modeling

The research facility said it plans to add multiphase-flow-testing capabilities for heavy oil and different viscosities.

In this study, a method was developed to analyze the effects of drilling through transitions on bit-cutting structures and construct an ideal drilling strategy using a detailed drilling model.

This paper describes a machine-learning approach to accurately flag abnormal pressure losses and identify their root causes.

Data and impartial viewpoints can help de-risk exploration portfolios and keep resource estimates in check.

This research aims to develop a fluid-advisory system that provides recommendations for optimal amounts of chemical additives needed to maintain desired fluid properties in various drilling-fluid systems.

The objective of this microfluidic investigation is to identify and test two novel applications for magnetic fluids in porous media for subsurface oilfield applications.

This paper describes a study to design and implement an enhanced oil recovery project via huff ’n’ puff using Y-grade injectant.

This paper discusses a comprehensive hybrid approach that combines machine learning with a physics-based risk-prediction model to detect and prevent the formation of hydrates in flowlines and separators.

This paper presents an approach to subsea hydrate-risk management based on the understanding that some crudes have induction properties that delay hydrate formation even when the pressure and temperature conditions reach the hydrate thermodynamic region.

This paper describes an automated workflow that helps mitigate sanding caused by excessive drawdown by determining the minimum tubinghead pressure.