Predictive maintenance for upstream and downstream

Transcription

1 Digital Technologies INVESTMENT OPPORTUNITY: Predictive maintenance for upstream and downstream

2 Executive summary Predictive maintenance is designed to help monitor and determine the condition of in-service equipment in order to predict when maintenance should be performed using big data analytics. There is an opportunity for local vendors in Qatar to deliver services needed for implementation, including Consulting. Services related to data structuring, cleaning, and preparation followed by model and dashboard building. Technical implementation support. Implementation of infrastructure and system integration. Energy players in Qatar have already started the journey towards digital equipment monitoring and management but have not yet started implementing advanced predictive analytics models for critical assets. Over five years, Energy players in Qatar are expected to roll out predictive maintenance across approximately 50 upstream and downstream facilities. The total demand for predictive maintenance (implementation and ongoing support) is forecasted to be a total of approximately QAR 400M over five years. Land allocation in a free zone, favorable ICV-backed procurement schemes, and pilot/proof of concept opportunities to showcase capabilities will be considered to incentivize localization and ensure value capture from predictive maintenance for the Energy sector. 2

3 Detailed opportunity description Text Predictive maintenance on upstream and downstream assets Predictive maintenance combines historical conditions and process data with real-time information (from existing and new IoT sensors) to predict equipment failures more accurately and avoid unplanned shutdowns. There is potential to localize part of this demand by incentivizing established international vendors to increase presence in Qatar and encourage development of local SMEs for future implementations on noncritical assets and ongoing support. Localization opportunity Business scope Predictive modeling of equipment failures of production assets, including Data structuring, cleaning, and preparation Data analytics and modeling Change management and training Infrastructure implementation System integration (firewalls, protocols) Out of scope Software and licenses Analytics platform for development and testing of new models Front-end visualization and applications for maintenance personnel Hardware Equipment, sensors, or operating technology IT infrastructure/infrastructure as a service (IaaS) Capital intensity Low (QAR 0-10M) Medium (QAR 10-50M) High (QAR 50M+) 3

4 Market size Estimated spend (Qatar only) on predictive maintenance, QAR M Implementation spend Ongoing support and maintenance Assets predictive maintenance rolled out, number Key highlights Predictive maintenance is currently being piloted in a few companies in Qatar and is expected to be implemented in most Energy players in the future. Within five to seven years, implementation is expected in upstream and downstream facilities (including LNG trains). The total consulting cost per facility is estimated to be USD 1-1.5M from an average of approximately ten critical equipment assets in a facility. This includes a data readiness assessment, components selection, data collection and preparation, model building, and system integration. There is an opportunity to localize part of future predictive maintenance demand for services. There is potential for growth through the emergence of new markets for machine learning and AIbased services and software. Relevant stakeholders Potential buyers* All Qatari Energy players Transportation, heavy industries, automotive *Examples shown not exhaustive 4

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