modeling

This paper investigates the use of machine-learning techniques to forecast drilling-fluid gel strength.

This paper aims to close some of the many knowledge gaps that exist in the field of drilling CO2 storage wells, a task that is expected to involve handling an influx of CO2 into the drilling fluid.

This paper presents a specialized workflow that aims to quantify the severity of condensate banking and subsequently optimize reservoir development strategies for a deep formation in the Permian Basin.

This paper describes numerical modeling studies of fracture-driven interactions using a coupled hydraulic-fracturing-propagation, reservoir-flow, and geomechanics tool.

This paper describes the suite of cloud-based digital twin tools that the operator has developed and is integrating into its operations, providing online, real-time calculation of scale risk and deployed barrier health to manage risk on a well-by-well basis.

This paper presents a comprehensive literature review and critical examination of the published modeling and experimental studies regarding the recovery mechanisms of cyclic gas injection and the conditions under which the process can enhance oil recovery with the aim to identify lessons learned and areas in need of further study.

This paper describes installation of autonomous inflow control valves in the Bretaña Norte field in Peru, enabling effective water control even though the trial well was placed in the flank, close to the oil/water contact.

New and evolving artificial lift technology is helping operators improve production rates.

This paper presents the processes of identifying production enhancement opportunities, as well as the methodology used to identify underperforming candidates and analyze well-integrity issues, in a brownfield offshore Malaysia.

This paper presents a workflow that combines probabilistic modeling and deep-learning models trained on an ensemble of physics models to improve scalability and reliability for shale and tight-reservoir forecasting.