Predicting performance of SiO₂ nanoparticles on filtration volume using reliable approaches: application in water-based drilling fluids

ABSTRACT

Designing the drilling fluid is crucial in order to make a thin and impermeable filter cake on the wellbore that minimizes the drilling fluid filtration volume. Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Interference System (ANFIS), and Least Square Support Vector Machine (LSSVM) modeling are used to predict the drilling fluid filtration volume as a function of drilling fluid properties, SiO₂ nanoparticles, and KCl salt concentration. The performance of the proposed models is evaluated using statistical parameters including Average Absolute Relative Error (AARE), Mean Square Error (MSE), and Correlation Coefficient (R²). The results show that the models could predict the filtration volume well and are in agreement with the actual measured values. The proposed ANN, ANFIS, and LSSVM models could predict the filtration volume with R² values of 0.9926, 0.9900, and 0.9940, respectively. This indicates that the presented models are accurate enough for predicting the nanoparticles performance on filtration volume of water-based drilling fluid.

KEYWORDS:

• Filtration volume
• nanoparticles
• ANN
• ANFIS
• LSSVM

Additional information

Notes on contributors

Alireza Golsefatan

Alireza Golsefatan is a Ph.D student of Petroleum Engineering at the Petroleum University of Technology. He received his M.Sc. in Petroleum Engineering from Petroleum University of Technology. His research interests including Reservoir Simulation, Nanotechnology in Petroleum Industry, Artificial Intelligence Tools, Modeling and Programming.

Khalil Shahbazi

Khalil Shahbazi is an Associate Professor of Petroleum Engineering at the Petroleum University of Technology. He received his Ph.D in Petroleum Engineering from University of Calgary. His research interests including Conventional and Underbalanced Drilling, Drilling Fluids and Rheology of Non-Newtonian Fluids, Hydraulic Fracturing Modeling, Applications of Finite Difference in Wellbore and Near-Wellbore Flow, Multi-Phase Flow in Wellbores and Fractures.