31 May The Battle For Data Is Hotting Up
The battle for access to data in oil and gas is seriously hotting up. It’s not too late to get in the fray.
Data – The New Battleground
The debate within oil and gas companies about whether digital is a thing is now well and truly settled. First publicly highlighted by the IEA in 2017 in its seminal review of the impacts of digital on energy, and given a huge push in 2020 as the industry responded to COVID 19, digital innovation is not only here to stay but the battle grounds are taking shape. Among the largest industry participants, data acumen, data insights, data driven business are now central themes.
The challenge is that after a decade of relentless pressure on margins, volatile prices and unprecedented levels of workforce displacement, the industry has fundamentally reset its cost structure to cope with what could be lower pricing forever. The leading candidate opportunity that can help the industry progress is in approaching data differently. The oil and gas industry is exceptionally data rich, and generates terabytes by the minute.
Data issues and opportunities are fiendishly hard to articulate and generally fail to inspire a response inside most oil and gas outfits. This is now risky as capital markets keenly reward companies that have a strong value proposition based on their data assets. The largest companies in the world by market capitalisation for the past three years are companies that merchant primarily in data (Amazon, Apple, Alphabet, Facebook, Microsoft). The largest companies in the world by revenue include several oil and gas companies like Sinopec, Shell and PetroChina. Capital and investment has shifted decisively towards those companies with an explicit or implicit data strategy.
As I see it, the challenges of data management internal to most industrial concerns are like the minor tribal battles of ancient cultures. Ask employees in the pipeline, upstream or LNG industry about their experience with data and they can all point to the litany of little issues that frustrate them:
- No clear vision from company management about the role and importance of data to the company.
- Industry software product designs that imprison data inside and thwart efforts to release.
- A tendency by department heads and technical experts when purchasing new software to overweight analytic functions, and underweight data features
- A resulting landscape of information islands or silos, with multiple definitions of such basic things as gas wells and compressors
- A huge gap between the operating data captured by PLCs, DCS’s, and SCADA historians, and the cloud environments housing commercial data and the world’s best analytic engines
- A reliance on Excel to solve for all manner of data-related problems, such as facilitating the exchange of data between two incompatible systems, manipulating data, and visualising data via charts and graphs.
- Accounting rules that treat data as a cost to be managed, rather than an asset to exploit.
- Unclear responsibilities for data ownership, and an inability to articulate the capital requirements and benefits from better data management practices.
- Few performance metrics that are specific to data quality.
- Internal competition for capital between divisions that gives rise to active measures to thwart data sharing, collaboration and mutual support.
It’s baffling that the country of Estonia, with 6 million citizens, has legally mandated that the government cannot ask its citizens for the same data twice. Once you have provided your home address or birth date, or tax number to a government department, just once, that’s it. They can never ask you for it again. Their systems have to sync up, by law. That’s vision.
I don’t spend much time working in other industries, but I suspect these kinds of challenges are endemic to many sectors, particularly those that are heavy asset centric (where the cost of such assets is substantial and tends to dominate balance sheets).
Oil and gas services companies and technology providers now look at this state and sense an opportunity in the making.
The war for data supremacy is underway
Many years ago, I worked with a major fracking outfit who, as a way to high-grade their quality of service, connected all their frack spreads to their in-house control room. Frack data flowed continuously into this facility, frequently via expensive satellite uplinks. The company’s best engineers and fracking experts spent their days in the control room guiding the fracks, tuning the horsepower, dealing with upsets and generally keeping the assets as utilised as possible. They did nothing with the data once the frack was over. Today, however, they keep the data.
Four years ago I met with a downhole tool company whose measurement devices provided highly reliable visibility to production conditions — water pressures, temperatures, radioactivity, flow rates, volumes and many other data points. They sold the tools to their customers (in this case, gas well operators), who used the data to manage well performance, schedule services and forecast production.
I asked them at the time why, in an emerging world of cloud computing and analytics, did they chose not to capture the data themselves and interpret it as a service for their customers. They could have built up an enormous library of well operating conditions. Too hard, they claimed, even though their own product testing regime included the kinds of analytics that their customers valued.
Today, shrewd field services companies and hardware suppliers are now woke to the potential value of the data. Queries about who owns the data (paid for by the exploration company, generated by the hardware, collected by the service company) elicit a mumbled and vague response. Service contracts are silent on who owns the data, and what restrictions there might be on the use and sharing of the data. For the moment, producers are ok, if a bit uneasy, with the Drillco/Frackco/Serviceco “utilizing” the data, provided it’s not shared.
What can you do with a huge ocean of data? Why, feed it into an artificial intelligence engine and see what you can make of it. That’s how the voice engines in Siri, Alexa, and Google Home have gotten better and better. Just for fun, pull up your browser and play Google Quick Draw, an AI engine that interprets doodles and guesses what you’re attempting to draw. It’s amazing how good it is. It works because millions of people are playing with it at the same time, feeding it millions of data points.
Using AI, a fracking firm like a Calfrac gets smarter and smarter over time about frack design and execution, because it has accumulated hundreds of frack histories, and offers a superior fracking service. A drilling company does the same thing with drilling data (see Pason), and a compression company with turbines (see GE). More and more sensors will be added to industrial infrastructure over the next 5 years, rising from 8 billion in 2016 to 20 billion by 2025, and business model designs are separating sensor ownership from the data the sensor generates. That’s a good thing. But petroleum companies are alarmed at the prospect of unintentionally enabling service companies to create monopolies within their business models and the risk that the data companies proceed to extract rent.
Another looming scenario features all that data tied up in the clutches of a dozen different companies, and not just in upstream areas, but operating data for pumps, compressors, and engines. Getting it back won’t be easy. Linking your fracking data from one company with the drilling data from another company to the artificial lift data from a third company requires clever integration, platforms, or both.
Apple has changed the rules on its platform to require the user to agree to share their personal data with apps. Sure, inadvertently sharing personal or corporate data is a risk concern, but in a world of AI, that’s yesterday’s risk. Today’s risk is business model shift.
How the battle is shaping up
The battleground for data in oil and gas is taking shape, and the players (owners, EPC firms, hardware suppliers, services companies, cloud firms, platforms) are taking their respective positions:
- The strategic objective is to own the data resource (similar to Google’s mission to organize the world’s data), and leverage AI tools to extract value.
- Some service companies are stealthily on the offensive, and are increasingly concentrating their investment dollars on this objective.
- They are maneuvering into positions of maximum advantage over their quarry (oil and gas companies).
- By offering seductive terms (subscription models, no money down) and avoiding the strategic discussion (who owns the data, data sharing, data usage), they are securing their high ground strategic location for the future.
- Most oil and gas companies are complacent or unaware of their precarious valley position.
- The surprise on the battlefield will come in the form of pricing power or restrictive feature access.
It’s time to strategize.
This article first appeared on December 17, 2018.
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