TECHNICAL PROGRAMME | Energy Infrastructure – Future Pathways
Pipelines, Storage and SPRs
Forum 8 | Hall 9 - Technical Programme 2
28
April
10:00
11:30
UTC+3
Effective management of pipelines, storage facilities, and Strategic Petroleum Reserves (SPRs) is paramount for ensuring energy security and market stability. As global energy demand exhibits fluctuations, the infrastructure required for oil transportation and storage must adapt to guarantee a reliable supply. This forum will examine advancements in pipeline technology, storage solutions, and the strategic significance of SPRs in mitigating supply disruptions. Key areas of discussion encompass enhancing pipeline safety, optimising storage capacity, and the role of SPRs in emergency response and market stabilisation, thereby contributing to a resilient energy system within a dynamic global context. The geopolitical relevance of this topic is undeniable, as the diversification of pipeline routes emerges as a cornerstone of energy security for entire regions.
Leak detection in oil and gas pipelines remains a critical challenge for operators such as Saudi Aramco, where undetected leaks can escalate into severe safety, environmental, and economic consequences. Conventional methods—including Computational Pipeline Monitoring (CPM), Real-Time Transient Modeling (RTTM), and Negative Pressure Wave (NPW) detection—are widely deployed but suffer from false alarms, sensitivity to operational transients, and limited adaptability. The novelty of this work lies in proposing a unified reflective middleware that fuses CPM, RTTM, and NPW within a single MAPE-K (Monitor–Analyze–Plan–Execute–Knowledge) loop, augmented by lightweight AI modules. Unlike prior studies that treat each method in isolation, the middleware integrates all three into a composite residual test, enhanced by AI-based adaptive thresholding, bias correction of RTTM predictions, and a priority-aware risk index that accounts for critical infrastructure and urban safety zones. This hybrid Physics + AI design preserves the transparency and regulatory trust of physics-based models while introducing adaptability and risk-awareness absent in current systems. The architecture also goes beyond anomaly detection by including an execution layer that issues automated valve closures, compressor throttling, and publish/subscribe alerts via MQTT. Compliance with API RP 1130, API RP 1175, and relevant Saudi Aramco Engineering Standards (SAES) is explicitly addressed, ensuring operational relevance. Simulation studies on representative crude oil and natural gas pipelines demonstrate significantly improved Probability of Detection (POD), reduced False Alarm Rate (FAR), and shorter Mean Time to Detect (MTTD) compared to CPM- or RTTM-only baselines. This integration of physics-based rigor, AI adaptability, and standards compliance defines a new class of reflective middleware for pipeline integrity management, designed specifically for the scale and operational requirements of Aramco’s oil and gas network.
The actual production management and operation mode of the Central Asian natural gas pipeline involves multiple parties at home and abroad in all links of "production, supply, sales and use". The production and operation involve technical issues of regulation and operation across multiple countries and multiple gas sources, and also face many challenges such as complex coordination of multiple parties and complicated integration of multi-national operation systems. At the same time, the contradiction between the gas source and the demand side is also more prominent in different periods. Therefore, how to establish a cross-multinational integrated regulation platform suitable for the actual operation characteristics of the Central Asian pipeline is crucial to ensure the safe and stable supply of my country's natural gas resources. Through system establishment, technological breakthroughs, standard formulation, talent training and other means, the company strives to ensure supply, save energy and reduce consumption, and while fulfilling the obligations of the Northwest Energy Strategic Channel, practice the Belt and Road Initiative and shape the company's political status.
Innovations:
Innovations:
- Create an integrated control platform with the international pipeline control center as the core, build the industry's first-class integrated control platform hardware foundation, and realize the reconstruction of the cross-border long-distance natural gas pipeline control mode.
- The construction of the dispatching and coordination mechanism in Kazakhstan has achieved timely and reliable information communication, ensured the safe and stable operation of the pipeline, and guaranteed the domestic natural gas supply.
- Build an optimized operation system, establish a complete set of optimized operation management content covering work procedures, professional technology, standard unification, and digital exploration, and achieve breakthroughs in optimization technology and energy saving and consumption reduction.
Pipelines are essential to derisking the energy transition by providing a reliable, efficient, and scalable solution of transporting both conventional and low-carbon energy sources. As the world moves toward decarbonization, the versatility of the pipeline infrastructure – particularly its capacity to transport hydrogen and carbon dioxide- supports the integration of clean energy technologies.
Hydrogen pipelines have been in operation for decades, but almost exclusively in an industrial context, carrying hydrogen as a feedstock or product of processes on behalf of industrial gas companies. The widespread use of hydrogen pipelines for energy transportation and storage is new, and has not been implemented at scale.
Carbon dioxide pipelines have equally got a long, and safe track record, but almost exclusively in the context of using naturally occurring CO2, normally in the context of Enhanced Oil Recovery. Anthropogenic CO2, and its associated compositional limits and impurities, has not been transported at scale through pipelines.
Considering the use of pipelines for Future Fuels energy purposes, an operator or investor needs to consider some key questions:
This paper will outline various case studies that have been performed across the world by ROSEN to answer these questions and demonstrate the methods used to perform these studies. These efforts contribute to path the way for a secure and efficient energy transition.
Hydrogen pipelines have been in operation for decades, but almost exclusively in an industrial context, carrying hydrogen as a feedstock or product of processes on behalf of industrial gas companies. The widespread use of hydrogen pipelines for energy transportation and storage is new, and has not been implemented at scale.
Carbon dioxide pipelines have equally got a long, and safe track record, but almost exclusively in the context of using naturally occurring CO2, normally in the context of Enhanced Oil Recovery. Anthropogenic CO2, and its associated compositional limits and impurities, has not been transported at scale through pipelines.
Considering the use of pipelines for Future Fuels energy purposes, an operator or investor needs to consider some key questions:
- How do I ensure that the design characteristics of my pipeline are adequate for the proposed service – is it big enough (both diameter and wall thickness) and is it in the right place?
- How can I prioritise projects or pipelines to ensure the most efficient use of resources?
- What are the predicted CapEx and OpEx requirements for Hydrogen and CO2 pipelines, how do these vary between new-build and repurposed infrastructure?
- To what extent can existing infrastructure be repurposed, how can I ensure that my existing pipelines are suitable for repurposing?
- How can I manage the integrity of my pipeline when it is in Hydrogen or CO2 service given the different operational requirements and integrity threats, e.g. Hydrogen embrittlement and running fracture?
This paper will outline various case studies that have been performed across the world by ROSEN to answer these questions and demonstrate the methods used to perform these studies. These efforts contribute to path the way for a secure and efficient energy transition.
Energy storage technologies are evolving rapidly, driven by the need for efficient and sustainable solutions. Large-scale underground energy storage solutions are pivotal for managing energy resources efficiently, especially as we transit towards renewable resources and low carbon emitting solutions. With increase in global concerns towards changes in patterns of energy usage; from hydrocarbons to green energy, mass scale storage requirements are also adding up challenges to create safe and secured repositories. Underground storage solutions for various type of liquids and gases, varies as per their storage states under ambient temperature conditions. Underground storage solutions are tailored for hydrocarbons and other gaseous products as per their behaviour under storage temperature conditions as it influences the design and engineering aspects of storage systems. Thus, selection of a mass scale storage solution and its functionality beneath the surface depends on many factors such as temperature, pressure, volume, and the chemical properties of the substance.
Underground mass storage solutions can be achieved in form of unlined rock caverns and lined rock cavern systems. Unlined rock caverns are sealed within the rock cavity under the umbrella of outside static water pressure head, for liquids and gases that can be stored in liquid state under ambient temperature conditions. However, technical requirements; depth, overburden and storage chamber design can vary depending upon whether the stored product is crude oil or LPG. High pressurised compressed gases; Natural Gas, Hydrogen and other similar products can also be stored in underground rock caverns; however, the system requirement changes to a more innovative emerging technique of lined rock caverns. This storage system ensures storability of compressed gases at shallower depths, providing high deliverability and turnover rates, underscoring its adaptability to diverse geological conditions along with offering flexibility for future expansion.
This paper presents a case study focused on Lined Rock Cavern (LRC) technology to assess structural performance and stability of underground storage system for varying pressure requirements of up to 200 bars. The findings from the study highlights vital requirements to be considered for carrying out design and engineering of lined rock caverns structures along with associated risk and challenges. Numerical analysis studies demonstrate that internal pressure significantly influences deformation and stress distributions of rock mass and associated concrete lining structure .Technical findings summarised as part of this study critically analyses feasibility of these storage systems for storing compressed gases for pressures varying from 50 to 200 bars.
Underground mass storage solutions can be achieved in form of unlined rock caverns and lined rock cavern systems. Unlined rock caverns are sealed within the rock cavity under the umbrella of outside static water pressure head, for liquids and gases that can be stored in liquid state under ambient temperature conditions. However, technical requirements; depth, overburden and storage chamber design can vary depending upon whether the stored product is crude oil or LPG. High pressurised compressed gases; Natural Gas, Hydrogen and other similar products can also be stored in underground rock caverns; however, the system requirement changes to a more innovative emerging technique of lined rock caverns. This storage system ensures storability of compressed gases at shallower depths, providing high deliverability and turnover rates, underscoring its adaptability to diverse geological conditions along with offering flexibility for future expansion.
This paper presents a case study focused on Lined Rock Cavern (LRC) technology to assess structural performance and stability of underground storage system for varying pressure requirements of up to 200 bars. The findings from the study highlights vital requirements to be considered for carrying out design and engineering of lined rock caverns structures along with associated risk and challenges. Numerical analysis studies demonstrate that internal pressure significantly influences deformation and stress distributions of rock mass and associated concrete lining structure .Technical findings summarised as part of this study critically analyses feasibility of these storage systems for storing compressed gases for pressures varying from 50 to 200 bars.
Pipeline safety serves as a foundational element in ensuring the operational integrity and security of energy infrastructure. Due to the factors, such as geological instability, material fatigue, and unauthorized third-party encroachment, persistent challenges continue to impede real-time risk detection and coordinated emergency response in pipeline systems. Operators now leverage advanced monitoring technologies such as distributed fiber optic sensing (DFOS), unmanned aerial systems (UAS), and computer vision-enabled cameras (CVCs) to achieve holistic perimeter threat assessment. These solutions have driven a 94.4% decrease in pipeline failure rates, with incident frequency dropping from 1.78 (2015) to 0.1 (2024) per 1,000 km-year.
Despite these technological improvements, significant issues persist. Interoperability constraints between monitoring systems have resulted in disparate data ecosystems that hinder unified risk management. Specifically, alarms generated through DFOS lack automated correlation with UAS and CVC data streams, requiring human operators to manually reconcile multi-system verification. Furthermore, post-detection processes dependent on manual data aggregation and decision-making introduces substantial response delays during emergency operations, potentially unsatisfying the requirement of 5-minute emergency action prescribed in API RP 1174:2020 for pipeline leak containment.
The past decade has seen unprecedented advances in artificial intelligence, marked by developments in large language models (LLMs) such as ChatGPT and DeepSeek. These innovations have fundamentally redefined operational paradigms in big data analysis. To address issues mentioned above, a DeepSeek-based intelligent agent was developed for pipeline risk management, enabling intelligent multi-system coordination and decision support across pipeline safety operations. This pioneering architecture positions the AI agent as a cognitive command hub, achieving seamless external system interoperability through industry-standard API protocols while enabling bidirectional data/control flows.
The system architecture further incorporates a AI-powered alarm risk verification engine, which integrates multi-source alert feeds with rule sets and historical incident repositories. This risk verification module autonomously executes various system tasks, such as drone takeoff or camera rotation, and generates priority rankings.
The framework also culminates in an intelligent emergency response knowledge base that synthesizes real-time risk analysis with operational metadata, including pipeline specifications and High-Consequence Area (HCA) geospatial parameters. This cognitive agent autonomously executes: risk stratification through machine learning classifiers, resource allocation optimization via constraint-based algorithms, activation of emergency plans, and automated notification workflows—delivering decision support throughout the emergency management lifecycle.
This technological innovation introduces pipeline risk management into a framework that comprehensively addresses the full operational lifecycle from risk control to emergency response. By implementing intelligent cross-system data integration and leveraging DeepSeek's advanced analytical capabilities, the solution achieves significant operational efficiencies - reducing manual intervention while accelerating risk verification and response times. Operational data demonstrates the system's proven effectiveness, currently delivering annualized cost savings exceeding RMB 3.6 million through optimized resource allocation and incident prevention measures.
Co-author/s:
Zixiao Chen, Senior Engineer, PetroChina.
Liwen Tan, Senior Engineer, PetroChina.
Jingdong Chen, Senior Engineer, PetroChina.
Despite these technological improvements, significant issues persist. Interoperability constraints between monitoring systems have resulted in disparate data ecosystems that hinder unified risk management. Specifically, alarms generated through DFOS lack automated correlation with UAS and CVC data streams, requiring human operators to manually reconcile multi-system verification. Furthermore, post-detection processes dependent on manual data aggregation and decision-making introduces substantial response delays during emergency operations, potentially unsatisfying the requirement of 5-minute emergency action prescribed in API RP 1174:2020 for pipeline leak containment.
The past decade has seen unprecedented advances in artificial intelligence, marked by developments in large language models (LLMs) such as ChatGPT and DeepSeek. These innovations have fundamentally redefined operational paradigms in big data analysis. To address issues mentioned above, a DeepSeek-based intelligent agent was developed for pipeline risk management, enabling intelligent multi-system coordination and decision support across pipeline safety operations. This pioneering architecture positions the AI agent as a cognitive command hub, achieving seamless external system interoperability through industry-standard API protocols while enabling bidirectional data/control flows.
The system architecture further incorporates a AI-powered alarm risk verification engine, which integrates multi-source alert feeds with rule sets and historical incident repositories. This risk verification module autonomously executes various system tasks, such as drone takeoff or camera rotation, and generates priority rankings.
The framework also culminates in an intelligent emergency response knowledge base that synthesizes real-time risk analysis with operational metadata, including pipeline specifications and High-Consequence Area (HCA) geospatial parameters. This cognitive agent autonomously executes: risk stratification through machine learning classifiers, resource allocation optimization via constraint-based algorithms, activation of emergency plans, and automated notification workflows—delivering decision support throughout the emergency management lifecycle.
This technological innovation introduces pipeline risk management into a framework that comprehensively addresses the full operational lifecycle from risk control to emergency response. By implementing intelligent cross-system data integration and leveraging DeepSeek's advanced analytical capabilities, the solution achieves significant operational efficiencies - reducing manual intervention while accelerating risk verification and response times. Operational data demonstrates the system's proven effectiveness, currently delivering annualized cost savings exceeding RMB 3.6 million through optimized resource allocation and incident prevention measures.
Co-author/s:
Zixiao Chen, Senior Engineer, PetroChina.
Liwen Tan, Senior Engineer, PetroChina.
Jingdong Chen, Senior Engineer, PetroChina.
Julian von Gramatzki
Chair
Executive Vice President Process Technology
TÜV NORD Systems GmbH & Co. KG
Germany
Brima M Baluwa Koroma
Vice Chair
Director General
National Petroleum Regulatory Authority
Sierra Leone
Qingshan Feng
Vice Chair
General Manager, Production Department
China Oil & Gas Pipeline Network Corporation
China
Leak detection in oil and gas pipelines remains a critical challenge for operators such as Saudi Aramco, where undetected leaks can escalate into severe safety, environmental, and economic consequences. Conventional methods—including Computational Pipeline Monitoring (CPM), Real-Time Transient Modeling (RTTM), and Negative Pressure Wave (NPW) detection—are widely deployed but suffer from false alarms, sensitivity to operational transients, and limited adaptability. The novelty of this work lies in proposing a unified reflective middleware that fuses CPM, RTTM, and NPW within a single MAPE-K (Monitor–Analyze–Plan–Execute–Knowledge) loop, augmented by lightweight AI modules. Unlike prior studies that treat each method in isolation, the middleware integrates all three into a composite residual test, enhanced by AI-based adaptive thresholding, bias correction of RTTM predictions, and a priority-aware risk index that accounts for critical infrastructure and urban safety zones. This hybrid Physics + AI design preserves the transparency and regulatory trust of physics-based models while introducing adaptability and risk-awareness absent in current systems. The architecture also goes beyond anomaly detection by including an execution layer that issues automated valve closures, compressor throttling, and publish/subscribe alerts via MQTT. Compliance with API RP 1130, API RP 1175, and relevant Saudi Aramco Engineering Standards (SAES) is explicitly addressed, ensuring operational relevance. Simulation studies on representative crude oil and natural gas pipelines demonstrate significantly improved Probability of Detection (POD), reduced False Alarm Rate (FAR), and shorter Mean Time to Detect (MTTD) compared to CPM- or RTTM-only baselines. This integration of physics-based rigor, AI adaptability, and standards compliance defines a new class of reflective middleware for pipeline integrity management, designed specifically for the scale and operational requirements of Aramco’s oil and gas network.
Energy storage technologies are evolving rapidly, driven by the need for efficient and sustainable solutions. Large-scale underground energy storage solutions are pivotal for managing energy resources efficiently, especially as we transit towards renewable resources and low carbon emitting solutions. With increase in global concerns towards changes in patterns of energy usage; from hydrocarbons to green energy, mass scale storage requirements are also adding up challenges to create safe and secured repositories. Underground storage solutions for various type of liquids and gases, varies as per their storage states under ambient temperature conditions. Underground storage solutions are tailored for hydrocarbons and other gaseous products as per their behaviour under storage temperature conditions as it influences the design and engineering aspects of storage systems. Thus, selection of a mass scale storage solution and its functionality beneath the surface depends on many factors such as temperature, pressure, volume, and the chemical properties of the substance.
Underground mass storage solutions can be achieved in form of unlined rock caverns and lined rock cavern systems. Unlined rock caverns are sealed within the rock cavity under the umbrella of outside static water pressure head, for liquids and gases that can be stored in liquid state under ambient temperature conditions. However, technical requirements; depth, overburden and storage chamber design can vary depending upon whether the stored product is crude oil or LPG. High pressurised compressed gases; Natural Gas, Hydrogen and other similar products can also be stored in underground rock caverns; however, the system requirement changes to a more innovative emerging technique of lined rock caverns. This storage system ensures storability of compressed gases at shallower depths, providing high deliverability and turnover rates, underscoring its adaptability to diverse geological conditions along with offering flexibility for future expansion.
This paper presents a case study focused on Lined Rock Cavern (LRC) technology to assess structural performance and stability of underground storage system for varying pressure requirements of up to 200 bars. The findings from the study highlights vital requirements to be considered for carrying out design and engineering of lined rock caverns structures along with associated risk and challenges. Numerical analysis studies demonstrate that internal pressure significantly influences deformation and stress distributions of rock mass and associated concrete lining structure .Technical findings summarised as part of this study critically analyses feasibility of these storage systems for storing compressed gases for pressures varying from 50 to 200 bars.
Underground mass storage solutions can be achieved in form of unlined rock caverns and lined rock cavern systems. Unlined rock caverns are sealed within the rock cavity under the umbrella of outside static water pressure head, for liquids and gases that can be stored in liquid state under ambient temperature conditions. However, technical requirements; depth, overburden and storage chamber design can vary depending upon whether the stored product is crude oil or LPG. High pressurised compressed gases; Natural Gas, Hydrogen and other similar products can also be stored in underground rock caverns; however, the system requirement changes to a more innovative emerging technique of lined rock caverns. This storage system ensures storability of compressed gases at shallower depths, providing high deliverability and turnover rates, underscoring its adaptability to diverse geological conditions along with offering flexibility for future expansion.
This paper presents a case study focused on Lined Rock Cavern (LRC) technology to assess structural performance and stability of underground storage system for varying pressure requirements of up to 200 bars. The findings from the study highlights vital requirements to be considered for carrying out design and engineering of lined rock caverns structures along with associated risk and challenges. Numerical analysis studies demonstrate that internal pressure significantly influences deformation and stress distributions of rock mass and associated concrete lining structure .Technical findings summarised as part of this study critically analyses feasibility of these storage systems for storing compressed gases for pressures varying from 50 to 200 bars.
Pipeline safety serves as a foundational element in ensuring the operational integrity and security of energy infrastructure. Due to the factors, such as geological instability, material fatigue, and unauthorized third-party encroachment, persistent challenges continue to impede real-time risk detection and coordinated emergency response in pipeline systems. Operators now leverage advanced monitoring technologies such as distributed fiber optic sensing (DFOS), unmanned aerial systems (UAS), and computer vision-enabled cameras (CVCs) to achieve holistic perimeter threat assessment. These solutions have driven a 94.4% decrease in pipeline failure rates, with incident frequency dropping from 1.78 (2015) to 0.1 (2024) per 1,000 km-year.
Despite these technological improvements, significant issues persist. Interoperability constraints between monitoring systems have resulted in disparate data ecosystems that hinder unified risk management. Specifically, alarms generated through DFOS lack automated correlation with UAS and CVC data streams, requiring human operators to manually reconcile multi-system verification. Furthermore, post-detection processes dependent on manual data aggregation and decision-making introduces substantial response delays during emergency operations, potentially unsatisfying the requirement of 5-minute emergency action prescribed in API RP 1174:2020 for pipeline leak containment.
The past decade has seen unprecedented advances in artificial intelligence, marked by developments in large language models (LLMs) such as ChatGPT and DeepSeek. These innovations have fundamentally redefined operational paradigms in big data analysis. To address issues mentioned above, a DeepSeek-based intelligent agent was developed for pipeline risk management, enabling intelligent multi-system coordination and decision support across pipeline safety operations. This pioneering architecture positions the AI agent as a cognitive command hub, achieving seamless external system interoperability through industry-standard API protocols while enabling bidirectional data/control flows.
The system architecture further incorporates a AI-powered alarm risk verification engine, which integrates multi-source alert feeds with rule sets and historical incident repositories. This risk verification module autonomously executes various system tasks, such as drone takeoff or camera rotation, and generates priority rankings.
The framework also culminates in an intelligent emergency response knowledge base that synthesizes real-time risk analysis with operational metadata, including pipeline specifications and High-Consequence Area (HCA) geospatial parameters. This cognitive agent autonomously executes: risk stratification through machine learning classifiers, resource allocation optimization via constraint-based algorithms, activation of emergency plans, and automated notification workflows—delivering decision support throughout the emergency management lifecycle.
This technological innovation introduces pipeline risk management into a framework that comprehensively addresses the full operational lifecycle from risk control to emergency response. By implementing intelligent cross-system data integration and leveraging DeepSeek's advanced analytical capabilities, the solution achieves significant operational efficiencies - reducing manual intervention while accelerating risk verification and response times. Operational data demonstrates the system's proven effectiveness, currently delivering annualized cost savings exceeding RMB 3.6 million through optimized resource allocation and incident prevention measures.
Co-author/s:
Zixiao Chen, Senior Engineer, PetroChina.
Liwen Tan, Senior Engineer, PetroChina.
Jingdong Chen, Senior Engineer, PetroChina.
Despite these technological improvements, significant issues persist. Interoperability constraints between monitoring systems have resulted in disparate data ecosystems that hinder unified risk management. Specifically, alarms generated through DFOS lack automated correlation with UAS and CVC data streams, requiring human operators to manually reconcile multi-system verification. Furthermore, post-detection processes dependent on manual data aggregation and decision-making introduces substantial response delays during emergency operations, potentially unsatisfying the requirement of 5-minute emergency action prescribed in API RP 1174:2020 for pipeline leak containment.
The past decade has seen unprecedented advances in artificial intelligence, marked by developments in large language models (LLMs) such as ChatGPT and DeepSeek. These innovations have fundamentally redefined operational paradigms in big data analysis. To address issues mentioned above, a DeepSeek-based intelligent agent was developed for pipeline risk management, enabling intelligent multi-system coordination and decision support across pipeline safety operations. This pioneering architecture positions the AI agent as a cognitive command hub, achieving seamless external system interoperability through industry-standard API protocols while enabling bidirectional data/control flows.
The system architecture further incorporates a AI-powered alarm risk verification engine, which integrates multi-source alert feeds with rule sets and historical incident repositories. This risk verification module autonomously executes various system tasks, such as drone takeoff or camera rotation, and generates priority rankings.
The framework also culminates in an intelligent emergency response knowledge base that synthesizes real-time risk analysis with operational metadata, including pipeline specifications and High-Consequence Area (HCA) geospatial parameters. This cognitive agent autonomously executes: risk stratification through machine learning classifiers, resource allocation optimization via constraint-based algorithms, activation of emergency plans, and automated notification workflows—delivering decision support throughout the emergency management lifecycle.
This technological innovation introduces pipeline risk management into a framework that comprehensively addresses the full operational lifecycle from risk control to emergency response. By implementing intelligent cross-system data integration and leveraging DeepSeek's advanced analytical capabilities, the solution achieves significant operational efficiencies - reducing manual intervention while accelerating risk verification and response times. Operational data demonstrates the system's proven effectiveness, currently delivering annualized cost savings exceeding RMB 3.6 million through optimized resource allocation and incident prevention measures.
Co-author/s:
Zixiao Chen, Senior Engineer, PetroChina.
Liwen Tan, Senior Engineer, PetroChina.
Jingdong Chen, Senior Engineer, PetroChina.
