Not Competitive: In traditional O&G companies, complex problems are divided up and solved by different teams. The decision being handed along some chain of experts that create a final solution. But for shale, the solution is an interrelated complexity that needs to be passed back and forth, debated and understood, engineers and finance, field and management, back and forth. That’s what AI is for. Complex, connected decisions that need a best fit and iterative tradeoffs.
Competitive: Re-architect your business to an AI factory. You need one software hub, not layers of software tools that slow down your internal process. One unifying platform with all the data inputs, technology and algorithms in one collaborative place. Petro.ai.
Manage complexity. Build agility. Increase scope. Decrease deliberation time. Figure out the real, right solution. Consolidate your data to develop a single version of the truth. Go AI and start competing at a new level. Petro.ai is an AI-centric software hub that provides direct access to a clear, decision-making model that pulls together the many traditional processes of the O&G industry. Years of fine-tuning, aggressively pursuing the latest research and technological developments in AI and O&G have created this ultimate AI factory.
“It’s not like you need to fit Petro.ai into a workflow, it’s all your workflows in one place,” Dr. Derek Ruths, Chief Data Scientist of Petro.ai explains.“We have developed an AI hub that brings your data into an intricately constructed data pipeline and employs all the relevant algorithms from your many legacy tools. Need another complex computational application? Our Petro.ai engineering team can build artificial intelligence and machine learning models quickly and responsively to any client’s timeline. We have put in years of work to make this easy and accurate for your team.
“You need to be able to collaborate, communicate, create your decision-making matrix all in the same place. That’s what a competitive AI factory is in today’s highly demanding marketplace of oil and gas. The package that we offer is these Petro.ai algorithms that produce a 360-degree view of a new well’s lifecycle. There’s a lot involved in that, a lot working behind the scenes.
“Petro.ai needs to sit on your desktop as your own AI-powered consultant, that you can reference daily as you work through each part of the well lifecycle. This is something your people can use, can be cruising through and gain a richer understanding at each step.”
“In my experience,” Dr. Ruths continues, “There are usually just two reasons for not adopting technologies that have proven their utility, such as AI. The first is a natural resistance to disrupting a process when it has a history of working. Unfortunately, that mindset has moved many firms to extinction that have pushed back against bringing AI insights into their operating models.
“The second is the type of workflow you use. If you have a soft workflow, one that isn’t well-defined, it’s hard to know how to innovate because you haven’t mapped out what people are doing. There’s a lot going on in the complex world of unconventionals. You need to know the steps you’re taking as a team.
“AI works best when applied across disciplines, integrating data and all the participants in the many aspects of the well lifecycle decisions. With Petro.ai, the different applications inside the platform are interconnected for enhanced collaboration. And adding an application to the platform for a specific group or need is easy to do, always maintaining full integration among the community of users. And AI is the only way to manage complexity.”
Without AI, complexity is a business’ nemesis. “Ultimately, complexity becomes the downfall of traditional organizations…Digital systems scale more easily and continue to improve despite the size and complexity of its operations,” according to Marco Iansiti and Karim Lakhani in Competing in the Age of AI:Strategy and Leadership When Algorithms and Networks Run the World, “These processes are digitized and enabled by an AI factory that treats decision making as an industrial process. The AI factory creates a virtuous cycle between user engagement, data collection, algorithm design, prediction and improvement. These algorithms not only make predictions but also use the data to improve their own accuracy.”