Artificial Intelligence in Upstream Redefines the Value of Oil and Gas

Artificial intelligence in upstream is moving beyond its role as a digital support tool to become a factor capable of transforming the productivity and economics of oil and gas operations. A McKinsey analysis evaluating more than 550 use cases estimates that the application of these technologies could generate up to USD 230 billion in annual value under a full-potential scenario, primarily through production optimization, drilling, reservoir management, and decision automation.

However, achieving that potential will not depend solely on incorporating new algorithms. Data quality, integration with existing systems, the ability to modify operational processes, and even the commercial models between operators and service companies will determine how much of that value can actually be converted into results.

Artificial intelligence in upstream concentrates its value in critical applications

The economic scale of the opportunity changes considerably depending on the level of adoption. McKinsey estimates approximately USD 65 billion in annual recurring value in the near term using technologies available today. Broader implementation of already proven solutions would increase that figure to USD 125 billion, while the potential could reach USD 230 billion annually under scenarios incorporating autonomous operations and a much broader application of AI across the upstream value chain. These estimates account for more than USD 30 billion annually in implementation costs.

However, the impact is not evenly distributed across hundreds of potential applications. The 10 highest-impact use cases account for nearly half of the identified value, the top 20 represent approximately two-thirds, and around 60 applications account for nearly 95%.

This highlights an important difference between digitizing multiple processes and selecting those capable of directly changing asset performance. Production, drilling, and reservoir management are precisely among the areas where AI could generate the highest returns.

Production and drilling concentrate immediate opportunities

Production assets continuously generate large volumes of operational information. Pressures, temperatures, flow rates, vibrations, equipment conditions, and well behavior provide data that can feed models capable of identifying deviations and optimizing operating parameters. This evolution is part of a broader transformation driven by digital technologies in the Oil & Gas sector, where artificial intelligence, machine learning, IoT, and advanced analytics are expanding capabilities in exploration, production, and asset management.

In production, the applications analyzed include rod pumps, electric submersible pumps (ESPs), gas lift, water injection, production networks, surface facilities, flow assurance, and chemical programs. The potential value can materialize through incremental production, reduced well downtime, fewer failures, lower intervention costs, and lower energy intensity.

Drilling represents another high-impact area for artificial intelligence in upstream. Algorithms can support well planning and equipment selection, detect anomalies at an early stage, and optimize activities such as trips and connections.

The evolution toward closed-loop drilling systems further expands this capability by allowing information acquired during operations to be used to adjust parameters and improve well placement within the reservoir. More efficient execution can also reduce nonproductive time (NPT) and improve productive-time utilization.

Reservoir models are also entering a new stage

The impact of artificial intelligence in upstream does not end with physical operations. Subsurface interpretation represents another area with the potential to significantly accelerate upstream processes.

Machine learning, hybrid models combining physics and machine learning, deep learning, generative AI, and agentic systems can support updates to static and dynamic models, accelerate simulations, evaluate recovery strategies, and improve reserve estimates.

The potential change lies in the speed of analysis. Exploration and development processes that have traditionally taken months or years could be reduced to weeks or even days in certain workflows, although this would require transforming planning processes that currently remain fragmented across different disciplines and organizations.

The study also estimates that AI-driven improvements in exploration success could generate more than USD 35 billion annually in additional balance-sheet value through reserve additions.

The industry is already beginning to bring AI into the field

Some companies are already moving from digital trials toward operational applications. SLB and Vår Energi, for example, are working together on collaborative well-planning workflows on the Norwegian Continental Shelf, with the objective of significantly reducing cycle times between discovery and first oil. Baker Hughes and Expand Energy also announced a multiyear collaboration to deploy Leucipa, an AI-powered automated production solution, across thousands of natural gas wells and to pilot an artificial intelligence-based production assistant.

These developments show that artificial intelligence in upstream is beginning to integrate directly with decisions involving wells, production, and physical assets, rather than remaining limited to administrative applications or isolated data analysis. This evolution is also reflected in smart and automated offshore oil platforms, where advanced sensors, real-time analytics, digital twins, and AI are enabling progress toward operations with greater autonomy. However, moving solutions from pilot projects to large-scale operations remains one of the main obstacles.

Data and legacy systems could limit the USD 230 billion potential

Incomplete data, legacy technology infrastructure, limited connectivity, and models that are not sufficiently robust for complex field environments continue to hinder implementation.

The challenge, however, is also organizational. The analysis indicates that AI generates greater value when it changes the way decisions are made. This capability is also evolving in predictive maintenance with artificial intelligence, where prescriptive analytics makes it possible to move from anomaly detection toward intervention recommendations based on operational information and asset condition. A company may have advanced algorithms and still achieve limited results if its processes remain fragmented or if there is no strategy for integrating digital recommendations into operational decisions.

One indicator is particularly significant: fewer than 20% of the implementations analyzed track the value generated by AI and share that KPI within the organization. The absence of measurement makes it difficult to determine which solutions produce tangible benefits and which remain technology initiatives without clearly quantified returns.

AI could also transform the oilfield services business

The advance of artificial intelligence in upstream introduces a less obvious economic consequence. What represents greater efficiency and savings for an operator can reduce certain billable activities for an oilfield services company.

Wells drilled faster, fewer failures, lower downtime, and fewer interventions generate benefits for producers but can reduce revenues associated with traditional service activity. The analysis estimates that approximately USD 17 billion in OFSE company revenues could be exposed at current levels of AI implementation, a figure that could reach USD 60 billion under a full-potential scenario.

This does not necessarily mean a structural loss for service companies. It also creates an opportunity to modify their commercial models.

Performance-based contracts, software as a service (SaaS), gain-sharing arrangements, and future models in which providers share part of the risk associated with autonomous decisions could become increasingly relevant. Helmerich & Payne, for example, uses performance contracts under which it can receive per-well bonuses for exceeding certain drilling speed benchmarks.

This evolution could move some OFSE companies from a model based primarily on selling activity toward one in which they act as performance partners, combining automation, equipment knowledge, data infrastructure, and operational expertise to participate directly in productivity improvements.

The real value will lie in turning intelligence into decisions

The USD 230 billion figure represents a potential scenario, not a guaranteed benefit for the industry. Achieving it would require high levels of adoption, greater operational autonomy, and the ability to extend the most effective applications across a considerable portion of the upstream value chain.

Therefore, the next stage of artificial intelligence in upstream will likely be determined less by the number of tools available and more by the ability to integrate them with assets, people, and processes.

Production, drilling, and reservoir management indicate where some of the most significant returns could emerge. But the transformation will be deeper if the technology also changes the way performance is measured, risks are distributed, and services are contracted.

The question for the industry is therefore no longer simply how much AI can do. The real challenge will be determining how much of that technological potential can be converted into additional production, lower costs, better decisions, and measurable economic value.

Source: Oil & Gas Journal