Can Artificial Intelligence and Machine Learning Predict the Performance of Nano-based Drilling Fluids? A Review

Abstract

Drilling fluids play a crucial role in the control and functionality of oil and gas well operations. Continuous monitoring, enhancement, and optimization of their properties are essential for successful drilling processes. Recently, a variety of additives, including nanoparticles (NPs) and novel polymers, have been introduced to modify and improve the performance of drilling fluids, addressing the emerging challenges in the field. The behavior of these fluids can change over time or under extreme drilling conditions, necessitating the use of predictive models to optimize their properties, particularly their rheological characteristics. In the past decade, there has been a growing trend of developing new models and correlations through artificial neural networks (ANN) and machine learning (ML) techniques within the petroleum industry. These methods enable the development of mathematical formulas that can predict the behavior of specific parameters based on known variables. Compared to traditional models, ANN and ML offer enhanced reliability and accuracy in predicting drilling fluid properties. This review aims to provide a comprehensive overview of the latest applications and mechanisms of various additives, with a particular focus on NPs, in drilling fluids. Additionally, it highlights the valuable insights and advancements in using ANN and ML techniques to predict and optimize the behavior of drilling fluids, which could pave the way for innovative applications and more efficient utilization of these technologies.