The emergence of new artificial intelligence (AI) technologies like ChatGPT has captured global attention and sparked unprecedented interest in the transformative potential of AI. But while consumers are just beginning to explore AI’s capabilities and understand their implications, industrial sectors have been engaging with AI for decades. This even extends to established industries known for their relatively slower adoption of new technology and perceived reluctance to change, most notably including the oil and gas industry.

The traditional energy sector has leveraged AI technology for almost half a century, but recent developments have seen their current role and future potential brought to the forefront. However, these AI booms, past or present, are often accompanied by inflated expectations driven by media narratives and ambitious marketing, earnest or otherwise. This can make it difficult to parse fact from fiction without letting the current confidence surrounding AI’s potential obscure the truth. Thus, today's optimism surrounding AI’s ability to revolutionize the industry warrants scrutiny, and to elucidate its true potential, it’s essential to examine its historical contributions, current applications and future challenges.

Applications past and present across the value chain

The oil and gas industry started to leverage AI as early as the 1970s, when expert systems began to support decision-making in oil exploration and the interpretation of seismic data. These first applications were an early sign that AI could indeed make a positive impact on operations, but progress was limited by two fundamental constraints that still persist today: insufficient computational power and sparse, low-quality data. These challenges, when paired with the complexity inherent to the oil and gas industry, meant that progress was slow and incremental. But in recent years, with the development of more sophisticated algorithms, more extensive data acquisition technology and a suite of other technological and organizational improvements, AI systems have become commonplace and already span the value chain.

Upstream

The upstream sector encompasses exploration, drilling and reservoir management and is one of the most data-driven and capital-intensive segments of the oil and gas industry, making it well-suited for AI integrations. One example is the aforementioned AI-driven interpretation of seismic data. Fast and accurate interpretation is fundamental to the exploration process, and AI can improve if not outright replace conventional methods through machine learning (ML) techniques like artificial neural networks (ANNs) or support vector machines (SVMs). These algorithms record the elastic wave responses of subsurface layers to construct 3D models called seismic cubes. AI dramatically accelerates model production and more easily transforms noisy datasets into high-resolution images that reveal reservoir boundaries and structural features to guide exploration.

Looking down the upstream segment past exploration, drilling likewise stands to gain from AI. AI is used to leverage real-time data from logging-while-drilling (LWD) and mechanics-while-drilling (MWD) sensors that enable dynamic optimization of drilling parameters. This allows operators to mitigate critical risks like wellbore instability and kick events, which have traditionally been major causes of costly downtime. In fact, machine learning-based drilling support systems have been shown to reduce non-productive time by up to 50%, with some studies reporting failure reductions of 90% through proactive adjustments. Viewing even further afield, AI can also benefit reservoir management. AI combines historical production data with real-time field measurements to generate more accurate simulations of reservoir conditions and design better models that improve recovery rates and extend reservoir lifespans. Previously, these simulations could take months to complete but can now be executed up to 1,000 times faster without compromising on accuracy.

Midstream

The midstream sector pertains to transportation, storage and distribution, where AI also provides several important advantages. Perhaps the most significant instantiation is found in pipeline monitoring and anomaly detection. Oil and gas pipelines often span thousands of miles and thus require constant surveillance to prevent damage or disruption, whether via leaks, corrosion, malfunction or attack. AI systems analyze real-time data from embedded sensors to detect anomalies that are indicative of potential failures. For example, multi-agent systems (MAS) combined with ML have been used to classify pipeline threats like metallic intrusions. This enables rapid response and helps minimize environmental damage and financial losses.

Beyond monitoring, AI also aids transportation efforts. ML algorithms optimize routing and scheduling for pipelines and various vehicular transport systems to save money and greatly enhance transportation efficiency. Such systems can also improve load balancing in storage facilities by predicting inventory levels and streamlining transfer schedules to facilitate smooth transitions between nodes. Similarly, logistical coordination can likewise integrate AI platforms for its own enhancements. These platforms consolidate data from enterprise systems, field operations and external markets and use predictive analytics to anticipate disruptions and suggest alternative routes. For example, AI systems can forecast seasonal demand shifts and adjust delivery schedules accordingly in order to maximize profitability.

Downstream

The final segment in the oil and gas value chain, downstream operations, entails refining, petrochemical production and product distribution and it is characterized by complex processes and fluctuating market demands that make it similarly well suited for AI applications as the other segments of the oil and gas value chain. In refining operations, AI-driven process optimization tools are changing how facilities manage their energy consumption, throughput and product yields. ML algorithms can analyze real-time operational data to predict equipment performance and recommend adjustments to key parameters. As an example, ML models have been applied to cracking processes that break down hydrocarbons into lighter, more valuable products to achieve higher yields and reduced energy consumption.

Hand in hand with refining operations is predictive maintenance, which is designed to anticipate equipment failures that can otherwise lead to costly downtime and acute safety hazards. Data from sensors that monitor key metrics can identify patterns that signal impending failures, and AI systems allow operators to take a proactive approach to maintenance efforts that are rewarded with more uptime and a longer lifespan of critical infrastructure. Moving past technical considerations, AI applications also offer to improve downstream distribution and inventory management, affording similar functionality to what’s employed in the midstream segment. AI can integrate market data, demand forecasts and logistical constraints to enhance supply chain efficiencies, supported by predictive analytics that aid refineries and distributors in optimizing their production schedules and inventory levels. Likewise, AI-powered systems can also adapt to shifting market conditions, such as sudden changes in demand or price volatility, by dynamically reallocating resources and adjusting distribution strategies.

The imminent future of AI in oil and gas

The future of AI in the oil and gas industry holds significant promise but remains uncertain. Upstream operations could enjoy more precise reservoir modeling by integrating real-time data with hybrid AI-physics models that improve recovery rates while also reducing environmental impact. The midstream segment may include intelligent, adaptive pipeline networks that enhance monitoring and logistics, minimizing hydrocarbon losses and operational risks. In downstream operations, AI could optimize refining efficiency, supply chains and energy usage, driving sustainability and profitability simultaneously. However, realizing these advancements will require successfully addressing major technical hurdles like the currently limited access to high-quality data, high implementation costs and integration with aging infrastructure. Organizational resistance paired with a global shortage of AI talent further complicates progress. So while we can expect AI is all but guaranteed to continue to deliver incremental improvements, its ability to fundamentally transform the oil and gas industry remains speculative and dependent on overcoming significant technical and structural barriers in the coming decades.

Moreover, the question of transformation itself is subjective. Is AI’s current role — streamlining processes, optimizing operations and enabling smarter decision-making — sufficient to call it transformative? Or does the term demand a complete overhaul of how the industry functions? Its historical trajectory suggests gradual innovation, but new developments like hybrid AI-physics models and autonomous systems could signal larger shifts. To best gauge AI’s future impact, stakeholders should monitor advancements in autonomous operations, the integration of real-time analytics and sustainability-focused applications such as carbon capture techniques. Industry leaders must balance optimism with realism if they are to avoid overhyped expectations while still fostering the conditions needed to maximize AI’s potential. Though its ultimate transformative power remains uncertain, AI’s trajectory promises a future of continued progress that makes it a technology worth the investment of both time and money.

About the author

Tyler Gleckler is an accomplished scientist, writer and renewable energy expert with a strong background in chemistry. He holds undergraduate and graduate degrees in chemistry, having studied and worked at prestigious institutions such as the University of Oxford, the University of Edinburgh, the National University of Singapore and the Hebrew University of Jerusalem. With a research focus on material chemistry and applied nanotechnology, Tyler has worked on a diverse set of projects, spanning many subjects and applications.