modeling

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 presents a closed-loop iterative well-by-well gas lift optimization workflow deployed to more than 1,300 operator wells in the Permian Basin.

This paper explores the use of machine learning in predicting pump statuses, offering probabilistic assessments for each dynacard, automating real-time analysis, and facilitating early detection of pump damage.

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

This paper focuses on developing a model that can be used in an automated, end-to-end flare-smoke detection, alert, and distribution-control solution that leverages existing flare closed-circuit television cameras at manufacturing facilities.

This paper describes an alternative lower-completion concept for developing Lower Wilcox reservoirs referred to as high-angle multifractured well design.