Predicting performance of SiO<sub>2</sub>nanoparticles on filtration volume using reliable approaches: application in water-based drilling fluids

Golsefatan, Alireza; Shahbazi, Khalil

doi:10.1080/15567036.2019.1639854

Cited by 7 publications

(1 citation statement)

References 25 publications

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“…Recently, inorganic nanoparticles, such as nanosilica, graphene oxide, ZnO, Fe 3 O 4 , and nanoclay, inherently possess rheological control and filtration prevention capabilities. Researchers have synthesized salt-responsive amphiphilic copolymers based on modified nanosilica, demonstrating excellent viscosity and low filtration even at 150 °C and 36% salinity .…”

Section: Introductionmentioning

confidence: 99%

Application of Hybrid Polymer Containing POSS as High-Temperature- and High-Salt-Resistant Fluid Loss Additive in Water-Based Drilling Fluids

He,

Li,

Shao

et al. 2024

Energy Fuels

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Deep drilling often faces high-temperature and high-salt environments. Bentonite/water-based drilling fluids (BT/ WBDFs) suffer serious fluid loss. Polyhedral oligomeric silsesquioxane (POSS) is introduced to synthesize a hybrid fluid loss additive (PAAD) through emulsion polymerization with acrylamide and 2-acrylamido-2-methylpropane sulfonic acid, and dimethyl diallyl ammonium chloride. The hybrid polymer exhibits improved filtration control performance in BT-WBDFs under conditions with a coexisting high Na + concentration and high temperature. BT-WBDFs added with 2.0% PAAD exhibit satisfactory performance for reducing fluid loss under contamination of 20% NaCl, even at a high temperature of up to 220 °C. Microstructure analysis demonstrates that forming "polymer−clay" gel through intermolecular forces and promoting clay particle dispersion efficiently stabilizes BT-WBDFs. PAAD shields against the adverse effects of Na + and promotes the hydration and dispersion of BT, finally forming a thin and compact filter cake. This study can provide a basis for applying POSS in the design of efficient additives for drilling fluids.

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Section: Introductionmentioning

confidence: 99%

Application of Hybrid Polymer Containing POSS as High-Temperature- and High-Salt-Resistant Fluid Loss Additive in Water-Based Drilling Fluids

He,

Li,

Shao

et al. 2024

Energy Fuels

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Prediction of Rheological and Filtration Loss Properties of Nano-Zirconium-Dioxide Drilling Fluids via Machine Learning Techniques for Energy Exploration

Jason,

Ilyas,

Ridha

et al. 2024

Lecture Notes in Civil Engineering

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Application of machine learning to determine the shear stress and filtration loss properties of nano-based drilling fluid

Ning

Ridha

Ilyas

et al. 2022

J Petrol Explor Prod Technol

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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.

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Predicting performance of SiO₂nanoparticles on filtration volume using reliable approaches: application in water-based drilling fluids

Cited by 7 publications

References 25 publications

Application of Hybrid Polymer Containing POSS as High-Temperature- and High-Salt-Resistant Fluid Loss Additive in Water-Based Drilling Fluids

Application of Hybrid Polymer Containing POSS as High-Temperature- and High-Salt-Resistant Fluid Loss Additive in Water-Based Drilling Fluids

Prediction of Rheological and Filtration Loss Properties of Nano-Zirconium-Dioxide Drilling Fluids via Machine Learning Techniques for Energy Exploration

Application of machine learning to determine the shear stress and filtration loss properties of nano-based drilling fluid

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Predicting performance of SiO2nanoparticles on filtration volume using reliable approaches: application in water-based drilling fluids

Cited by 7 publications

References 25 publications

Application of Hybrid Polymer Containing POSS as High-Temperature- and High-Salt-Resistant Fluid Loss Additive in Water-Based Drilling Fluids

Application of Hybrid Polymer Containing POSS as High-Temperature- and High-Salt-Resistant Fluid Loss Additive in Water-Based Drilling Fluids

Prediction of Rheological and Filtration Loss Properties of Nano-Zirconium-Dioxide Drilling Fluids via Machine Learning Techniques for Energy Exploration

Application of machine learning to determine the shear stress and filtration loss properties of nano-based drilling fluid

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Predicting performance of SiO₂nanoparticles on filtration volume using reliable approaches: application in water-based drilling fluids