_ In my 30 years in the energy industry, I don’t think I’ve seen anything capture our collective imaginations quite as much as digital has—not only in our industry, but also in our personal lives. We’re surprised—and often amazed—by the way digital has changed our lives with smart phones, streaming, and online connections. We’ve seen the medical industry transform patient well-being and retail supply chains become vibrant when they’re underpinned by strong digital tools. Irrespective of what’s happening elsewhere, however, we must admit that the oil field is still in the early days of its digital journey. There are many reasons for our slow start, of course, and the intention of this editorial is not to belabor them; instead, I want to provide a good dose of pragmatism as we consider what’s next for digital in the oil field. At this juncture in our journey, it’s time to give serious thought to the expectation-reality gap … the cultural differences between the way we’ve always done things in the oil field and the way that digital is changing us … and the pain points that may trip us up unless we’re careful. Data, Digital, and Expectations In oil and gas operations, we collect enormous amounts of data—and we should expect that this data contains insights to help us streamline operations; orchestrate activities across multiple stakeholders; deliver more efficient, consistent outcomes; and maximize recovery. The challenge is that data doesn’t mean much by itself. I agree with mathematician Clive Humby, who referred to data as the “new oil.” Oil must be refined into products—such as gasoline or plastics—to create value. The same is true of data which, when refined, can be used to understand reservoir characteristics, calculate nonproductive time, and myriad other things. But unlike oil, data is a renewable resource—which enables it to create additional value on new and different levels. We can use refined data to make decisions, refine it again to make different decisions, or even go back to the original data and reprocess it to gain new insights. We can apply data to models repeatedly as they evolve, improving clarity around particular challenges, or finding new ways to solve problems. But, in the end, data is still only data. And that’s why we’re so focused on digital. Digital gives us the tools to do something with the data. There’s an interesting construct here. Just as data can’t do much by itself, digital is simply a box of tools—data analytics, visualization, the internet of things (IoT), machine learning, digital twins, cloud capabilities, and more. The relationship between data and digital is necessarily symbiotic—and we will do well to remember this when we set expectations for what digital can and cannot do.
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Jan 22, 2024
Ravi Srinivasan + 2
Open Access