Inspection/maintenance

ADNOC, Honeywell Team on Large-Scale Predictive Maintenance Project

ADNOC will utilize Honeywell’s asset monitoring and predictive analytics platform to improve asset efficiency and integrity across the operator’s upstream and downstream businesses.

Representatives of ADNOC and Honeywell at contract award
Source: ADNOC.

Abu Dhabi National Oil Company (ADNOC) and Honeywell have struck a 10-year agreement in which ADNOC will utilize Honeywell’s asset monitoring and predictive analytics platform to improve asset efficiency and integrity across the operator’s upstream and downstream businesses.

The Honeywell platform will leverage machine learning and digital twins to help predict equipment stoppages and reduce unplanned equipment maintenance and downtime, resulting in cost savings for ADNOC, the Abu Dhabi-based state-owned firm said.

ADNOC will deploy Honeywell Forge Asset Monitor and predictive analytics solutions at its Panorama Digital Command Center at its headquarters in Abu Dhabi. The command center aggregates real-time information across all ADNOC business units and uses data, analytical models, and artificial intelligence (AI) to generate operational insights and recommend new pathways. ADNOC said the addition of Honeywell’s solutions will enable the central monitoring of up to 2,500 pieces of critical rotating equipment across all ADNOC Group companies.

Insights into equipment health will also allow ADNOC to evaluate equipment overhaul extension programs with the aim of maximizing production. Built on a scalable enterprise platform, the Honeywell solutions will provide ADNOC engineers with a host of embedded data science and simulation tools.

The predictive maintenance project is part of ADNOC’s centralized predictive analytics and diagnostics (CPAD) program. CPAD underpins the company’s “2030 smart growth strategy” and “Oil & Gas 4.0” initiatives.

Other digital initiatives include its smart data analytics Thamama Subsurface Collaboration Center as well as the use of AI-assisted value chain modeling, rock image pattern recognition technologies, and blockchain-based hydrocarbon accounting.