As digital tools and technology reach maturity, Oil and Gas organizations are prepared to maximize efficiency and optimize operations.
A new wave of advanced technologies simultaneously reached maturity together around six years ago, resulting in the idea that we may have entered the ‘Fourth Industrial Revolution.’ This wave of advancement in operating models was the result of rapidly increasing integration between digital and physical systems allowing ‘smart factories’ or ‘digital twins’ to transform manufacturing and traditional industries like Oil and Gas. Artificial Intelligence (AI) has been seen as a cornerstone of this effort and has proven an integral enhancement across all industries in recent years. Still as true today as it was when it was predicted by BP in 2016: “AI is enabling the fourth industrial revolution” for Oil and Gas.
The advancement in technological power comes at a time of great challenge for the Oil and Gas industry. In fact, a recent piece in Forbes discussed these challenges and the concept that advanced digitalization will be key for addressing them. Major challenges in the space include significant pressure to adopt lower-carbon approaches to energy while maintaining large-scale, complex and aging systems for Oil and Gas production. Critical initiatives also involve increasing efficiencies in upstream production, minimizing cost through optimizing maintenance needs, preventing accidents and system failures, and forecasting and reducing carbon output. All of these concepts involve using digital data and intelligent automation to do more with less.
Unlike the digital-first industries of social media and e-commerce, Oil and Gas organizations are late adopters of more digital technologies. Emerging technologies driving this shift are beginning to reach maturity, such as the Internet of Things (IoT), Smart Sensors, and Robotics. Together these technologies provide real-world devices and physical systems the ability to connect, interact, perform analysis, and implement automation. With advances in real-time asset connectivity with IoT, along with the massive power of cloud computing, the advanced digitalization of digital twins, and the highly scalable intelligence of AI, the industry is poised for modernization. AI that works in conjunction with digital twin technology will be key to the success of this next phase of transformation in the Oil and Gas Industry.
Modern data and machine learning models are now capturing and storing this knowledge... this type of AI-driven approach will be vital to Oil and Gas organizations’ longevity in the new digital age.
Modern AI involves a variety of advanced methods for software systems to imitate natural or human intelligence, often using large volumes of data and significant computational power. AI systems may learn deep patterns from data, handle complex information such as photographic images or written documents, and forecast or predict future events based on historical observations. Machine learning, deep learning, neural networks, computer vision, and natural language processing (NLP) have made major strides toward maturity in recent years. AI sophistication runs from simple advice such as Google Maps suggesting the best route to take to work, to highly advanced automation and decision-making with self-driving cars.
Part of the transformative power and recent growth of AI is a result of massive growth in data and computation power. Early pioneers in AI, such as Google and Facebook, have succeeded partly because of their wealth of data. Every single time someone searches on Google and follows through with clicking a website, Google’s AI systems learn to predict human behavior, which they monetize through advertisements.
Similarly, the Economist wrote nearly five years ago that “the world’s most valuable resource is no longer oil, but data.” Coincidently, this was around the same time that leaders in the Oil and Gas industry rapidly began investing in digital transformation. Since this shift in focus, Oil and Gas companies have committed to digitizing operational processes, moving towards data and analytics, and the production of the kind of high-quality data required to develop effective AI systems. Today the transformation continues, as organizations in the industry seek to further drive business and operational functionality and implement improvements to their infrastructure.
One key application for AI in Oil and Gas is its ability to capture intelligence in your organization’s value chain, which can then be redistributed and scaled. In typical operational processes, employees gain experience through trial and error slowly throughout their careers. Employees have limitations to their scale, and when an employee leaves, an organization loses that knowledge. “Brain Drain” is a significant concern in the Oil and Gas space, with more than half of the workforce eligible for retirement in the next decade. Modern data and machine learning models are now capturing and storing this knowledge, which can be applied to a multitude of use cases and scaled nearly limitlessly over multiple activities. This type of AI-driven approach will be vital to Oil and Gas organizations’ longevity in the new digital age.
Introduction of Digital Twins
Digital twins are a real-time simulation of a physical or real-world system with data captured using IoT sensors, computer vision, and other smart devices. Basic forms of digital twins have existed for decades, especially popular in the manufacturing or automotive industries, but only recent advancements have enabled dynamic, real-time digital twins for the interactions of large-scale systems such as refineries and all of their related assets.
As digital-first industry operations have inherently modernized, much of the digital infrastructure and technologies have matured and are now ready for Oil and Gas to adopt. Digital twins are enabling new avenues for accelerating business value, driving concepts like hyper automation and smart factories for the Oil and Gas industry. How do you optimize the vastly complex, real-time dynamic interactions of all of the devices, systems, and assets across the entire Oil and Gas value chain? How do you increase efficiency and maximize uptime in production operations? How do you increase production by analyzing geological and geophysical historical data to forecast new wells? Advancements in AI and digital twin technology are expected to combine to address some of these core strategic challenges in the space.
As AI and digital twins develop, they will empower each other through the next wave of automation and intelligence. The infrastructure established from digital twins is enabling an advanced machine learning technique called ‘reinforcement learning,’ which allows AI systems to learn within a vastly complex and dynamic real-world environment. Digital twins provide real-time data, pulling together vast amounts of information, from the smallest components to the largest assets, into a coherent platform. Through reinforcement learning, AI learns from the digital twin and is ultimately able to make complex decisions on the operations’ large-scale systems. This emerging technology presents boundless opportunities for the Oil and Gas industry to scale, modernize, and optimize systems and operations.
With a foundation in digital transformation along with advancements across AI including digital twins and reinforcement learning, Oil and Gas companies are positioned to super-charge their production, maximize operational efficiency, and minimize environmental impact. Navigating the challenges associated with implementing emerging technology will require innovative, AI-focused leadership and will be vital for the growth and resiliency of the industry.