How can data and AI make traditional energy businesses fit for the future?
However, today, we are living in more ‘energy-aware’ societies. We know a lot more about energy networks and how energy impacts the environment. Our understanding of our relationship with energy, the waste we generate and the way our energy is produced is also much more informed.
This prompts the need for oil and gas companies, especially traditional ones to reimagine the way in which they operate to both become more sustainable and transform how they are viewed in the minds of customers.
There is a clear paradigm shift taking place in the role that energy plays in society. The climate crisis can’t be ignored and industries and governments around the world are setting the goal of achieving ‘net zero’ carbon emissions.
Data and Artificial Intelligence (AI) will play a vital part in this paradigm shift for traditional energy businesses.
Challenges facing the industry
In 2009, the Fortune 500 had four energy companies in the top 10 by market capitalisation. In 2020, there were none. The rise of data as a valuable business asset during this past decade has been remarkable and traditional commodity and asset-driven businesses have struggled to embrace this. A remarkable seven of the top 10 Fortune 500 companies today could be seen as data-driven ones. To be data-driven in today’s business world is to be relevant.
The first step towards future proofing an industry which is being threatened by changing public opinion, the erosion of traditional business value drivers and technological shifts lies in re-establishing its purpose. Many traditional oil and gas companies have already made renewables a core part of their portfolios, but that only begins to scratch the surface when it comes to making the industry fit for the future.
Data has not really been at the forefront of their agenda, as, to date, it hasn’t been essential. However, in order to capitalize on the value drivers, the cost efficiencies and the new streams of revenue that data can bring to an industry, energy firms need to reimagine themselves and truly understand the data landscape.
A changing mindset
A relentless focus on putting data at the forefront of your business structure and utilising AI can yield many benefits. However, in many industries there’s a disconnect between an organisation’s desire to embrace new technology and its ability to deliver on the potential of that technology. This is especially true when it comes to so-called legacy industries whose systems, processes, mindsets and cultures are often based on outdated management principles.
The data and analytics landscape is growing more complex with data volumes of a typical energy company doubling every 12 to 18 months. The opportunities therefore for oil and gas companies to make better use of data is tremendous.
One key factor required to do this is to change behaviours or creating the conditions in which people can be incentivized to use data and, fortunately, in the energy industry many people already are – a technical analyst on board an oil rig, for example, works with data all the time. However, developing a data culture is central to understanding how that data connects and has an impact across the organisation.
Where to start?
Taking a data-driven approach to business processes means questioning the way things were done before and reimagining them. For many energy companies the promise of clean, connected data and AI-enabled business intelligence has not yet materialised. Often because sufficient maturity of both data foundations and operating models does not yet exist. However, by identifying these data maturity gaps and the constraints which cause them, energy companies can be free to realise the value of being truly data-driven.
For example, Accenture worked with a large oil company to re-assess their new well planning process. Traditionally, the methodology companies use for predicting time and cost involves using the nearest well to the current potential drill site as a source of reference. Reservoir engineers reference many reports containing millions of disparate data points for the nearest well such as manual drilling logs, engineering reports, lithology sequences, shoe casing size and Non-Productive Time (NPT) events to predict the time and cost to drill a new well. However, a data-driven approach means that the net is cast wider and those same data points are taken from multiple wells around the new location, in this case the entire North Sea, to build a more accurate time and cost forecast.
The traditional approach has a 75% similarity rating (i.e. how similar a new drill location is to an older drill location across different variables) and involves up to 12 months in planning while the data-driven approach has a 94% similarity rating which improves as the model learns and only takes 2 months of planning, with the majority of that time spent on data management activities.
This approach beats gut feel and is a better way to improve efficiencies, in some cases saving millions of pounds. Once the first model is built, the new well data can simply be added to the model to drive further efficiencies in the planning process for new well sites.
Data is far from being a silver bullet for the industry but can be used now to improve processes, reduce costs and increase value. This, in turn, will better enable the industry to achieve goals such as carbon neutrality, reduce reliance on fossil fuels and usher in greater efficiency gains, new business models and new revenue streams.
Just as oil is essentially valueless until it is refined, the same can be said for data – it will take a root and branch adjustment to reinvigorate the energy industry and ensure it is one that is fit for the future, for all of us.
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