China's energy companies are entering a more advanced stage in their digital and intelligent transformation.

　　Recently, the "2025 China Petroleum and Petrochemical Enterprises Information Technology Exchange Conference & High-Level Forum on the Development of New-Generation Productivity in the Oil and Gas Industry" and the "Digital Intelligence Leading the Future: The 'Smart' and 'Green' Paradigm for Energy Systems" conference were held in Beijing. Experts attending the conferences engaged in in-depth discussions and extensive exchanges around such topics as how digital and intelligent technologies are driving innovation and green, low-carbon transformation in the petroleum and petrochemical industries; trends and application scenarios of digital and intelligent technologies; the latest achievements in digital and intelligent transformation; as well as the practical implementation of large-scale AI models and ensuring independent control over key core technologies.

　　This edition is based on the latest practices of four state-owned energy enterprises—China National Petroleum Corporation, Sinopec, CNOOC, and the National Pipeline Network—and provides an in-depth analysis of the strategic framework, key technological breakthroughs, and typical application scenarios for the digital and intelligent transformation of the energy industry, showcasing the vibrant real-world examples of digital and intelligent transformation undertaken by China’s energy companies.

An intelligent, unmanned sample-handling vehicle equipped with ethylene technology shuttles around the Tianjin Nangang plant, picking up and delivering samples to various units. Photo report by Zhang Xundie and Qi Xiaojiao.

The nation’s first nine-axis intelligent welding robot, developed by Shijian Company, is currently performing pipeline welding operations. Photo by Tian Yuanwu.

　　China's energy companies are entering a more advanced stage in their digital and intelligent transformation.

　　□ This newspaper’s reporter, Ji Jiaxin

　　Currently, the global energy industry is undergoing an unprecedented transformation toward digitalization and intelligentization. With the rapid advancement of next-generation information technologies such as artificial intelligence, big data, and cloud computing, energy companies are accelerating their digital and intelligent transformations to enhance production efficiency, optimize operational management, and innovate business models, ultimately achieving high-quality development. As a major global producer and consumer of energy, China’s state-owned energy enterprises’ practices in digital and intelligent transformation hold significant demonstrative value and serve as a valuable reference for others.

　　Strategic Leadership and Top-Level Design: Crafting a Blueprint for Digital and Intelligent Transformation

　　The digital and intelligent transformation of energy companies is a comprehensive change that involves strategic restructuring, organizational transformation, and business reshaping. It has gone beyond mere technological upgrades and has become the core driving force behind corporate development. Energy companies generally elevate digitalization to the level of corporate strategy, ensuring the systematic and sustained nature of the transformation through scientific top-level design.

　　The digital and intelligent transformation of energy companies is by no means a simple technological upgrade; rather, it’s a comprehensive transformation that involves strategic restructuring, organizational change, and business reshaping. Leading energy companies have long since elevated digitalization to the core of their corporate strategy, ensuring the systematic and sustained nature of the transformation through scientifically designed top-level planning. Zhao Xueliang, Deputy General Manager of the Information and Digitalization Management Department at Sinopec, believes: “The development of artificial intelligence in the oil and petrochemical industry needs to proceed in an orderly manner, and industrial development should focus on application-oriented approaches. In particular, we must adhere to the objective laws governing the development of informatization, digitalization (standardization), and intelligentization.”

　　In terms of strategic positioning and goal systems, all four state-owned energy enterprises have identified digital intelligence as their core corporate strategy. China National Petroleum Corporation has designated “Digital Intelligence Oil” as its fifth major strategic initiative, comprehensively promoting three key projects—strengthening informationization, empowering with digital technologies, and fostering intelligent development—and regards the Kunlun Large Model as the central task of its intelligent development initiative. In its “1534” development strategy formulated in 2021, CNOOC explicitly incorporated digital transformation—from traditional management to modern, digital, and intelligent management—as an integral part of its strategic framework. This high-level strategic positioning ensures that digital intelligence initiatives will receive sufficient resource allocation and organizational attention.

　　Architectural design and implementation strategies are critical components for translating strategy into action. Each enterprise, based on its own business characteristics, has designed a systematic digital and intelligent architecture along with a detailed implementation roadmap. China National Petroleum Corporation has developed the “1+4+N” Kunlun Large Model Architecture System: The L1 layer features industry-specific large models aimed at empowering the energy and chemical sectors; the L2 layer includes four types of specialized large models—covering business, management, common capabilities, and scientific computing—and the L3 layer consists of scenario-specific large models built on demand. This hierarchical architecture not only ensures technological consistency but also meets the diverse needs of various business lines. Sinopec has formulated a clear three-phase plan: in 2025, it will focus on “comprehensive planning and key breakthroughs”; from 2026 to 2027, it will “accelerate progress and broadly enable”; and from 2028 to 2033, it will “deeply empower and reshape business models,” reflecting a long-term, transformation-oriented mindset.

　　Organizational mechanisms and cultural transformation are crucial safeguards for strategic implementation. In its digital and intelligent transformation, the National Pipeline Network has innovatively adopted a “process-penetration empowerment” approach, embedding digitalization into the very DNA of the pipeline network’s development. The National Pipeline Network has introduced a digitalization department management system, under which the general manager of each department serves as both the process owner and data owner for their respective 6.0 business domains, reflecting a profound shift in management culture and mindset. Jia Lei, Deputy General Manager of the National Pipeline Network’s Digitalization Department, stated, “Digitalization has indeed brought about some distinctive changes to the pipeline network,” and these changes largely stem from the innovative design of organizational mechanisms.

　　From a strategic perspective, the digital and intelligent transformation of energy enterprises has gone beyond the technological realm and become the core driving force behind management reform and business innovation. The remarks by Zhang Wei, Chairman of the National Pipeline Network, are particularly representative: “If we don’t embark on digital transformation, we won’t make ‘mistakes,’ but we’ll also miss an entire era.” The depth and breadth of this strategic awareness will ultimately determine the success of enterprises’ digital and intelligent transformation. CNOOC, for its part, emphasizes that “informationization is not merely a technical issue—it’s a strategic one.” By placing digitalization at the heart of its strategy, CNOOC ensures the continuity and stability of its transformation efforts. As the transformation progresses in greater depth, strategic planning must also be dynamically adjusted to seize new opportunities and meet new challenges brought about by advances in technology and market changes.

　　Data Governance and Foundational Capability Building: Solidifying the Foundation for Digital and Intelligent Transformation

　　High-quality data and robust foundational capabilities are prerequisites for successful digital and intelligent transformation. Faced with challenges such as data fragmentation and inconsistent standards, industries are reconfiguring their governance systems, establishing unified data architectures and quality standards, and developing specialized tools to enhance data processing efficiency.

　　The research and development of industrial software and mechanistic models are receiving great attention, with emphasis placed on the integration of data and industry-specific knowledge. The hardware infrastructure—including communication networks—is continuously being strengthened, providing the necessary support for real-time data acquisition and remote control. These foundational efforts, which require substantial investment and yield results only gradually, are driving the industry from superficial applications toward the development of core competencies, thereby laying a solid foundation for deep intelligentization.

　　In the practice of digital and intelligent transformation in the energy industry, data governance and foundational capability building constitute the prerequisites for successful transformation. Chen Su, Deputy General Manager of the Technology and Digital Intelligence Department at CNOOC, stated frankly: “The foundation of all intelligent initiatives stems from our understanding and perception of smart oilfields; the result of this perception is precisely the wealth of data we have today.” This statement underscores the central role of data in the digital and intelligent transformation process. Without high-quality data and robust foundational capabilities to support it, any advanced intelligent application would be like a castle built on sand—unstable and unlikely to endure.

　　Rebuilding the data governance system has become a common choice for energy companies. Faced with longstanding issues such as scattered data, inconsistent standards, and uneven data quality, various enterprises have launched intensive efforts to tackle data governance challenges. CNOOC has shut down all more than 100 previously used systems for exploration and development, and is now rebuilding an entirely new architecture—“data collection goes to collection, applications go to applications, with a data lake in between.” Although this “separation of collection and application, with governance at the source” model presents significant implementation challenges, it lays a solid foundation for subsequent intelligent applications. The data lake not only serves as a storage facility but also houses all governance processes, results, models, and services, while simultaneously taking on responsibilities such as data validation and management. Meanwhile, the National Pipeline Network has established a data system comprising 18 core domains and, through the “WeGet Data Usage Portal,” provides a seamless data consumption experience throughout the entire group.

　　Data quality standards and tool innovation are crucial supports for governance efforts. Sinopec took the lead in developing the first smart manufacturing standard system for the petrochemical industry, establishing a structured framework for smart manufacturing standards in the petrochemical sector and identifying 124 smart manufacturing standards. This work supported the official issuance by the Ministry of Industry and Information Technology of the “Guidelines for Building a Smart Manufacturing Standard System for the Petrochemical Industry (2022 Edition),” marking the completion of top-level standardization planning for smart manufacturing in the petrochemical industry and effectively addressing the question of “which standards are needed to build intelligent petrochemical plants.” In the development of the Kunlun large-scale model, PetroChina formulated a “four-stage, eight-step” methodology and deployed digital data processing tools on its AI platform to ensure high-quality data supply. They developed 73 data collection and annotation specifications tailored to specific needs and created specialized data processing tools for applications such as well logging and seismic data analysis. As a result, their datasets have reached a scale of 500 TB, including 1 million industry-specific question-and-answer pairs and 250,000 industry reasoning question-and-answer pairs. In April 2025, PetroChina’s seismic exploration and well logging datasets were selected as one of the 30 outstanding achievements in high-quality dataset construction among central enterprises at the 8th Digital China Construction Summit.

　　The research and development of industrial software and mechanistic models are receiving unprecedented attention. In response to risks such as supply disruptions and backdoors in petrochemical industry software, Sinopec has joined forces with universities and research institutions to establish innovative consortia, actively planning and advancing the systematic development and breakthroughs in petrochemical industry software to ensure the security of the petrochemical industry chain. Sinopec is making concerted efforts to promote the domestic development of industrial software including the Basic Physical Properties Database (PCdata), Process Simulation Software (OPEN), 3D Plant Design Software (PC3D), and Intelligent Pipeline Stress Analysis Software (PCFEA). Taking the Basic Physical Properties Database as an example, this database serves as a crucial cornerstone for both fundamental scientific research and the development of industrial chains. The PCdata software, independently developed by Sinopec through its own R&D efforts, is a professionally-oriented, self-controllable database platform covering fields such as petrochemicals, natural gas chemicals, and coal chemicals. It provides high-quality basic data services for scientific research and development, production operations and maintenance, engineering design, and software development. Currently, the α version of PCdata has been released and is providing foundational data support for national-level projects—including the development of high-pressure polyethylene process packages and research on petroleum properties under electromagnetic fields—as well as for Sinopec’s deep integration of artificial intelligence and scientific research (AI for Science). Additionally, PCdata is offering data query services to institutions such as Tsinghua University and XinFu Technology.

　　The fundamentals of network communication are particularly crucial for energy companies with widely dispersed operations. As CNOOC primarily operates offshore, it cannot fully leverage societal communication resources and thus has been compelled to build its own communication network independently. Take the “Deep Sea No. 1” project as an example: CNOOC has comprehensively employed a range of communication technologies—including microwave, scatter, microscatter, fiber optics, cables, and satellites—to address various challenges. The backbone link bandwidth between land and sea has been increased from 25 gigabits per second to 43.9 gigabits per second, and the Beidou satellite coverage rate for offshore production facilities has reached 100%, significantly enhancing these facilities’ resilience to risks.

　　Building foundational capabilities often requires significant investment and yields slow results; however, energy companies have come to recognize that this stage is indispensable for digital and intelligent transformation. The digital and intelligent transformation of China’s energy industry is now entering deeper waters—shifting from superficial applications toward the development of core competencies. As these foundational efforts continue to be refined and improved, the digital and intelligent applications of energy companies will open up even broader avenues for growth.

　　Scenario-Driven and Value Realization: Unleashing the Power of Digital and Intelligent Transformation

　　The effectiveness of digital and intelligent transformation is ultimately measured by the value created through business scenarios. The intelligentization of production and operations has significantly boosted the efficiency of exploration, development, and equipment management, and safety early-warning systems have shifted from post-event response to proactive prevention. Substantial breakthroughs have been achieved in process optimization and energy conservation and emission reduction, while management decision-making systems are evolving toward remote and intelligent operations. The paradigm of scientific research and innovation has been profoundly reshaped by AI, accelerating experimental design and theoretical breakthroughs. In the future, we will shift from isolated applications to collaborative development across the entire industrial chain, achieving end-to-end transformation through the seamless integration of various scenarios. By adhering to a problem-oriented approach and focusing on value measurement, we will drive technology from mere efficiency enhancement toward innovation-led advancement, ultimately realizing a qualitative upgrade of the industrial ecosystem.

　　The digital and intelligent transformation of energy enterprises ultimately needs to be grounded in specific business scenarios, demonstrating its effectiveness by addressing real-world challenges and creating tangible value. In the construction of the Kunlun large-scale model, China National Petroleum Corporation explicitly proposed the principle of "promoting construction through practical application," systematically developing scenario-based initiatives around five key aspects: "highlighting core businesses, delivering clear value, being knowledge-intensive, featuring abundant corpora, and ensuring technological feasibility." Since 2012, Sinopec has been engaged in the construction of smart factories, having established a total of 16 such facilities. Through this effort, Sinopec has pioneered a new model for informationization, achieving remarkable application results that have boosted production efficiency and economic benefits, enhanced safety and environmental protection standards, and accelerated the company's digital transformation. This underscores a fundamental consensus in the energy industry regarding the application of digital and intelligent technologies: technology must serve business needs, and the realization of value is the ultimate benchmark for gauging the success of transformation.

　　Intelligent production and operations represent the area of greatest concern for energy companies. In exploration and development, CNPC’s “intelligent seismic interpretation” generates prediction results directly through minimal labeling and fine-tuning, boosting work efficiency by a factor of nine. This technology has been applied in oil and gas fields such as Changqing, Tarim, and Southwest China, where it has improved prediction accuracy by 10 percentage points, enabling the identification of fracture-cavity systems. Meanwhile, “intelligent full-waveform inversion,” under the same computational power conditions, shortens the processing time by 60% and can compress exploration project cycles by 20%. CNOOC’s “Deepsea No. 1” platform, through centralized upgrades to its mechanical, electrical, instrumentation, and safety systems, has become the world’s first semi-submersible production, storage, and offloading platform equipped with remote-control capabilities, achieving a 3% increase in production efficiency and generating cumulative benefits exceeding 200 million yuan.

　　The advancement of intelligent technologies in equipment management and safety early warning has been remarkable. By fine-tuning and pre-training on operational data from pumping unit wells, China National Petroleum Corporation has achieved intelligent condition diagnosis, smart single-well metering, and intelligent optimization of production parameters. The accuracy rate for diagnosing abnormal conditions exceeds 93%, and the accuracy rate for smart metering reaches 90%. Sinopec has shifted its equipment management from post-event maintenance to predictive maintenance and transformed its safety prevention and control from reactive response to proactive early warning. A comprehensive equipment integrity management system has been established, covering the entire lifecycle of equipment assets—from operation and inspection to maintenance, renovation, and decommissioning. Meanwhile, based on standardized operating procedures and leveraging technologies such as visual recognition, big data analytics, and predictive early warning, the company is accelerating the shift in safe production from static analysis to dynamic perception, from post-event emergency response to proactive prevention, and from isolated point-based control to holistic, coordinated defense. Through technologies including 5G, personnel positioning, robots, and drones, the National Pipeline Network has achieved comprehensive situational awareness, providing digital support for safe production.

　　Substantial breakthroughs have been made in process optimization and energy conservation and emission reduction. China National Petroleum Corporation has implemented “Intelligent Operation Optimization for Ethane-to-Ethylene Process,” enabling real-time monitoring of plant operating conditions, early warning of production anomalies, and optimization of operational parameters. This system can provide early anomaly warnings up to 72 hours in advance, with a prediction accuracy rate for plant operating conditions exceeding 90%. It has also boosted ethylene yield by 0.4 percentage points, with an expected annual revenue increase of over 10 million yuan per unit. Meanwhile, Sinopec has shifted its approach to energy conservation and emission reduction from mere indicator monitoring to proactive source reduction. By leveraging technologies such as steam power optimization, online energy consumption assessment, and online early warning systems for harmful gas emissions, Sinopec can now track energy consumption and environmental emissions in real time throughout the entire production process—from supply and generation to transmission, conversion, and consumption—enabling fine-grained management and online optimization across all stages. This approach facilitates multi-medium energy optimization and cascaded energy utilization, effectively reducing energy consumption and pollutant emissions while enhancing overall energy efficiency and environmental performance.

　　The intelligent transformation of management decision-making and service systems has also yielded remarkable results. In the area of integrating artificial intelligence with business management—known as “AI for Management”—Sinopec has deployed big-data audit models that fully support “remote online plus” auditing. By leveraging remote data analysis and pre-audit investigations, these models have significantly enhanced both the quality and efficiency of audit oversight. The application of intelligent auditing models and intelligent customer service systems has likewise greatly boosted work efficiency. The National Pipeline Network is focusing on eight emerging business areas—including new energy, next-generation information technology, and future information—and further subdividing them into more than 20 industries such as optical fiber, intelligent computing centers, and quantum technologies. Actively harnessing artificial intelligence, the network is strategically planning and deploying these cutting-edge industries, thereby building a new productivity matrix that integrates digital technologies with energy development.

　　The research and innovation model is being profoundly transformed by AI for Science. In its practical applications, Sinopec has divided AI for Science into three key areas: "able to read, capable of computation, and diligent in execution." Large-scale models' capabilities in summarizing and reading literature—including the ability to process multilingual texts—help researchers quickly grasp the essence of scholarly articles. In scientific computing, AI can accelerate the guidance of scientific experiments, inspire new theories and algorithms, and optimize the scientific computing process, reducing tens of thousands of experimental trials down to just dozens or hundreds, thereby rapidly narrowing down the optimal experimental direction. "Diligent in execution" refers to using machines to conduct standardized experiments around the clock without interruption, accurately recording the direction and steps of each experiment. This transformative shift in the research paradigm could lead to groundbreaking, disruptive innovations.

　　In the future, scenario collaboration and end-to-end reshaping will become the key development directions. China National Petroleum Corporation plans to “string beads into a chain and cluster chains into a system,” fully integrating artificial intelligence into every stage of the industrial value chain—from upstream to downstream—and throughout the entire process, thereby building an intelligent industrial brain. Sinopec is committed to reshaping its business models by deeply integrating AI technologies with core operations such as scientific and technological R&D, production manufacturing, and operational management, thus driving optimization of resource allocation, reengineering of process flows, and upgrading of safety management paradigms. This evolution—from isolated breakthroughs to holistic optimization—marks that energy companies’ digital and intelligent applications are entering a more advanced stage.

　　The golden rule for applying digital and intelligent solutions in the energy sector is to start with solving business problems and use value creation as the benchmark. This value-oriented mindset ensures the effectiveness of digital and intelligent investments. As technology continues to mature and its applications deepen, the digital and intelligent scenarios in the energy industry will become even more diverse, and value creation will take on greater variety, ultimately enabling a transformative leap from quantitative change to qualitative transformation.

　 　Link: The transformation has just begun.

A corner of the Jiujiang Petrochemical Laboratory. Photo by Xie Fei.

　　The digital and intelligent transformation of energy enterprises is a comprehensive overhaul involving strategy, management, technology, and business—a transformation of unprecedented depth and scope in industrial history. This transformation not only affects the enhancement of enterprises’ own competitiveness but also plays a crucial role in ensuring national energy security and realizing the nation’s digital economy strategy.

　　Future development trends are heading in five main directions: First, shifting from isolated applications toward collaborative innovation across the entire petroleum and petrochemical industry chain—from upstream to downstream in both the supply and value chains. Second, transitioning from data-driven approaches to a dual-engine model driven by both knowledge and data, with greater emphasis on embedding industry-specific knowledge and mechanistic models. Third, extending from technology application to business model innovation, exploring new formats and services enabled by digitalization. Fourth, expanding from enterprise-level autonomy to industry-wide collaboration. Fifth, making the leap from efficiency enhancement to innovation leadership—particularly through avenues such as AI for Science, which can lead to original breakthroughs.

　　Faced with the numerous challenges of digital and intelligent transformation, energy companies must: first, maintain strategic composure—digital and intelligent transformation is a long-term endeavor that requires sustained investment and patience; second, strengthen basic research, especially in key areas such as industry-specific large models, mechanistic models, and the cultivation of multi-disciplinary talent; third, refine the evaluation system by establishing a multidimensional value assessment framework that goes beyond financial metrics; fourth, promote industry collaboration to create synergy in areas such as data standards, foundational software, and shared computing power; fifth, prioritize secure development by building a comprehensive security protection system that covers data, algorithms, systems, and ethics; and sixth, innovate institutional mechanisms and break down organizational barriers.

　　Energy is the material foundation for the development of human civilization, while digitalization serves as the technological engine driving progress in today’s society. The deep integration of these two forces is giving rise to new forms and new patterns in the energy industry. The digital and intelligent transformation practices undertaken by China’s state-owned energy enterprises not only support their own high-quality development but also provide valuable lessons for the global energy industry’s digital transformation. In the era of the digital economy, digital and intelligent transformation has become an essential path for energy companies to build their core competitiveness. Those enterprises that can deeply integrate digital technologies with their core energy businesses and continuously innovate their management and business models will undoubtedly secure a favorable position in the future energy landscape.

　　Looking ahead, as technology continues to advance and its applications deepen, the digital and intelligent transformation of energy companies will unlock even greater potential. From smart oilfields to intelligent energy ecosystems, from efficiency improvements to innovation-led growth, this transformative journey has just begun. Energy companies need to maintain strategic focus, stay true to value-driven principles, strengthen their foundational capabilities, and deepen scenario-based innovation—thus seizing the initiative and securing a bright future in the tide of digital transformation.