and integrity across its upstream and downstream operations. Utilizing Artifical Intelligence technologies such as machine learning and digital twins, ADNOC’s predictive maintenance platform helps predict equipment stoppages, reduce unplanned equipment maintenance and downtime, increase reliability and safety, and is expected to deliver maintenance savings by up to 20%. Against a backdrop of unprecedented market conditions, the adoption of new technology remains at the heart of ADNOC’s strategy in maximizing the value from every barrel of oil, while delivering the greatest possible returns to the UAE.
The predictive maintenance project, which was announced in November 2019, is being implemented over four phases and is one of the largest in the oil and gas industry.
ADNOC’s predictive maintenance project is part of the company’s digital acceleration program, which focuses on embedding advanced digital technologies across the company’s operations.
The first phase of the project covers the modeling and monitoring of 160 major turbines, motors, centrifugal pumps and compressors across six ADNOC Group companies. All four phases of the project are expected to be completed by 2022 and the project will enable the central monitoring of up to 2,500 critical machines across all ADNOC Group companies.
The predictive maintenance platform is an integral part of ADNOC’s Panorama Digital Command Center at its headquarters and is implemented in partnership with Honeywell, using the Honeywell Asset Performance Management and predictive analytics enterprise solutions.
The predictive maintenance project is just one of many digital transformation initiatives by ADNOC to embed cutting-edge technology across its entire value chain. Other digital initiatives include its AI and big data-driven “Panorama Digital Command Center;” its smart subsurface data analytics “Thamama Subsurface Collaboration Center;” and its use of computer vision technologies, big data modeling tools for value chain optimization, and blockchain for hydrocarbon accounting.