viscosity for water-based drilling fluids. J. Dispers. Sci. Technol. 34, 822–827 (2013). https://doi.org/10.1080/01932691.2012.704746

5. Elkatatny, S.; Tariq, Z.; Mahmoud, M.: Real time prediction of drilling fluid rheological properties using Artificial Neural Networks visible mathematical model (white box). J. Pet. Sci. Eng. 146, 1202–1210 (2016)