One Platform: A built-for-purpose oil and gas data model with applications that allow all team members to communicate, deliberate, and decide.
In the Common Tongue*: An automated data pipeline that’s built so that any member of any team can access the decision workflow. No handoffs, you work inside the platform together.
One Platform for all—for geologists, for reservoir engineers, for completions engineers, for economic analysts. One place, one set of data, one platform of workflows that is accessed and shared for all inputs and outputs. And dare we add, a way to communicate and reference each other and even build your own applications inside? “Petro.ai is laying the groundwork for a real transformation in the way people process and use data in unconventionals to make decisions,” Dr. Brendon Hall, VP of Geoscience reveals, “Until now, workflows have been tool focused and siloed between disciplines. The geoscientists use geoscientist applications, the engineers use engineer applications. Or maybe everybody uses excel, just not the same sheets.
“We just had a conversation this morning with a geoscientist. We were talking about the economics model he was going to use and he said, ‘Yea I’ll pull in the economics team to look at that.' And I said, 'Here’s the great thing. In Petro.ai, you can send them a link to the same application that you’re using, and they can add their inputs and see what effect that has on the outcome.They can be using the same application you are with a place for their data to go.’ That’s not been possible before.”
“When people think about gas and shale workflows, it comes down to data and disciplines,” Kyle LaMotta, VP of Analytics adds. "Your data is always hard to connect and then working among disciplines is difficult because people are siloed, the data is siloed and there’s just so many different applications that are used for their day-to-day workflows.
“I think one of the biggest gaps for shale workflows is in the data to do any sort of analysis from picking a well location to drilling wells or deciding on a completion design and then monitoring production and running economics. All those different tasks require data to make a decision. And they’re all typically in different software programs.
“The base level of Petro.ai is a built-for-purpose oil and gas data model. We’re bringing geoscience and engineering data types into one system.
A lot of tools today, even the new ones, are built for a specific purpose whether it’s looking at drilling data or modeling frac designs or making type curves or decline curves. Each one of those applications is going to have a data model that’s specific for that workflow but they’re all disconnected so they’re not connecting to the next step in the workflow.
“In the Petro.ai platform we’re starting with a data model that has all those engineering and geoscience data types that you would need to run these different workflows. It’s the only integrated oil and gas data model built for unconventionals. Our goal is for Petro.ai to be a stand-alone platform. It’s something that we work towards. It should be the only software that you use for any sort of shale workflows.
“We also understand that data integration goes both ways. Because of that, Petro.ai has an easy data integration from the source data whether it’s public data subscriptions or the output of your completions and frac hit data or structure grids. It’s all this information that’s coming from different sources and we make that data integration seamless. We also provide access to that data easily through files, through connectors, and also through our API. We’re very open with the API which allows anyone to programmatically access any of the data types that Petro.ai has.
“That opens up the ability to build your own applications on top of Petro.ai. So maybe you’ve got an internal machine learning model that you like to use, but you also like the way Petro.ai calculates well spacing. First, you could run through a well spacing workflow and get the drainage for every well. Then that output or that result could be an input to your internally developed machine learning models. You could just use queries in python, for example, to get that data.”
Closing gaps in shale workflows means building the bridge between siloed members of a decision team and making the same data accessible to an entire company. “Historically, industry software has been individually tool focused. One tool is useful for a certain task, has its own database, and its own way of computing things. Then you take the output of that tool and hand it off to somebody else or open it up in another application and do something else with it,” Hall concludes. Petro.ai’s mission has been to bridge that gap of understanding, of data obscurity, of lack of context and communication found in disparate tools used by separate teams. Inside Petro.ai lives an access to knowing your shale that makes it the one platform for all.
*The Common Tongue: Westron is the common language in the Lord of the Rings sagas. Derived from several of Tolkien’s base languages, Westron is always indicated in the stories by the use of English.