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Success stories show how advanced analytics software is used to create insights and improve process efficiency. See three use case examples.
Decades of carbon capture experiments with oil, gas and coal missed targets and sources of emissions
Oil & Gas Engineering targets engineers responsible for business-critical functions, technology and processes from the wellhead to the refinery. These professionals design, specify, implement, upgrade and maintain equipment in the areas of automation, process management, cybersecurity, asset management and worker safety.
Increasing adoption of analytics bridges the gap between process experts and data scientists, encouraging manufacturing collaboration
Original air date
September 17, 2021
Hiber to deliver well-integrity monitoring solutions for energy giant Shell; HiberHilo solution improves well safety and sustainability
Identifying efficiencies in exploration and production is paramount for recovery and competitiveness
How self-service analytics contribute to asset reliability and operational performance
Case study examples demonstrate real world value
Search for improved data use to address industry challenges
BOP system tester uses analytics software to help its oil & gas clients avoid downtime and incident
With a GPU-accelerated database, organizations evaluate larger data sets.
Advanced analytics use machine learning to simplify user experience, generate process data insights.
Solution identifies production and equipment problems before they become apparent.
Upstream oil & gas operations improved using data analytics.
Oil & gas revenue increases lead 4% sector growth according to a report from IHS Markit.
It’s critical to have the proper framework and cybersecurity measures in place to help prevent cyber attacks for cloud-based deployments of supervisory control and data acquisition (SCADA) systems.
A research report by Ponemon Institute indicates that while oil and gas cybersecurity is strong, the industry is note keeping pace with technology developments and their cyber readiness is not high.
By integrating seismic, geological, and engineering data, Seismic-Based Analytics workflow identifies the most prospective areas and zones for drilling down to the stage level.