Oil’s AI Awakening: A New Era of Efficiency or a Recipe for Disaster?

The oil and gas industry has long been criticized for its slow adoption of new technologies. Applied Computing’s $20 million Series A raise suggests that even this reluctant giant may be on the cusp of an AI revolution. The London-based startup claims to have developed a foundation model called Orbital, which can predict the state of an entire facility in minutes using sensor readings, physics and chemistry, and equipment constraints.

Facilities currently make operating decisions based on less than 8% of available data, making Orbital’s promise of speed particularly significant. However, this also raises questions about the industry’s readiness for such a seismic shift. The oil and gas sector has a history of being slow to adapt, and its entrenched industrial software suppliers will not give up their market share without a fight.

AspenTech, AVEVA, Cognite, and Seeq are among the players that Applied Computing must navigate if it hopes to succeed. The company argues that its moat lies in assembling AI researchers to build a model that can compete with Orbital, implying that access to industrial data or process knowledge is not the key to success. This could be a game-changer for the industry, as it suggests that even small startups like Applied Computing can disrupt the status quo.

However, the partnership with KBR raises concerns about the level of influence larger players will have over the smaller startup’s decision-making process. Will Applied Computing be able to maintain its independence in the face of significant investment?

The company plans to expand internationally, hire for research and engineering roles, and explore deployments with energy clients, indicating a commitment to making Orbital a global phenomenon. As it sets its sights on new markets, including the Middle East, it must also be mindful of the regulatory landscape in each region.

The AI revolution in oil and gas may be upon us, but it’s too early to tell whether this will be a blessing or a curse. As Applied Computing takes its first steps into the global market, one thing is certain: the industry will never be seen in the same light again.

The Double-Edged Sword of AI Adoption

Applied Computing’s Orbital model uses sensor readings, physics and chemistry, and equipment constraints to predict facility state in minutes, which could potentially reduce energy waste and maintenance delays. However, this raises questions about the level of human intervention required to make AI-powered decisions. Will operators be able to trust the model’s predictions, or will they still need to rely on their own judgment? And what happens when the model makes a mistake?

The Rise of the New Oil Majors

Applied Computing’s partnership with KBR and its plans for international expansion suggest that it may be poised to become one of the new oil majors. However, this also raises concerns about the concentration of power in the industry. Will smaller players be squeezed out by the likes of Shell and ExxonMobil, or will Applied Computing be able to carve out a niche for itself?

The company’s argument that its AI model is not just a tool for energy companies but rather a platform for anyone who wants to predict facility state may be a clever move. However, it also requires careful consideration of the regulatory environment in each region.

The Human Factor

Orbital’s ability to combine sensor readings, physics and chemistry, and equipment constraints to predict facility state is undeniably impressive. However, it also raises questions about the human factor in AI adoption. Will operators be able to trust the model’s predictions, or will they still need to rely on their own judgment? And what happens when the model makes a mistake?

As Applied Computing takes its first steps into the global market, it must also be mindful of the cultural and social implications of its technology. How will Orbital be integrated into existing workflows, and what kind of training will operators receive to ensure they can trust the model’s predictions? These are questions that go beyond technical considerations and require careful consideration of human psychology.

The Future of Energy

Applied Computing’s AI model may be a game-changer for the oil and gas industry. However, it also raises questions about the future of energy. As we become increasingly reliant on digital technologies to manage our resources, what does this mean for the role of humans in the energy sector? Will we see a rise in automation, or will operators continue to play a key role in decision-making?

The answer lies not just in the technology itself but also in how it is implemented and integrated into existing workflows. As Applied Computing takes its first steps into the global market, it must be mindful of these broader implications and work towards creating a future that is both efficient and equitable.

As the oil and gas industry hurtles towards an AI-powered future, nothing will ever be seen in the same light again.