The oil and gas industry, while working at the cutting edge of engineering physical systems, remains a laggard in adopting digital disruption.
If this imbalance between physical and digital technologies persists, capital destruction will continue to grow, the time to identify sweet spots will lengthen, and the trauma of a future workforce shortage will intensify. The industry has discussed digital oil fields, integrated reservoir management, and real-time, data-driven decision-making for over 3 decades, yet many of these concepts remain in their infancy.
Today, the upstream oil and gas industry is at a crossroads, facing another strategic inflection point (SIP). This SIP proposes adopting generative artificial intelligence (AI) and agentic AI now to advance the industry and build resilience. Otherwise, the industry will continue to incur the costs of delayed action, as has been the case with big data and traditional AI.
For example, the Open Subsurface Data Universe (OSDU) Forum, now known as The Open Group OSDU Forum, was established in 2018 to break down silos in the oil and gas industry through an open-source, standardized data platform. It has been almost 7 years since its inception, and the forum is working to bring traditional AI into the fold, underscoring the industry's slow adoption of emerging technologies.
The industry needs to leapfrog from digital oil fields to agentic oil fields to remain a key player in the energy transition landscape.
What is the agentic oil field, and how can one plan for, deploy, and benefit from it?
I define an agentic oil field as one that uses advanced AI systems—especially generative AI and AI agents—across its operations to work more independently and intelligently. These systems can understand information, make decisions, and take actions with less human intervention while operating within clear, flexible rules and regulations. This approach goes beyond small improvements. It changes how the oil field runs, making it faster, smarter, safer, and more productive.
The agentic oil field goes well beyond a digital oil field. In simple terms, the digital oil field focuses on connecting equipment, collecting data, and giving greater visibility to professionals, with humans remaining at the center of command. In an agentic oil field, AI agents and generative AI systems understand data, reason through scenarios and options, and make decisions with minimal human intervention. This allows seamless integration of the industry’s typical silos—exploration, drilling, production, operations, and others.
Let me illustrate the importance of the agentic oil field within the context of three grand challenges the oil and gas industry faces at scale.
The first grand challenge is declining productivity and margins in mature assets and complex reservoirs. The digital oilfield solution improves visibility and helps optimize operations manually, yielding incremental gains that fall short of reducing high costs.
However, with the agentic oilfield practice, autonomous real-time adjustments enhance production while reducing costs caused by delays in manual intervention, resulting in sustained performance improvement across the asset life cycle. This reflects my research, which incorporates input from academics, companies, and national authorities, and indicates that production uplift may rise to 8% with the agentic oil field, compared with less than 4% if the industry focuses only on the digital oil field.
The second grand challenge the industry faces is maintaining its reputation as a sustainable, clean, and safe energy provider. Rising carbon footprints, increased safety incidents due to delayed detection, and siloed information are some of the concerns. With digital oil fields, incidents can be reduced by up to 10%. In an agentic oil field, this reduction could be more than 30%, and with the maturity of the agentic AI ecosystem, incidents could be reduced by up to 50%.
The third grand challenge is hard to believe, as it exists at the same scale as a decade ago. The challenge is taming “massive data” to generate value while contending with disputes over data ownership—across the industry, within companies, and among business lines. Nothing about this issue has changed, even though first- and second-generation AI solutions have proven the value of integrating data across all industries.
With digital oil fields, one can achieve some centralized data usage and reporting with minimal benefits. With agentic oil fields, the cultural silos are dismantled quickly, and organizations can transform from data-rich and data-locked to insight-driven, value-focused organizations.
In Table 1, some key performance indicators are compared for digital oil fields and agentic oil fields.
The question now is how to mature the agentic oil field in a way to reduce capital waste, increase production at a lower cost, and improve safety. I propose a RACE framework for the agentic oil field.
- R—Revisiting the vision of digital disruption
- A—Accelerating the adoption of generative AI and agentic AI
- C—Creating a truly integrated platform
- E—Experiencing the exponential impact of the third wave of AI (i.e., agentic AI at scale)
To expand further, the RACE framework applies the following concepts:
- Revisiting the vision of digital disruption is about focusing on autonomy and AI-driven execution of workflows, with the ambition of creating self-optimizing assets vs. only seeking better insights from data.
- Accelerating the adoption of generative AI and agentic AI is about moving away from the industry's blind spot: the thought process of proof of concept and change management. This requires embedding AI agents directly into every operational workflow, developed with autonomous end results in mind, within an adaptable governance framework.
- Create a truly integrated platform with unified, distributed, cloud-enabled, secure subsystems that communicate seamlessly in real time via AI agents to simulate scenarios, coordinate actions, and make decisions within compliance frameworks. The integration will enable organizationwide autonomy with minimal effort and improved efficiency.
- Experience the exponential impact of the third wave of AI (agentic AI at scale). The third wave of AI—generative AI and AI agents—enables reasoning, planning, and coordinated action at scale. When deployed across the value chain, improvements compound across production, cost, safety, and carbon metrics. The impact is exponential because each autonomous improvement feeds into the next, accelerating value creation over time.
With the RACE framework in place, the agentic oil field is within reach in 2 years or less, unlike the simple OSDU implementation, which is taking more than 7 years.
Fig. 1 shows a simple RACE roadmap to an agentic, autonomous oil field.
In conclusion, the time to leapfrog from digital oil fields to agentic oil fields is now. The industry should not cling to the title of laggard in adopting and adapting to exponential technologies, nor continue to remain unsafe, inefficient, and unattractive to the next generation of the workforce.
Satyam Priyadarshy, SPE, is the founder of Reignite Future which helps industry leaders leverage exponential technologies the right way to transform their organizations and operations with a value-focused vision. He is recognized as the first chief data scientist in the oil and gas and energy industries. Previously, he was the technology fellow and chief data scientist at Halliburton, and prior to that, he held many leadership roles in companies that leverage data, AI, and digital for growth. He is also a trustee of the AI Advance Consortium in the US and a board advisor to Global Quantum Intelligence. He was a 2021–2022 SPE Distinguished Lecturer. Priyadarshy holds a PhD in quantum field theory from IIT Bombay and an MBA with honors in the technology and finance domain from Virginia Tech’s Pamplin School of Business.