How the UK water industry could learn from oil and gas

It is an exciting time for the UK water industry as it approaches the completion of the first year of the PR 19 regulatory period. The sector is undergoing massive transformation amid a market redesign called for by regulator Ofwat to improve performance, lower customer costs, enhance environmental stewardship, and build in resiliency to future-proof itself against disruption. The next four years of this regulatory period will play an integral role in shaping the future of water in the UK, say EY’s Mark Deighton and Raj Malayathil.

In the coming months, we will be coming together with Microsoft to present a series of articles that take a deeper dive into several key priorities for the UK water sector, including driving efficiencies cultural transformation, creating a business model to prepare for increased societal debt while delivering strong customer experience. In this article, we examine how the water industry could learn from oil and gas companies to drive operational efficiencies through adoption of technology and cultural transformation.

The two sectors make for easy comparison because of their asset intensive business and an execution environment that is highly process- and task-centric. They also take a natural resource and refine it to provide a product for which the environmental responsibilities are paramount.

The transition to a low-carbon future has pushed oil and gas companies to reinvent themselves, and to use technology to improve operational efficiency and sustainability. Similarly, the targets set by Ofwat in PR 19 – to cut leakage rates by at least 15%, reduce mains bursts by 12%, and lower consumers’ water and wastewater bills – are expected to drive transformation in the water industry. Water companies have a great opportunity to take some lessons from oil and gas sector’s history of bold structural moves, adoption of innovation, and operational transformation.

Digital twins

A close examination of the oil and gas sector’s uptake of digital technologies and data is a good place to start. BP, for instance, has built APEX, a highly sophisticated digital twin that recreates every element of a real-world plant in digital form. This digital twin leverages an integrated asset data model and advanced analytics algorithms to spot issues in a plant before they have major effects on production. For system optimisations, what would normally take 24–30 hours of engineering time can now be completed in 20 minutes with APEX.

Water companies are now beginning to get their feet wet with the use of digital twins too. Pioneer Anglian Water has started building a digital representation of the region’s water treatment and distribution infrastructure and embedded an AI system to incorporate predictive capabilities and intuitive decision support into operations.

Water companies can learn from how oil and gas companies started small and rapidly scaled these advanced technology platforms – and the opportunity exists now to apply these learnings to their industry’s value chain for multiple assets, such as pumping stations, pipe networks, and treatment works. For instance, digital twins could also be used to optimise pump transitions in a pumping station, to reduce power demands and save water.

In another example, Shell introduced predictive maintenance and machine learning at its Pernis refinery, where data is collected from 50,000 sensors. Through use of AI, its model can predict failures in control valves and limit costly unplanned downtime. Shell estimates the technology provided cost savings of US$2 million in its first two weeks, and the company has now scaled it across its refineries. By taking a big-picture outlook, rather than adopting an incremental plan, oil companies’ use of technological advances is bringing transformational outcomes.

Recognising the enormous role big data can play in asset optimisation, oil companies have invested in cloud computing, thereby putting the tools in the toolkit for their engineers. For instance, XTO Energy, a subsidiary of ExxonMobil, uses Microsoft Azure as a unifying platform for all its data, allowing both office and field workers to make sense of vast quantities of information, empowering them to make better decisions and better troubleshoot mechanical failures to limit downtime.

Certainly, the opportunity for water utilities to deploy big data and analytics platforms is there for the taking. Applying machine learning to pressure and flow data sets could help detect leakages and sewerage obstruction in advance, to avoid flooding and interruptions to supply. Using geospatial analytics could also help locate leaks more efficiently – and at a significantly lower cost – than legacy techniques such as acoustics.