P. Bernadi, TotalEnergies, Le Havre, France; G. COTTET, TotalEnergies, Lyon, France; and C. CROW, AVEVA, Houston, Texas (U.S.); and C. HARCLERODE, AF Expert Consulting LLC, Houston, Texas (U.S.)
TotalEnergies has established a comprehensive global framework for industrial operational data management that emphasizes enterprise standardization, scalability and governance. This framework represents a sophisticated and precise solution for assessing industrial asset health, energy consumption and greenhouse gas (GHG) emissions, wholistically, worldwide. The cornerstone of this solution is the creation of configurable smart asset templates—developed by a team of subject matter experts (SMEs) in assets and industrial operational data management—that feature centralized development and management of asset base templated with global distribution and enabling localization by local SMEs. These smart asset templates are used to create a global “operational digital twin” with associated self-serve hierarchical rollups, visualization and reporting that power the company’s operational excellence management system based on solid energy, asset and GHG baselines, key performance indicators (KPIs) and continuous improvement work processes.
Initially, this global energy and GHG solution was implemented in the company’s refining and petrochemicals division. It showcased a pioneering approach to quantifying GHG emissions from combustion sources at the molecular level through the application at the asset template level stoichiometry and thermodynamics, forming a crucial component of real-time GHG assessment for asset, process units and sites on a global scale. This foundational capability was subsequently enhanced by accurately determining and allocating GHG emissions from steam producers to steam consumers.
The initial framework for industrial operational data management and the associated smart asset templates were later expanded to encompass the company’s global upstream operations. This expansion resulted in the creation of an integrated global solution for energy, asset and GHG performance management, positioning the company as a leader in measuring, forecasting, and managing its global asset performance, energy and GHG emissions while confidently tracking progress toward stated sustainability objectives.
The foundation: Solid industrial operational data management. When the authors’ multinational integrated energy company changed its name from Total to TotalEnergies in 2021, it signaled its commitment to diversifying its energy production and taking a holistic approach to sustainability. To fully realize its goals of establishing itself as a world leader in the energy transition and achieving carbon neutrality by 2050, the company needed to address its operational data management strategy and capabilities.
The company had to establish a digital framework that would not only break down its business silos, capture and standardize operational data calculations, KPIs and analytics but also scale with a governed, standards-based industrial information management infrastructure. Its teams needed to figure out a way to develop global consistency in the way asset performance, energy and GHGs were determined, establish solid baselines, track GHG emissions and, most importantly, operationalize the information in its operational excellence management system with accountability and work process integration. Additionally, the company simultaneously—and cost effectively—needed to effectively monitor new renewable energy sources and the impact on reducing emissions. As the International Energy Agency’s (IEA’s) “Strategies for affordable and fair clean energy transitions”1 notes, cost-effectiveness is a central component of a sustainable green energy transition.
Historically, GHG emissions calculations relied heavily on lagging data, proxies and extensive manual editing and reviews, which often resulted in delayed and less precise assessments, especially globally. Recognizing the need for a more dynamic and accurate approach, TotalEnergies has leveraged modern digital capabilities to transform its energy, asset performance and GHG determination, monitoring and forecasting practices.
The foundation for the company’s approach has been the co-author’s company’s integrated operations data management systema and embedded modeling toolb, with which it has established a robust data contextualization and governance infrastructure. Developers and process engineers began by elaborating a clear set of priorities for data management, data governance and data security. For data management, they first established best practices governing both the asset framework of the operations data management systema and web-based display and dashboard building toolc.
They produced a set of checklists to control development, requested forms to enable the creation of new units of measurement, and drew up wikis to empower the entire enterprise to take part in this project according to best practices. In the next phase of data governance, teams articulated a clear global asset framework hierarchy and also focused on modeling toolb asset framework objects developed by a core team of SMEs—400 master asset base templates that included display and dashboardb templates—that could be distributed to global sites and augmented with localized additions to the corporate base templates by local SMEs.
By using a contextual data modeling layerb (PI AF), the authors’ company empowered its onsite teams to have autonomy—full control of site objects—and adapt the templates to its needs on the individual sites. By allowing for site-specific derivation from a master object, the company maintains a global standardization of approach while enabling flexibility and ownership among onsite teams. FIG. 1 is an illustration of this approach.
With these smart asset templates, the company was able to digitalize its equipment and process streams (e.g., steam and unit feeds), creating industrial digital twins that could be viewed on 160 master dashboardc displays. Using a PI AFb-to-PI AF manager and a PI AF security manager, the business has been able to ensure consistency and security globally. All teams can use the same templates on the data management systema, all managed by the same services. By establishing a centralized system where teams across the enterprise could use the same templates for their onsite needs, and reuse them each day, the company had increased its overall efficiency in the present and created the infrastructure for an even more efficient future.
Through its standardization of templates, the company is paving the way toward new operational efficiencies and opportunities for sustainable growth. From here, the authors’ company just had to accelerate its deployment to other sites, begin to expand its use cases and discover the additional value of its operational data, when applied at the molecular level for combustion sources such as heaters, flares and furnaces. These combustion sources account for > 80% of GHG emissions in the company’s refining and chemicals division.
From GHG molecular monitoring to emissions reduction. Although the library established a comprehensive operational data management system with smart asset templates, designed and evolved by SMEs and ensuring robust governance and data integrity, the company still faced challenges in monitoring at the GHG molecular level to realize emissions reductions. Even as they interrogated the same information, different teams were involved with energy, emissions and performance, and each team had a different perspective on the data. For example, the process team concentrated on process optimization, while the environment team focused on GHG emissions, the asset reliability team focused on asset reliability, and the energy management performance team focused on energy efficiency. This meant that teams could fail to share work, duplicate each other’s work and end up producing inconsistent results. The company needed a method that would facilitate collaboration while enabling teams to carry out the complex, standardized calculations necessary for measuring energy efficiency, asset performance and monitoring emissions.
It broke down these silos by turning to a deep structure to organize its templates and assets. It used generic templatesb,c to track gas at the molecular level across the molecules’ lifecycles. It began tracking the molecule in the combustion process using fuel gas composition and stoichiometry. From there, teams can track the molecules through the flowrate of combustible streams, in the thermal efficiency of combustible equipment such as a heater or a boiler, into emissions of chimneys and all the way to the emissions of the refinery and chemical plants, division, country and globally. FIG. 2 illustrates this approach to determine GHG emissions for combustion sources.
By beginning with accurate, precise information about the molecule per nominal cubic meter (Nm3) of gas, teams can now track energy as well as emissions simultaneously, layering optimization use cases on top of monitoring use cases, and then compare. By measuring the actual processes against the online operational digital twin where they provide optimized target values for the combustion processes, SMEs now have the context-rich data to adjust and optimize these plants in near real time. This approach has empowered the company to treat GHG molecules and emissions streams as valuable assets, each with specific attributes and calculations derived from stoichiometry and thermodynamics principles augmented with financial data.
By implementing real-time monitoring with a frequency of one minute across four plants and 45 pieces of combustion equipment, the company has achieved a level of accuracy and transparency in GHG emissions reporting that is unprecedented in the industry. Using the operations data management systema, the entire team can access the asset performance necessary to make intelligent decisions. This capability enables frontline operators, process engineers, SMEs and line managers to gain immediate insights into their emissions data, fostering an environment where informed decisions can be made to proactively reduce GHG emissions. By tracking the molecular movement across its lifecycle, teams can not only ensure accurate monitoring and compliance with emissions regulations, but can also promise to increase future savings, as this energy optimization can save hundreds of thousands of euros by reducing energy, and GHG emissions while increasing asset performance and reliability.2,3
Reallocating GHG emissions from steam producers to steam consumers. To improve the accuracy and management of steam consumers, the authors’ company has extended its innovative GHG determination method to steam production with allocations to steam consumers. Traditionally, steam-produced and associated GHG emissions do not get reallocated to steam consumers due to the complexity of typical steam distributions with associated steam pressure distribution systems. However, the company took on this challenge by extending the use of its modeling toolb to determine steam producer GHG emissions and the modeling toolb network of calculations that distributes GHG emissions to the steam consumers proportionally and adding to the combustion GHG emissions, increasing the accuracy of steam consumer GHG emissions calculations, KPIs and baselines.
Building a carbon dioxide (CO2) intensity index for steam networks. Expanding upon its successful molecular monitoring approach, the company implemented this method across 11 steam networks, 54 sub-networks, 16 assets (including boilers, turbo alternators and steam crackers), and > 530 product and steam streams. Utilizing 750 modeling toolb elements, 300 modeling toolb element references, and 1,200 modeling toolb analyses, the company digitalized its steam networks through real-time calculations using the operations data management systema. By leveraging KPIs for steam and electricity producers, engineers could differentiate the CO2 intensity of various production sources, allowing them to trace emissions as they moved across the network.
With this contextual data modeling layerb infrastructure, the company could now dynamically monitor the CO2 intensity of its steam networks and processes and make informed decisions to optimize efficiency. The approach enabled teams to detect operational inefficiencies, track performance variations and implement corrective measures to maintain optimal system performance.4
Enhancing efficiency and sustainability. With success in the company’s refining and chemicals division’s use of the data management systema to determine GHG emissions, the framework was extended to the company’s upstream division (FIGS. 3 and 4).
Takeaways. TotalEnergies is committed to achieving its 2050 GHG emissions targets by revolutionizing the way energy, asset performance and emissions are measured, managed and operationalized through advanced digital solutions. This approach marks a significant step forward in the energy sector’s pursuit of sustainability and environmental responsibility.
At the core of this transformation is an enhanced operational data management systema that ensures global consistency and governance. By leveraging real-time GHG intelligence at the molecular level—along with precise determination and allocation of steam production emissions to downstream consumers—the authors’ company is establishing a new benchmark for accuracy, transparency and continuous emissions reduction. This comprehensive framework not only optimizes emissions management but also reinforces the company’s leadership in driving the energy transition. HP
NOTES
AVEVA™ PI System™
AVEVA’s PI Asset Framework
AVEVA™ PI Vision™
LITERATURE CITED
International Energy Agency (IEA), “Strategies for an affordable and fair clean energy transition,” June 2024, online: https://iea.blob.core.windows.net/assets/86f2ba8c-f44b-494a-95cc-e75863cebf95/StrategiesforAffordableandFairCleanEnergyTransitions.pdf
TotalEnergies, “Two talks: (1) Data governance and (2) Digitization of molecules for energy efficiency and emissions monitoring,” AVEVA World Amsterdam, 2022, online: https://resources.osisoft.com/presentations/two-talks---1--data-governance---2---digitization-of-molecules-for-energy-efficiency-and-emissions-monitoring---totalenergies/
TotalEnergies, “Reallocation of the CO2 emitted by steam producers to the steam consumers: Management of the steam networks with PI AF,” AVEVA World Paris, 2024, online: https://www.aveva.com/en/perspectives/presentations/2024/totalenergies---reallocation-of-the-co2-emitted-by-steam-producers-to-the-steam-consumers---management-of-the-steam-networks-with-pi-af/
TotalEnergies, “Leveraging real-time operational data to reduce greenhouse gas emissions,” AVEVA World San Francisco, 2023, online: https://resources.osisoft.com/presentations/totalenergies--leveraging-real-time-operational-data-to-reduce-greenhouse-gas-emission/
PIERRE BERNADI brings more than 25 yrs of experience at TotalEnergies, primarily within the Refining and Chemicals Branch (Downstream). Throughout his career, Dr. Bernadi has held a variety of roles including Research Engineer, Process Modeling Engineer, Project Studies Lead, Refinery Senior Process Engineer and Process Group Leader. He presently serves as Technical Advisor for Advanced Monitoring and Modeling, where he focuses on leveraging data and analytics to optimize industrial performance. He holds an MS degree in chemistry and a PhD in heterogeneous catalysis.
GAËL COTTET is an industrial IT engineer and OSIsoft PI System expert with 19 yrs of experience delivering PI projects across diverse sectors, including oil and gas, mining, wind energy and manufacturing. His global project experience spans Canada, Europe, Africa and Asia, where he has taken on multiple roles such as Project Manager, Pre-Sales Consultant, System Architect and Industrial IT Systems Integrator. He also specializes in application design (UX/UI), commissioning and training, bringing a comprehensive and hands-on approach to digital transformation in industrial environments.
CINDY CROW is a seasoned oil and gas professional with more than 42 yrs of industry experience. Her career spans a wide range of engineering and leadership roles at major companies including Chevron, ExxonMobil, Baker-Petrolite, Nalco and Schlumberger. She has held positions in engineering disciplines, marketing and executive marketing, alliance management, deepwater sales and technical sales, and has contributed to numerous engineering projects across the upstream sector. Currently serving as an Industry Principal at AVEVA, Cindy focuses on upstream oil and gas, helping customers assess and optimize their use of engineering, information, automation technologies, analytics and AI. She is passionate about developing strategies that drive measurable business value. Crow holds a BS degree in chemical engineering and an MBA in marketing and international business.
Craig Harclerode is CEO of AF Expert Consulting LLC. He has more than four decades of successful experience leading and leveraging digital technologies to deliver transformative business results. Harclerode brings a unique, seasoned and powerful perspective building on his roles at Amoco Oil, Honeywell IAC, Aspen Tech, and the last two decades at OSIsoft (now AVEVATM). He departed AVEVA in June 2025 and, with co-workers Ales Soudek and Curt Hertler, formed AF Expert Consulting, focused on “raising value to the power of AF– ValueAF”. Harclerode holds a BS degree in chemical engineering from Texas A&M, an MBA from Rice, and holds a PMP certification from the PMI. With more than 25 published articles and regular speaking engagements at conferences and events, he is recognized as a thought leader.
This article is published with contributions from Craig Harclerode, formerly of AVEVA.