Copyright OilPrice
In the ongoing digital transformation of the energy industry, artificial intelligence (AI) has emerged as one of the most discussed — and misunderstood — tools available to operators. While the promises of AI often stretch toward the speculative, the technology’s most immediate and profound impact is already visible in how companies organize, interpret, and act upon data. For national oil companies (NOCs), this impact is potentially transformative. Unlike many private firms, NOCs sit atop immense, often underutilized data reservoirs — decades, sometimes a century, of geological, operational, and financial information that forms a foundation few other entities in the energy sector can match Take Mexico’s National Rock Library, administered by the Ministry of Energy (SENER). It holds physical cores from wells drilled more than a hundred years ago — an extraordinary record of geological history. When digitized and paired with AI-driven analytics, such datasets can unlock patterns invisible to the human eye. Subsurface models, for example, can be refined by feeding these historical datasets into machine learning algorithms to improve facies prediction and reservoir quality estimation, and even identify underexplored analogs for new drilling campaigns. The same principle applies to seismic reprocessing or production optimization: the richer and more structured the dataset, the more powerful and reliable the AI interpretation. This is the core advantage for NOCs — not just the data itself, but the continuity of data stewardship. Many of the Gulf Cooperation Council (GCC) NOCs, for instance, have built decades-long archives of standardized, high-quality information. Their institutional stability and continuity of purpose make it possible to apply AI to longtime series without the disruptive policy shifts that sometimes affect state-owned entities elsewhere. When models are trained on consistent, comparable data across decades, they can produce far more reliable insights on production decline trends, reservoir management, and capital efficiency. Contrast this with some Latin American or African NOCs, where political cycles and policy shifts often reset corporate priorities. In such cases, the challenge is not the absence of data, but its fragmentation, both across time and across institutions. Here, AI’s promise lies as much in data integration and cleansing as in predictive analysis. Natural language processing tools can help standardize legacy documentation; supervised learning models can detect inconsistencies or gaps in well logs; and AI-powered metadata tagging can turn unstructured archives into queryable, connected knowledge systems. Real-world examples of this are emerging. Saudi Aramco has developed proprietary AI systems for predictive maintenance, analyzing millions of sensor readings to anticipate equipment failure before it occurs. ADNOC’s Panorama Digital Command Center aggregates real-time data from across the company’s operations, allowing executives to visualize energy flows, costs, and emissions at a glance. These systems rely not on speculative “intelligence” but on disciplined data collection, governance, and model training — areas where NOCs, by virtue of their scope and national mandate, have a natural edge. Schreiner Parker, Head of Emerging Markets & NOCs The next phase for many NOCs will involve expanding these applications beyond the technical realm into strategic and commercial domains. AI can already help model fiscal sensitivity under different price and tax regimes, or identify optimal timing for licensing rounds by analyzing global exploration trends. As carbon intensity becomes a defining metric for competitiveness, AI can also play a key role in monitoring emissions, optimizing energy efficiency, and guiding investment toward low-carbon opportunities — all grounded in data the NOC already possesses. Yet optimism must be balanced with realism. AI is not a silver bullet; it is an amplifier of organizational discipline. The sophistication of the algorithm matters less than the quality, structure, and governance of the underlying data. For NOCs to truly harness AI, they must think less about “buying AI solutions” and more about building data cultures, where engineers, geologists, and economists alike treat data not as a byproduct of operations but as a strategic asset. If that happens, NOCs — especially those with deep archives and long institutional memory — will be uniquely positioned to lead the next wave of digital transformation in the energy sector. In the end, it is not the algorithm that determines success, but the intelligence embedded in how nations preserve, interpret, and learn from their own energy histories. By W. Schreiner Parker, Head of Emerging Markets & NOCs at Rystad Energy. More Top Reads From Oilprice.com Nigeria Looks to Revive State-Owned Refineries Global Power Demand to Skyrocket 30% by 2035 Lower Oil Prices Drag Down Profits at Chinese Giant CNOOC
