聚焦科技创新与产业发展深度融合，国资央企再出重磅部署。 国资委党委日前召开专题党委会议，对国资央企科技创新工作作出新一轮部署，强调促进科技创新与产 业创新深度融合，发挥国资央企优势，以科技创新重塑产业链、以产业发展牵引创新链，加快向现实生 产力转化。 中国企业联合会特约高级研究员刘兴国对《华夏时报》记者表示，2026年国企改革将持续深化"三项制 度"改革，健全多元有效激励机制，完善创新体系、优化创新环境、创新发展模式，加快科技成果转 化，全面激发企业创新动能。同时，将针对科技型企业特点优化激励政策，让科研骨干共享发展成果， 持续释放创新创造活力。 近期密集召开的央企年度工作会议上，"科技创新"成为高频词与硬任务。 中国五矿集团2026年度工作会议强调，"十五五"高质量发展的成败关键在于科技创新，现代化产业体系 建设的关键在于科技创新，提质增效转型升级的机遇在于科技创新。国家能源集团总经理冯来法在2026 年全国能源工作会议上强调，强化创新驱动发展。充分发挥新组建的集团科研总院作用，突出企业创新 主体地位，推进科技创新与产业创新深度融合，加快高水平科技自立自强。 中国石油集团去年在科技创新方面着力高水平科技自立 ...

Core Insights - The integration of intelligent manufacturing and artificial intelligence (AI) in China is deepening, providing strong momentum for industries like petrochemicals and steadily building future competitive advantages [1] Group 1: Intelligent Factory Development - The cultivation of leading intelligent factories is a strategic approach for China to establish global manufacturing benchmarks, with significant progress observed in manufacturing models, core technologies, and industrial value [2] - Leading factories are becoming industry transformation benchmarks, exemplified by Zhenhai Refining & Chemical's catalytic cracking unit achieving over 99% self-control rate through digital twin technology, resulting in a 29% increase in production efficiency and a 47% reduction in product defect rates [2] - AI has penetrated over 70% of business scenarios in leading factories, with over 6,000 vertical models developed, driving the large-scale application of more than 1,700 key intelligent manufacturing equipment and industrial software [2] - Leading factories are transitioning from "product manufacturers" to comprehensive providers of "products + services + solutions," collaborating with over 1,300 upstream and downstream enterprises to elevate the entire industrial chain [2] Group 2: AI and Manufacturing Transformation - The Ministry of Industry and Information Technology (MIIT) has introduced the "AI + Manufacturing" action plan, focusing on technological innovation, integration applications, enterprise cultivation, ecological construction, and safety governance to accelerate high-quality development in the AI industry [4] - The MIIT emphasizes the importance of developing a workforce skilled in both AI and manufacturing, particularly in interdisciplinary fields such as synthetic biology and AI-assisted materials design, to support the transformation of the petrochemical industry [4] Group 3: Digital Industry Breakthroughs - The digital industry is a key support for the deep integration of the real economy and digital economy, with significant advancements in scale, innovation capability, and empowering applications since the start of the 14th Five-Year Plan [5] - The scale of the digital industry is steadily growing, reinforcing its role as an economic stabilizer, while innovation capabilities are accelerating, enhancing the effectiveness of new growth drivers [5] - The MIIT plans to continue accurately grasping the trends and rules of digital industry development, accelerating technological innovation and empowering applications, and strengthening standard leadership and ecological construction [5]

Core Viewpoint - The oil and gas industry must transition from a hypothesis-driven approach to a data-driven approach, especially in the context of advancements in artificial intelligence (AI) which is seen as a transformative force in the industry [1][9]. Group 1: Hypothesis-Driven vs. Data-Driven Approaches - Hypothesis-driven methodology relies on existing knowledge and theories to formulate assumptions, which are then tested through various forms of data collection and analysis [2]. - The advantages of hypothesis-driven approaches include clear direction and focus on specific problems, while the disadvantages involve potential biases from initial assumptions [2]. - Data-driven approaches emphasize the use of data to uncover patterns and insights, utilizing statistical analysis and machine learning, which can lead to more objective findings [3]. - The integration of AI technologies enhances the data-driven approach, allowing for significant advancements in the oil and gas sector [3]. Group 2: Historical Context and Evolution - Early oil and gas discoveries were primarily based on intuition and experience, with significant historical examples such as the first commercial oil well drilled by Edwin Drake in 1859 [5]. - The development of geological theories in the mid-19th century laid the groundwork for large-scale oil discoveries, demonstrating the effectiveness of hypothesis-driven exploration [6]. - Recent advancements in data-driven methodologies, such as the GeoGPT model, signify a shift towards integrating AI in geological research, enhancing the efficiency of oil and gas exploration [7]. Group 3: Future Implications and Industry Transformation - The oil and gas industry is witnessing a paradigm shift towards data-driven management, which is expected to significantly improve operational efficiency and decision-making processes [9]. - The potential for AI to revolutionize the industry includes enhancing resource discovery, increasing recovery rates, and integrating with renewable energy sources [10]. - Companies are encouraged to embrace the "AI + oil and gas" era, adapting to new technologies and methodologies to remain competitive and sustainable [10].

Investment Rating - The report maintains an "Overweight" rating for the basic chemical industry [1] Core Insights - The chemical and new materials industry is set to drive the comprehensive "AI + manufacturing" transformation, as outlined in the State Council's policy document released in August 2025, which aims for deep integration of AI across six key sectors by 2027 [3][4] - The focus for the petrochemical industry is on "quality improvement and efficiency enhancement" through AI, utilizing large models and digital twin technologies to optimize various processes [5] - The report identifies three main pathways for chemical companies to implement AI: self-developed large models, third-party model integration, and investment in AI startups [13][14] Summary by Sections Policy Guidance - The State Council's document emphasizes the necessity for AI integration in the chemical industry, marking it as a compulsory aspect for achieving high-quality development [3] - The Ministry of Industry and Information Technology's implementation opinions further detail goals for AI technology and its application in manufacturing by 2027 [4] AI Empowerment in Petrochemical Industry - AI's role in the petrochemical sector focuses on enhancing operational efficiency and safety through predictive maintenance and process optimization [5] - The establishment of high-quality data sets and infrastructure is crucial for supporting AI applications in the industry [5] AI Empowerment in New Materials Industry - The new materials sector aims to leverage AI for deep integration in research and development, enhancing capabilities in material design and synthesis [5] Implementation Pathways - **Self-Developed Large Models**: Companies like China National Petroleum Corporation (CNPC) and China National Offshore Oil Corporation (CNOOC) are developing proprietary AI models to enhance their operational capabilities [9][10] - **Third-Party Model Integration**: WanHua Chemical collaborates with Huawei Cloud to implement AI solutions for predictive maintenance and operational efficiency [11] - **Investment in AI Startups**: Companies like Qicai Chemical are investing in AI startups to accelerate innovation in materials science [12][13] Investment Recommendations - The report suggests focusing on leading companies that excel in data utilization and AI integration, such as CNPC, Sinopec, and WanHua Chemical [14] - Attention is also drawn to companies involved in new materials and fine chemicals, which are expected to benefit significantly from AI-driven R&D advancements [14]

石化化工行业智能化转型再获助力 记者注意到，作为《意见》的细化，两个附件提到的具体措施与石化化工行业息息相关。《指引》提出 要推动石化化工行业提质增效。如综合利用大模型、数字孪生技术突破油气勘探开发、化工新材料研发 范式。深度融合油气生产作业、管网储运、化工工艺等工艺机理、专家经验、生产运行数据等，打造石 化化工行业大模型，推动大小模型融合应用，实现油田作业区及化工安全生产监测预警、设备预测性维 护、工艺流程自适应优化、产品质量预测等。构建行业高质量数据集、数据资源节点等数据基础设施， 支撑行业大模型、智能体训练与开发，提升复杂场景人工智能应用水平。《指南》则为企业智能化转型 升级提供实施路径和方法指引。 "2023年以来，行业重点企业积极探索人工智能应用，涌现出智能化工大模型、TPT时序大模型、昆仑 大模型等系列专用大模型，但仍存在高质量数据集建设进展缓慢、可靠性不足、基础支撑薄弱等问 题。"工信部石化化工行业数字化转型推进中心秘书长李渊源介绍说，而此次《指引》专门提到推动石 化化工行业提质增效，《指南》也给出初步的落地应用指引，建议企业进一步夯实自身数据基础，挖掘 人工智能高价值应用场景，实现企业智能化升 ...

Core Insights - The integration of artificial intelligence (AI) and energy is a strategic approach to ensure energy security, promote green transformation, and cultivate new productive forces in China, reflecting the country's commitment to solving development challenges through technological innovation [1] Policy Direction - Since the 18th National Congress, significant achievements have been made in China's energy sector, but the triple mission of ensuring safety, promoting transformation, and improving efficiency remains challenging. By 2025, China's total electricity consumption is expected to exceed 10 trillion kilowatt-hours, with new wind and solar power installations projected at approximately 370 million kilowatts [2] - The National Development and Reform Commission and the National Energy Administration have issued implementation opinions to promote high-quality development of "AI + energy," establishing a timeline and roadmap for deep integration [2] - The goal is to explore replicable and scalable comprehensive solutions and business models, creating a new paradigm for the integration of AI and energy [2] Practical Applications - AI is enhancing efficiency across the entire energy supply chain. In coal mining, AI inspection systems have significantly improved safety hazard identification, reducing underground personnel by 25% [3] - In thermal power generation, intelligent scheduling systems can reduce coal consumption by 0.8 grams per kilowatt-hour, leading to substantial CO2 emissions reductions [3] - AI applications in renewable energy, such as precise forecasting models in Inner Mongolia, are increasing the utilization rate of green electricity and addressing transmission challenges [3] Competitive Advantages - China possesses unique competitive advantages in energy intelligent transformation, with over 80% of major international oil and gas companies already investing in energy digitalization. The country has a vast energy market and diverse application scenarios, with the AI industry expected to exceed 700 billion yuan by 2024 [4] - The "East Data West Calculation" project is facilitating the migration of computing power to clean energy-rich areas, creating favorable conditions for China to take the lead in global energy transition [4] Challenges and Solutions - Despite initial successes, the integration of AI and energy faces multiple challenges, including the "black box" nature of large models affecting reliability in critical areas like grid scheduling and nuclear safety [5] - Data sharing is hindered by inconsistent standards and the "data island" phenomenon, particularly in the oil and gas sector due to confidentiality and management differences [5] - There is a shortage of interdisciplinary talent who understand both energy systems and AI algorithms, which is a bottleneck for industry upgrades [5] Future Development - The integration of AI and energy is becoming a crucial indicator of core competitiveness in the energy sector, transforming the industry from passive to proactive management [6] - The ongoing transformation must prioritize safety and address existing bottlenecks to ensure that AI becomes the core engine of energy transition [6]

当台风"竹节草"裹挟狂风暴雨侵袭浙江沿海时，国网嘉兴供电公司的调控员在人工智能虚拟总指揮长"启航"的协 助下，30分钟内便完成了以往需数小时的故障信息梳理与处置方案制定；在内蒙古的茫茫草原上，AI算法提前24 小时精准预测出风电场的发电量波动，让电网调度提前做好应对准备；深海油气平台上，智能监测系统通过数据 分析实时排查设备隐患，将传统巡检的风险系数大幅降低……这些场景并非科幻电影的片段，而是中国能源行业 借助大数据与人工智能技术实现转型升级的真实写照。 在全球能源转型加速推进的今天，传统能源秩序正被悄然重构。从"资源主导"到"数据驱动"，从"人力运维"到"智 能调控"，能源行业的变革逻辑正在发生根本性转变。中国作为全球能源生产与消费大国，凭借庞大的能源基础设 施、丰富的应用场景和持续的技术创新，在能源大数据与人工智能融合应用领域走出了一条独具特色的发展道 路，不仅为自身能源高质量发展注入强劲动力，也为全球能源转型提供了新的思路与借鉴。 一、时代必然：能源转型与智能技术的双向奔赴 进入21世纪以来，全球能源格局面临着双重挑战：一方面，传统化石能源的过度依赖导致环境问题日益突出，碳 中和成为各国共同的发展目标； ...

Core Viewpoint - The announcement of the first batch of 15 leading intelligent factories in China, including companies in the petrochemical sector, highlights the significant potential for digital transformation and improvements in this industry [1]. Group 1: Intelligent Factory Development - The construction of intelligent factories is essential for digital transformation, with digital twin factories being a key component of digital engineering [2]. - China Petrochemical's Zhenhai Refining & Chemical Company is the only refining enterprise on the list of leading intelligent factories, achieving full coverage of project phases and integrated business processes through a collaborative digital engineering approach [2]. - The optimization of the entire production chain, from raw materials to product delivery, is a common challenge faced by petrochemical companies during digital transformation [2]. Group 2: Global Optimization Techniques - Global optimization is a foundational technology that enhances production efficiency across various scales and processes, aligning with national strategic needs [3]. - New intelligent global optimization solvers have been successfully applied to processes like wet phosphoric acid production, enabling precise simulations and comprehensive optimizations [3]. - The establishment of a modern management system is crucial for the operation of intelligent factories, as demonstrated by China National Petroleum's Dushanzi Petrochemical Company, which has gained recognition as a benchmark for intelligent manufacturing [3]. Group 3: AI Integration in the Industry - Artificial intelligence (AI) is a transformative technology for the petrochemical sector, with the Kunlun model being the first industry-specific large model approved in China [5]. - The Kunlun model has defined five key objectives, including industry models and application scenarios, and has developed 470 intelligent scenarios to enhance operational efficiency [5]. - China National Petroleum has trained 62 large models across various business lines, leading to initial improvements in operational efficiency [5]. Group 4: AI-Driven Innovations - The focus on computing power, data, and algorithms is essential for upgrading enterprise management and enhancing business efficiency through AI [6]. - The development of AI applications, such as AI-assisted design and material modification, showcases the significant potential of AI in the industry [6]. - A new generation of intelligent factory solutions integrating AI and industrial internet aims to address the diverse needs of the petrochemical sector, enabling fully automated operations and optimized production processes [6].

"当下先进的科技技术究竟该如何扎根于油气矿山行业，给行业带来改变？经过不断的摸索，我们的答 案是以用促建，让技术从业务的痛点中生长出来。"11月26日，在北京举行的华为中国政企业务油气矿 山2025媒体沟通会上，华为油气矿山集团副总裁吴海宇如是说。在这场媒体沟通会上，三位来自华为油 气矿山军团的发言人讲述了华为如何通过一场"自下而上"的技术渗透，让人工智能(AI)牢牢扎根于能源 化工行业，从辅助系统进入核心生产流程。 让技术沾上"机油味" 当前，传统能源化工行业面临多重挑战，安全管控、效率提升、绿色转型等不同要求为行业发展带来了 较为沉重的压力。在谈及如何应用AI助力能化行业转型时，吴海宇表示，AI的应用需要围绕真实的业 务难题，从场景出发，实现数字化技术的落地应用。 应用这一策略，华为瞄准石油化工行业能耗与安全性两大关键，联合云天化（600096）打造了全球首个 煤气化实时在线优化技术(RTO)大模型项目，使煤气化装置能够精确模拟并预测气化炉炉温、渣层厚度 及渣黏度等关键运行参数，从而保障生产过程的深度优化与极致稳定。该项目投用后，预计每年实现节 煤9000多吨、减少二氧化碳排放量2万多吨，带来每年超千万元 ...

Core Insights - The integration of artificial intelligence (AI) technology is accelerating within the oil and petrochemical industry, driving transformation across the entire value chain from exploration to production management [1] - The application of AI in industrial scenarios must be approached cautiously due to high safety requirements and low tolerance for errors, necessitating a "human-machine collaboration" strategy [2][3] - AI is transitioning from being an auxiliary tool to becoming a core support system in oil and gas field development, with significant potential to enhance recoverable reserves and reduce development costs [4] Group 1: AI Integration and Challenges - Experts emphasize the importance of a rational approach to AI adoption in the oil and petrochemical sector, balancing the potential to address knowledge asymmetries with the need to avoid blindly chasing new technologies [3] - The current mismatch in supply and demand for AI expertise in the industry is highlighted, with many companies lacking familiarity with industrial processes while AI-savvy firms lack industrial knowledge [2] - The establishment of platforms for AI-enabled industrial supply-demand matching is underway, facilitating over 300 offline connections and fostering successful case studies in various fields [2] Group 2: Technological Advancements - AI technologies are showing promising results in enhancing oil and gas field development, with studies indicating a potential 5% increase in recoverable reserves and a 10% to 30% reduction in development costs [4] - The development of autonomous and controllable technology systems is urgent, as over 90% of reservoir numerical simulation software is currently imported [4] - Innovative models and methods, such as the PINN-GCEM and the generalized connection unit method, have been developed to improve simulation speed and efficiency significantly [4] Group 3: Industry Initiatives - Major state-owned enterprises are leading the integration of AI and industrial internet applications, with China National Offshore Oil Corporation (CNOOC) implementing intelligent remote control production systems [6][7] - China Petroleum and Chemical Corporation (Sinopec) has developed a three-tier model system and a large model with significant parameters, enhancing data processing capabilities across various applications [7] - China National Petroleum Corporation (CNPC) has created a multi-layer architecture for its Kunlun model, significantly increasing the parameters of its language and visual models [8]