Murphy Oil Corporation

Routine status reporting often presents a challenge because of its intimidating and time-consuming nature for both employees and supervisors. With large language models, a system was developed to generate coherent artificial-intelligence-driven reports. The goal is to enhance the understanding of overall insights and reduce the time required for individual report reading.
Moksh Dani, Sean Petersen, Minh Nguyen, Ngoc Thao Van Phan, Huzeifa Ismail
Murphy Oil worked across its assets to create a tool called the Global Downtime Report (GDR). The GDR provides a global perspective of operational downtime data, focusing on nonproductive time related to drilling, completion, and production operations.
Huzeifa Ismail, Laura Naaykens, Mark Mick
Economical oil extraction depends largely on reducing wear and tear of downhole equipment and tubulars through regular monitoring and batch treatment to cut the risk of corrosion and costly workovers. Having a data-driven approach to scheduling batch treatment can bring down the frequency of treatment and avoid the unnecessary use of resources.
Tom Krawietz, Alex Ostertag, Huzeifa Ismail
Implementing a Metric-Driven Chemical Management Program Presentation
Dr. Huzeifa Ismail, Leslie Malone
A technology initiative is changing more than the digital landscape at Murphy Oil. Since its deployment in November 2018, Murphy Labs is changing the very culture of the corporation.
Eric Hambly, Sean Aslam, Huzeifa Ismail
A comprehensive, digitized water-management application has been designed to streamline and enhance the monitoring and management of water resources used in hydraulic fracturing.
Adam McConnell, Molly Smith, Huzeifa Ismail
Tropical storms severely affect oil and gas production in the Gulf of Mexico, especially during the storm season from June to December. Offshore well managers often need to shut down operations and evacuate the facility because of storm alerts. The purpose of this paper is to determine the effect of storms on production by quantifying metrics such as downtime days and downtime percentage after the storm has passed and whether a facility’s platform type affected these metrics.
Olasimbo Ogunde, Huzeifa Ismail
Murphy Oil has created a work flow to normalize the tags it uses when collecting data on its hydraulic fracturing stages. The work flow described here empowers decision makers, who no longer wait for hours to collect data or waste hours cleaning and preparing data for analysis.
Molly Smith, Sarah Carr , Huzeifa Ismail
This study examines the implementation of a predictive maintenance method using artificial intelligence and machine learning for offshore rotating production-critical equipment. Conducted over 2 years at Murphy Oil’s deepwater platforms in the Gulf of Mexico, the project aimed to detect equipment issues early, reduce downtime, and streamline maintenance processes.
Bilal Hussain, Andrea Vigueras, David Jones, Graeme Smith, Huzeifa Ismail
In 2018, Murphy Oil launched an initiative to open-source all internal live streaming data to its engineers. The process led it to implement a global data historian and a corresponding visualization tool. The insights provided are shaping the future of operations at the company.
Huzeifa Ismail, Francisco Ruiz, Thomas Nix