The Rise of Artificial Intelligence in Downstream Oil and Gas

The hype is real. Artificial intelligence is transforming the downstream oil and gas industry by delivering significant efficiency, safety, and sustainable gains. In the past decade, AI has emerged as a key driver in modernizing the traditionally labor-intensive and fragmented sector—resulting in improved operational breakthroughs and business performance.

 

Growth of AI adoption in downstream oil and gas 

In 2022, the global AI market within the oil and gas industry was valued at approximately $2 billion. According to Precedence Research, that figure will surpass $14 billion by 2030 with North America dominating the market share.

For an industry traditionally labeled as slow to adopt digitalization due supply chain complexity, AI has had “hockey stick” growth in recent years. In a 2018 EY survey, AI and machine learning ranked fifth in terms of expectations to have the greatest impact [over the next five years]. By comparison, today 92% of oil and gas companies have or are planning to invest in AI by 2027.

Several major players have already realized gains using AI and advanced analytics. Exxon and BP report using AI to improve the drilling process to find oil and gas more quickly and at a lower cost. Shell Energy cites implementing AI across multiple functions to reduce emissions and costs while optimizing processes, production and margins.

 

Market forces driving AI adoption 

AI offers unprecedented opportunities for downstream energy operations including demand forecasting, pricing strategies, inventory management, predictive maintenance, and supply chain optimization. Several market forces are driving companies to accelerate adoption including the four highlighted below.

According to Precedence Research, global AI market within the oil and gas industry will surpass $14B by 2030 with North America dominating the market share.

Democratization of data. As more technology companies enter the market offering scalable, user-friendly platforms designed specifically for the energy industry, small and mid-size companies will quickly realize these advantages. The proliferation of application programming interfaces allows robust data solutions to integrate seamlessly with existing applications, further reducing and simplifying technology barriers.

Margin restrictions. Geopolitical tensions, global economics, and rollercoaster swings in oil prices have compelled downstream energy companies to find better ways to optimize output and improve production efficiencies. AI-driven models can analyze large datasets from refinery operations to improve yield, reduce energy consumption, and deliver higher efficiency in converting crude into finished products.

Increasing regulatory compliance and risk management. The downstream industry is subject to a complex web of regulations and compliance requirements, spanning environmental protection, safety standards, pricing transparency, and data privacy.

Since 2020, several global regulations and policies, such as the European Union Emissions Trading System, have been implemented to address environmental concerns and promote sustainability, driving tech adoption to automate compliance reporting, and provide real-time monitoring and alerting capabilities. Advanced modeling can significantly mitigate compliance risks and ensure adherence to industry standards.

Data volume. Energy companies generate and manage terabytes of data daily. The rise in AI will significantly contribute to global data creation of more than 552 zettabytes forecasted by 2026.

 

The future of AI in energy 

As the oil and gas industry is embracing AI, the quality and transparency of big data will become more critical in helping companies remain competitive in a rapidly changing landscape.

…the future of AI will hinge on critically evaluating the accuracy, integrity, granularity, and reliability of its data sources.

Early adopters discovered several factors that should be considered vital for implementing AI processes, including defining the business problems they want AI to solve, cost and ROI, and scalability. One key that underscores the adoption and success for AI is decision-grade data.

AI systems rely heavily on data. The better the data input, the better the output. Without knowing the source of data, energy companies are betting millions of dollars on insights that may be inaccurate due to faulty inputs. It is critical that the company evaluate the data source, application and governance when determining whether to use the data.

As reliance and trusted data sources evolve, there will be opportunities to build digital collaboration with partners in the energy supply chain while maintaining information security protocols and competitiveness.  AI will play an increasingly vital role in ensuring that downstream operations remain resilient and competitive while achieving new standards of operational efficiencies.

Learn how the power of AI within the DTN Fuel Operations Hub to give downstream oil and gas companies a competitive edge.