In June 2019, before the seismic shift brought by the OpenAI revolution, I authored a JPT guest editorial on the oil and gas industry's digitalization journey. I emphasized the transformative roles of the industrial internet of things (IIoT), cloud computing, and artificial intelligence (AI).
Now, reflecting on that editorial from the vantage point of 2025, it is remarkable to witness how many predictions have materialized and how the industry has evolved beyond expectations. ¹
A Sudden Acceleration: The AI Shockwave in Oil and Gas
For nearly 3 decades, technological advancements in the oil and gas industry followed an evolutionary trajectory. Each breakthrough—2D and 3D seismic imaging, horizontal and directional drilling, semisubmersibles, and permanent sensors such as microelectromechanical systems (MEMS) and nanotechnology—built upon prior successes, driving incremental gains in efficiency and recovery.
The ability to acquire real-time downhole data has ushered in the "sense-compute-act" era, fundamentally transforming reservoir management. However, these advances followed a measured pace, allowing engineers time to integrate new methods with traditional workflows.
Then, in a span of just 20 months, the industry was hit by a technological shockwave. The AI revolution—accelerated by OpenAI’s breakthroughs—upended long-held paradigms. No longer do companies rely solely on downhole measurements from oilfield service providers. Instead, they now train AI models on decades of historical data to predict maximum efficient rate (MER) and expected ultimate recovery (EUR).
The “old world” of deterministic modeling is rapidly giving way to probabilistic AI-driven decision making.
This abrupt shift is not just about automation; it is redefining the fundamental principles of reservoir management. The industry finds itself grappling with uncertainties inherent in AI predictions, challenging engineers to place trust in models that are not explicitly programmed but instead learn patterns from vast data sets. The rate of technological change is outpacing our ability to fully comprehend its implications—a stark contrast to the controlled evolution of past innovations.
From Data Collection to Intelligent Systems: The Rise of Metaknowledge
In 2019, I introduced the concept of metaknowledge—understanding what we know and don’t know—as more critical than primary knowledge—the ability to discern what we know and what we do not. Today, this has become an essential component of digital transformation. AI no longer just processes data—it determines which data is meaningful, distinguishing signal from noise within the vast repositories of information.
Where traditional methods relied on human-driven interpretations, AI models now identify patterns and anomalies at scales beyond human capability. The ability to extract actionable insights from billions of data points has eliminated many inefficiencies in field operations. More importantly, it has reshaped the role of engineers, shifting them from manual interpreters to strategic AI curators who validate and refine machine-generated insights.
From Static Models to Living Digital Twins
Reservoir models were once static representations, periodically updated with new data. Today, the distinction between data at rest (archived information) and data in motion (real-time sensor readings) has blurred into a continuous feedback loop.
Living digital twins—which integrate AI, real-time sensor data, and historical archives—now provide dynamic, real-time updates of reservoir behavior. These systems self-correct based on new measurements, drastically improving predictive accuracy. Production forecasting, once constrained by static assumptions, has become a fluid, adaptive process capable of responding to subsurface changes as they occur.
The AI Revolution in Reservoir Management: From Measurement to Prediction
The shift from measurement-based to prediction-driven reservoir management is among the most profound transformations in the industry’s history.
- Traditional reservoir management. For decades, oilfield service companies provided seismic surveys, well logs, and production tests. Engineers analyzed this data to estimate hydrocarbon volumes and optimize production. While effective, this approach was slow, costly, and prone to human error.
- AI-driven optimization. Today’s AI models absorb vast data sets, analyze complex variable interactions, and generate far more precise production forecasts than traditional models. These algorithms continuously optimize field development strategies, reducing uncertainties and improving recovery factors.
- A new reality. AI-driven systems no longer just interpret data—they generate insights and recommendations autonomously. Engineers now work alongside AI, guiding rather than manually computing production strategies.
This transformation is already reducing operational costs while maximizing extraction efficiency. However, challenges remain—engineers must develop new skills to interpret and validate AI-generated models, ensuring that machine predictions remain aligned with the physical constraints of reservoir physics.
The Future: AI, Robotics, and the Next Leap in Oilfield Automation
While much of the industry’s AI transformation has focused on subsurface modeling, the next frontier will bring automation into physical operations.
- Advanced robotics for subsea maintenance. The convergence of AI, next-generation chips, and quantum computing will enable autonomous robots to conduct subsea inspections and repairs—a critical advancement in deepwater operations where human intervention is costly and hazardous.
- Edge computing evolution. AI models will soon operate directly at the wellsite, reducing decision-making latency and enabling real-time optimization without cloud dependency.
The ultimate vision is an autonomous oilfield, where AI-driven systems monitor, predict, and optimize operations without requiring continuous human oversight. However, achieving this requires an essential balance between automation and human intuition—a principle the industry cannot afford to overlook.
The Competitive Landscape: AI, Energy Transition, and the Role of Oil and Gas
While AI is revolutionizing oil and gas operations, it is also reshaping the broader energy landscape. The global push toward renewables, battery storage, and hydrogen is intensifying competition for capital and resources.
However, the transition away from fossil fuels will not be immediate. Oil and gas will remain indispensable for the foreseeable future, not just as an energy source but as a foundation for industries from petrochemicals to transportation. The key challenge is sustaining operational efficiency while preparing for a gradual shift toward cleaner alternatives.
AI will play a dual role in this transformation.
- Optimizing hydrocarbon extraction. By improving reservoir efficiency and reducing emissions, AI will help oil companies maximize returns from existing assets.
- Facilitating renewable integration. AI models will enable seamless integration of oil and gas operations with emerging energy systems, optimizing the balance between fossil fuel production and renewable adoption.
The industry’s long-term competitiveness will depend on how effectively it embraces digital transformation while positioning itself within the evolving energy mix.
Final Thoughts: The Path Forward—
The AI revolution is reshaping oil and gas faster than any prior technological shift. The industry is no longer in a period of incremental change—it is in an era of fundamental reinvention.
The companies that thrive will be those that
- Embrace AI-driven decision-making without losing sight of the underlying physics governing reservoir behavior.
- Leverage automation to enhance safety, efficiency, and cost-effectiveness.
- Adapt to the energy transition by integrating AI across both fossil fuel and renewable operations.
The path forward is not just about automation—it is about augmentation. AI is not replacing human expertise; it is amplifying it. Those who master this balance will define the future of oil and gas in a rapidly evolving energy landscape.
For Further Reading
Digital Transformation: Quest for Operational Efficiency by Michael Thambynayagam, JPT.
Michael Thambynayagam, SPE, is a retired scientist from Schlumberger with a career spanning over 35 years. He has held senior positions, including managing director of Schlumberger Gould Research, Cambridge, England. He is best known for his pioneering work on the mathematics of diffusion, published in The Diffusion Handbook: Applied Solutions for Engineers (McGraw-Hill, 2011), and was a recipient of the 2011 PROSE Award for Excellence in Physical Sciences, Mathematics, and Engineering.
