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Imagine for a moment that you are drilling a well in the Gulf of Mexico. Similar wells, with the same ‘signature,’ have been drilled before in the North Sea, the learnings from which could be valuable. It can be costly and time-consuming to reinvent the wheel with each new well, which is why using data drawn from similar experiences can help optimize your operation.

It all sounds very logical, but the truth is that for most drilling operations, the data is still locked in silos. We are essentially drilling in the dark, and not making decisions based on the volumes of historical and real-time data that could be at our fingertips. From common safety hazards to gas peaks and best practices, relevant data can serve as a guide for achieving greater optimization and efficiency with each new well. The question is: how do you make it happen?

Industrial DataOps drives confidence in decision-making

That’s where Industrial DataOps comes into play. It’s a relatively new discipline, sometimes referred to as the distant cousin of the more commonly known DevOps. The idea behind Industrial DataOps is that it requires a broad shift in how you think of and work with data. In an Industrial DataOps world, the data no longer resides with the data experts alone, but rather it is released from silos, contextualized and cascaded across an operation. The data experts are still essential, but their job is to support an overall data-focused enterprise. It’s about collaboration, integration and automating data flows so that everyone across the organization can make use of this valuable information.

Production engineers who are tasked to design and drill new wells are not strangers to the idea of evaluating similar wells before drilling begins. However, all too often, this is still a manual (and often non-digital) task requiring significant effort to retrieve that critical information and then put it in context. Now imagine doing that at scale across the operation. The time and effort required are simply too costly.

If we instead look at this task through a DataOps lens, the organization would have already recognized the need to bring both historical and real-time data together on a single platform, organize and contextualize it, and then make it usable for the engineers—so that they can easily access the well history or relevant drilling reports without having to hunt down random spreadsheets.

I like to think of this as a bit like accounting. If each department kept its financial information locked away in their individual filing cabinets, it would be extremely challenging and time-consuming to get an overall picture of the financial health of the company. And it would be nearly impossible to plan, strategize and make necessary adjustments for the future. It’s the same with data. It has little to no value when in confinement.

DataOps means unlocked and contextualized data, always available, to support production engineers

Once your organization gets behind DataOps as a tool for their digital transformation, there are a few key benefits that will emerge in time.

1. Data-backed visualization means better decisions

By liberating your data from individual silos, you can deliver it to your preferred visualization tools where engineers can access it and use it to improve decision-making.

2. Diagnose problems and get insightful recommendations

Data plus domain expertise is a winning combination. When production engineers can harness the power of machine learning and physics-based models, they are able to diagnose problems more accurately and get recommendations for corrective actions.

3. Less monitoring time, more value generation

Once data is organized and contextualized on a shared platform, it can be deployed across the organization to continuously deliver alerts as needed—reducing time spent on routine monitoring.

4. Quicker and cheaper proofs of concept

Industrial DataOps can help reduce time to realize value, giving engineers the tools to make proofs of concept that are quicker and easier to design, along with the tools to operationalize and scale them.

5. Improve and accelerate sustainability efforts

Not only can Industrial DataOps help improve drilling efficiencies, it also impacts sustainability efforts by connecting drilling data to rig data for emissions or power consumption monitoring. Production engineers will increasingly need to learn from other, low carbon footprint use cases, so that they can implement similar tactics in their own drilling operations.

ESG targets are unavoidable; it’s time to improve

ESG are the new measuring stick by which all oil and gas operators will be compared, and it’s those who figure out how to use the data to reduce their impact who will come out the winners. While 77% of U.S. Energy Leaders in a recent Harris Poll report said technology is an immediate solution to address environmental sustainability, only one in four industrial companies are extracting value from data to a significant extent, according to market intelligence provider IDC. They simply don’t have the right tools and processes to do so. The quick fix is to centralize all the data in a single place, but this can lead to ‘data swamps’ that lack meaning and give little value. The trick is to organize the data instead, which is at the heart of the Industrial DataOps discipline.

There’s still an enormous opportunity to be the frontrunner in realizing digital value in drilling operations. The key will be in ensuring that everyone—from humans to machines to systems—can access the data and leverage it, using it to improve sustainability, safety, efficiency, and realize value even quicker. Data has become the best drilling tool available today—when used in the Industrial DataOps way.

Francois Laborie serves as North American president of Cognite.