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ADNOC and Honeywell to undertake predictive maintenance project

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Oilfield Technology,


The Abu Dhabi National Oil Company (ADNOC) has announced a partnership with Honeywell as it embarks on one of the largest predictive maintenance projects in the oil and gas industry. Under the terms of a 10-year partnership agreement, ADNOC will utilise Honeywell’s asset monitoring and predictive analytics platform to maximise asset efficiency and integrity across ADNOC’s upstream and downstream operations.

Leveraging Artifical Intelligence (AI) technologies like machine learning and digital twins, the platform will help predict equipment stoppages, reduce unplanned equipment maintenance and downtime, increase reliability and safety, and enable substantial cost savings for ADNOC.

ADNOC’s predictive maintenance project is part of the company’s flagship Centralized Predictive Analytics and Diagnostics (CPAD) programme. CPAD, which underpins ADNOC’s 2030 smart growth strategy and Oil & Gas 4.0 initiatives, aims at transforming the company’s operations to maximise value from every barrel of oil, while delivering the greatest possible returns to the UAE.

His Excellency Dr. Sultan Ahmed Al Jaber, UAE Minister of State and Group CEO of ADNOC, said: “In an era where the oil and gas industry is being disrupted on multiple levels, it is important that we continue to drive innovation and seek new advanced technologies to further optimise performance and deliver sustainable returns. ADNOC’s predictive maintenance project is significant in scale and in its application of AI technology, and marks an important technological milestone for the industry. Honeywell was selected as our valued technology partner to provide these state-of-the-art solutions after a highly competitive and rigorous process.”

ADNOC will deploy Honeywell Forge Asset Monitor and Predictive Analytics solutions at its Panorama Digital Command Center in its headquarters. The Panorama Digital Command Center currently aggregates real-time information across all business units and uses smart analytical models, AI and big data to generate operational insights and recommend new pathways. The addition of Honeywell’s solutions will enable the central monitoring of up to 2500 critical rotating equipment across all ADNOC Group companies.

Darius Adamczyk, chairman and CEO of Honeywell said: “We are proud to partner with ADNOC as they lead the transformation towards Oil & Gas 4.0 and embrace the power of IIoT. Honeywell Forge delivers the advanced capabilities business leaders need to harness big data to drive more efficient decision-making and exceed business goals. We look forward to ADNOC realising these benefits after deploying this leading-edge technology.”

With continuous online monitoring of equipment, aided by machine learning analytics and digital twin models, operators and maintenance personnel at ADNOC will be able to identify impending machinery issues earlier, and shift from reactive and preventative maintenance practices to a predictive maintenance approach.

Sophisticated insights into equipment health will also allow ADNOC to evaluate equipment overhaul extension programs, and increase the availability of equipment and maximise production. Built on a scalable enterprise platform, the Honeywell solutions will accelerate time to value by providing ADNOC engineers with a host of embedded data science and simulation tools.

The predictive maintenance project is one of many digital transformation initiatives by ADNOC to embed cutting-edge technology across its entire value chain and deliver on its Oil & Gas 4.0 mission to stay ahead of the world’s growing demand for energy. Other digital initiatives include its Panorama Digital Command Center; smart data analytics Thamama Subsurface Collaboration Center; and the use of AI-assisted value chain modelling, rock image pattern recognition technologies, and blockchain-based hydrocarbon accounting, to name a few.

Read the article online at: https://www.oilfieldtechnology.com/digital-oilfield/22112019/adnoc-and-honeywell-to-undertake-predictive-maintenance-project/

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