ENEOS AI Strategy 2025: How AI-Driven Refinery Optimization is Securing its Future

ENEOS Commercial AI Projects: From Pilot to Autonomous Refinery Operations

ENEOS has advanced its AI strategy from initial pilots for process optimization to deploying fully autonomous control systems in its core refinery operations, using AI as a primary tool for both efficiency and future-proofing its business model. This strategic progression demonstrates a clear shift from experimental R&D to enterprise-wide adoption of AI as a foundational technology. For energy executives and investors, this transition provides a clear signal of where the company is allocating capital and building its competitive advantage.

• Between 2021 and 2024, ENEOS focused on validating AI in live industrial settings through its partnership with Preferred Networks (PFN). A key milestone was the world-first 40-day continuous autonomous operation of a large-scale plant unit, proving that AI could outperform skilled operators in stability and efficiency. This success led to the establishment of the Ideaplatz joint venture in October 2023 with an initial capital of 100 million JPY, a decisive move to commercialize the technology.
• Starting in 2024 and accelerating into 2025, ENEOS transitioned from single-unit trials to full-scale deployment. In January 2024, it began continuous autonomous operation of an entire crude oil distillation unit using a reinforcement learning AI. Building on this, the company announced its plan to implement AI-equipped failure prevention systems across all nine of its domestic refineries by fiscal year 2026, cementing AI as a core component of its nationwide operational strategy.
• The application of AI has expanded from process control to predictive maintenance and strategic planning, indicating a mature adoption curve. While the initial focus was on improving efficiency in existing hydrocarbon processes, the success of similar Japanese technology, such as Yokogawa‘s deployment with Aramco, validates the potential for ENEOS to export its AI-driven operational expertise or apply it to future low-carbon energy systems like hydrogen and ammonia production.

ENEOS AI Investment Analysis: Funding the Digital Transformation of Energy

ENEOS‘s investment in AI is channeled through strategic joint ventures, a model that leverages external expertise while retaining significant control, indicating a preference for collaborative development over purely in-house R&D. This approach allows the company to access cutting-edge technology from AI specialists like Preferred Networks (PFN) and accelerate its application in the capital-intensive energy sector.

Table: ENEOS Joint Venture Investments in AI

ENEOS Strategic AI Partnerships: Building a Collaborative Tech Ecosystem

ENEOS‘s AI strategy is built on a deep, long-term partnership with AI specialist Preferred Networks (PFN), a collaboration that has progressed from research to pilot testing and now to commercial deployment. This focused relationship has been instrumental in integrating advanced machine learning into the company’s core industrial processes, serving as a model for how heavy industry can partner with tech firms to drive transformation.

Table: ENEOS & Preferred Networks (PFN) AI Collaboration Milestones

Geographic Focus: ENEOS’s Domestic AI Deployment and Global Tech Validation

ENEOS has concentrated its AI deployment strategy within its domestic Japanese refinery network, while its technology partners and industry peers validate the approach on a global scale. This “local first” rollout allows the company to refine the technology across its own assets before considering international expansion, using its domestic operations as a large-scale testbed.

• Between 2021 and 2024, ENEOS‘s AI activities were confined to pilot projects within specific plants in Japan. These trials, including the successful 40-day autonomous operation, served as crucial proof-of-concept demonstrations in a controlled but live environment, primarily focused on de-risking the technology.
• From 2024 into 2025, the strategy scaled from single-plant trials to a nationwide domestic initiative. The company’s decision to deploy AI-based failure prevention systems across all nine of its Japanese refineries by 2026 represents a major capital commitment and a strategic move to standardize and upgrade its entire domestic asset base.
• The global relevance of this domestic strategy is validated by the success of other Japanese tech firms abroad. The deployment of autonomous control AI by Yokogawa at Aramco‘s gas plant in Saudi Arabia demonstrates that the underlying technology is robust, scalable, and has significant export potential, creating future commercial opportunities for ENEOS‘s own developed systems.

AI in Refining: ENEOS Advances from R&D to Commercial-Scale Autonomous Control

The AI technology for refinery control, particularly deep reinforcement learning, has matured from pilot-phase validation to initial commercial deployment within ENEOS‘s operations. This progression is a key indicator for investors that the technology is no longer an exploratory R&D expense but a value-generating operational asset.

• In the 2021-2024 period, the technology was in the advanced pilot stage. Successful long-duration trials by both ENEOS/PFN (40 days) and Yokogawa/JSR (35 days) proved the AI’s capability to manage complex, dynamic industrial processes. The formation of the Ideaplatz joint venture in 2023 signaled a clear intent to move from technical validation to commercialization.
• From 2024 into 2025, the technology entered the commercial scale-up phase. The launch of a fully autonomous crude oil distillation unit in January 2024 and the strategic plan to roll out AI across all nine refineries by 2026 confirms its transition from a successful experiment to a core operational standard for the company.

Table: SWOT Analysis: ENEOS’s Strategic Position in AI-Driven Energy

Future Outlook: ENEOS’s Next Move in the AI-Energy Nexus

The critical next step for ENEOS is to leverage its proven AI operational expertise beyond traditional refining and apply it to new low-carbon energy systems to secure its long-term viability in a decarbonizing world. The company has built a powerful capability in autonomous process control that can serve as the foundation for its energy transition strategy. Tracking these next moves is essential for understanding how ENEOS will compete in the future.

• The expertise gained in autonomously controlling complex chemical processes in refineries is directly transferable to managing new, data-intensive energy value chains. This includes optimizing blue hydrogen production, managing ammonia cracking facilities, and ensuring the stability of carbon capture and storage (CCS) operations.
• The foundational partnership with PFN, which began with materials informatics in the PFCC joint venture, provides a ready-made framework to accelerate the development of new catalysts and materials. These are essential for making future energy systems like hydrogen and synthetic fuels commercially viable.
• Japan’s massive government investment in AI, with over ¥10 trillion pledged for the sector, creates strong policy tailwinds. This financial and political backing will likely support ENEOS‘s efforts to secure funding and de-risk its expansion into next-generation, AI-driven energy projects.

Frequently Asked Questions

What is the main goal of ENEOS’s AI strategy for its refineries?
ENEOS’s primary goal is to advance from pilot projects to deploying fully autonomous control systems in its core refinery operations. The strategy aims to use AI to enhance operational stability and efficiency, with a plan to implement AI-equipped failure prevention systems across all nine domestic refineries by fiscal year 2026, securing its future business model.

Who is ENEOS’s key partner in developing its AI technology?
ENEOS’s key AI partner is Preferred Networks (PFN). Their collaboration has progressed from research and pilot tests, like the world-first 40-day autonomous plant operation, to forming commercial joint ventures such as Ideaplatz and Preferred Computational Chemistry (PFCC).

How is ENEOS funding its AI development and commercialization?
ENEOS is funding its AI initiatives through strategic joint ventures. It established the Ideaplatz joint venture with PFN in October 2023 with 100 million JPY in initial capital to commercialize autonomous plant operation systems. This follows an earlier joint venture, PFCC, also established with 100 million JPY, to develop new materials using AI.

What significant milestone did ENEOS achieve in early 2024 with its AI technology?
In January 2024, ENEOS achieved a world-first milestone by beginning the continuous autonomous operation of an entire crude oil distillation unit using a reinforcement learning AI. This marked the transition from successful pilot projects to live, commercial-scale deployment of the technology.

Beyond traditional oil refining, what are the future opportunities for ENEOS’s AI expertise?
The expertise ENEOS has developed in autonomous process control is transferable to new low-carbon energy systems. Future opportunities include optimizing hydrogen and ammonia production, managing carbon capture and storage (CCS) operations, and potentially exporting its AI-driven solutions globally, a model validated by Yokogawa’s success with Aramco.