David Cameron

• Cameron, David B.; Slaughter, Laura Ann & Karlsen-Davies, Nilla (2024). Building a broad-based collaborative partner programme in digital technologies: The dScience Partner Programme. In Meerman, Arno (Eds.), Academic and Practitioner Proceedings of the 2024 UIIN Conference: Challenges and solutions for fostering entrepreneurial universities and collaborative innovation. UIIN. ISSN 9789491901669. p. 36¨C47. Full text in Research Archive
• Cameron, David B.; Otten, Wilhelm; Temmen, Heiner; Hole, Monica & Tolksdorf, Gregor (2024). DEXPI process: Standardizing interoperable information for process design and analysis. Computers and Chemical Engineering. ISSN 0098-1354. 182. doi: 10.1016/j.compchemeng.2023.108564. Full text in Research Archive
• Kamburjan, Eduard; Klungre, Vidar Norstein; Tapia Tarifa, Silvia Lizeth; Schlatte, Rudolf; Giese, Martin & Cameron, David B. [Show all 7 contributors for this article] (2023). Emerging Challenges in Compositionality and Correctness for Digital Twins. CEUR Workshop Proceedings. ISSN 1613-0073. 3507. Full text in Research Archive
• Qu, Yuanwei; Zhou, Baifan; Waaler, Arild Torolv S?etorp & Cameron, David B. (2023). Real-Time Event Detection with Random Forests and Temporal Convolutional Networks for More Sustainable Petroleum Industry. In Liu, Fenrong; Sadanandan, Arun Anand; Pham, Duc Nghia; Mursanto, Petrus & Lukose, Dickson (Ed.), 20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023 (PRICAI 2023): Trends in Artificial Intelligence . Springer. ISSN 978-981-99-7024-7. p. 466¨C473. doi: 10.1007/978-981-99-7025-4_41. Full text in Research Archive
• Calhau, Rodrigo Fernandes; Sales, Tiago Prince; Oliveira, ?talo; Kokkula, Satyanarayana (Satya); Pires, Lu¨ªs Ferreira & Cameron, David B. [Show all 8 contributors for this article] (2023). A System Core Ontology for Capability Emergence Modeling. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 14367, p. 3¨C20. doi: 10.1007/978-3-031-46587-1_1. Show summary

  To properly understand organizational adaptation and innovation, it is critical to understand the emergence phenomenon, i.e., how the capabilities of a system emerge after changes. However, for this, we should be able to explain systems, their structure, behavior, and capabilities. In pursuit of an understanding of the emergence phenomenon and the nature of those new kinds of systems in organizations, we propose a well-founded system core ontology based on the Unified Foundational Ontology. The ontology is also grounded in system science definitions and disposition theories. For a more integrated explanation of emergence, the proposed ontology considers distinct perspectives of a system, such as its composition, structure, properties, and functions. In the end, we discuss the applications and implications of the proposed ontology on the enterprise architecture area and emergence modeling.

• Calhau, Rodrigo Fernandes; Kokkula, Satyanarayana (Satya); Cameron, David B.; Guizzardi, Giancarlo & Almeida, Jo?o Paulo A. (2023). Modeling Competence Framework Elements with an Ontology-based Approach. IEEE Conference on Business Informatics (CBI). ISSN 2378-1963. 25, p. 17¨C26. doi: 10.1109/CBI58679.2023.10187498. Show summary

  Organizations are required to pay constant attention to human resource development in order to prosper. Competence frameworks have received increased attention in this context, as the availability of qualified people with the right combination of competences establishes itself as a major issue for organizational performance. This paper investigates the modeling of competence frameworks in Enterprise Architecture: (i) we identify a key set of competence-related concepts found in competence frameworks; (ii) analyze them using the Unified Foundational Ontology to build a reference Competence Ontology, and, then; (iii) propose well-founded representation patterns for competence modeling in the ArchiMate EA language, discussing how these patterns can be embedded in enterprise competence-based practices.

• Korotkova, Nataliia; Benders, Josephus Gerardus; Mikalef, Patrik & Cameron, David (2023). Maneuvering between skepticism and optimism about hyped technologies: Building trust in digital twins. Information & Management. ISSN 0378-7206. 60(4). doi: 10.1016/j.im.2023.103787. Full text in Research Archive
• Cameron, David B.; Waaler, Arild Torolv S?etorp; Fj?sna, Erlend; Hole, Monica & Psarommatis Giannakopoulos, Foivos (2022). A semantic systems engineering framework for zero-defect engineering and operations in the continuous process industries. Frontiers in Manufacturing Technology. ISSN 2813-0359. 2. doi: 10.3389/fmtec.2022.945717. Full text in Research Archive
• Ghadrdan, Maryam & Cameron, David B. (2022). An Overview of the Evolution of Oil and Gas 4.0. In Sharifzadeh, Mahdi (Eds.), Industry 4.0 Vision for Energy and Materials: Enabling Technologies and Case Studies. John Wiley & Sons. ISSN 9781119695936. p. 241¨C268. doi: 10.1002/9781119695868.ch9. Show summary

  The world's largest economies are already acting on strategies toward Industry 4.0 (4IR). Germany is today's leading in the manufacturing and process industry with the Platform Industry 4.0, gathering government, scientific community, industry, and trade unions working toward a common vision. Global collaboration and alliances ensure a common direction toward standardization. This chapter provides a brief overview of application of Industry 4.0 methods and standards in the oil and gas industry, with examples from the energy company Equinor.

• Cameron, David; Skogvang, Arnljot; Fekete, Mihaly; Martinsson, Henrik; Strand, Morten & Castro, Marcel [Show all 10 contributors for this article] (2022). The Digital Design Basis. Demonstrating a framework to reduce costs and improve quality in early©\phase design. Digital Chemical Engineering. ISSN 2772-5081. 2. doi: 10.1016/j.dche.2022.100015. Full text in Research Archive Show summary

  As a joint initiative, Aker BP, Lundin-Norway, Aker Solutions, TechnipFMC, Aibel and Aize, together with Sirius (the oil and gas research centre at the University of Oslo) have developed and demonstrated a common digital model representation of the information in early-phase design bases for oil & gas field developemnts. The scope of the project was to develop a proof of concept for a Digital Design Basis that supports data-centric rather than document-based engineering. The project established a standards-based data model that holds data about both the design basis and functional requirements decided by an operator. This model that can be implemented in any relevant software tools in a concept study, to ensure that information shared between operators and EPC vendors, with their different software tools, have the same meaning and understanding. The model is based on a common digitalized language for communication along the field development supply chain.

• Kharlamov, Evgeny; Martin-Recuerda, Francisco; Perry, Brandon; Cameron, David B.; Fjellheim, Roar Arne & Waaler, Arild Torolv S?etorp (2019). Towards Semantically Enhanced Digital Twins. IEEE International Conference on Big Data (Big Data). ISSN 2639-1589. p. 4189¨C4193. doi: 10.1109/BigData.2018.8622503.
• Cameron, David B. & ?sterlie, Thomas (2018). Sirius: Collaboration across the digital divides in the oil and gas supply chain. In Meerman, Arno (Eds.), Practitioners Proceedings of the 2018 University-Industry Interaction Conference: Challenges and Solutions for Fostering Entrepreneurial Universities and Collaborative Innovation. University Industry Innovation Network. ISSN 9789491901331. p. 91¨C101. Show summary

  This paper describes how we have succeeded in setting up industry-university collaboration in SIRIUS, a centre for research-based innovation on digitalization in the oil and gas industry. Funded through the Norwegian Research Council's programmes for centres of excellence, it is comparable to what Champenois and Etzkowitz (2017) refer to as a hybrid autonomous organization. The centre consists of academic researchers from two of Norway's largest universities and the University of Oxford. The indus-try partners represent the ecosystem of companies in the oil and gas industry, with one major operator, a major oil service company, and a set of vendor companies who specialize in different aspects of digitali-zation. Centres for research-based innovation have two masters: they must deliver world class research for the universities and funding bodies and useful innovation for the partner companies. Drawing upon Gibbons et al.'s (1994) insight that knowledge is produced in industry-university collaborations only to the extent that the interests of all stakeholders are included, we therefore find that a key to succeeding in SIRIUS begins with explicitly acknowledging that research institutions and companies participate in the centre to further on-going activities on digitalization within their respective organizations. Another key to success is that we combine the strengths of the centre's networked organization with a formalized project qualification pipeline. This requires investment of intellectual resources in extensive prospecting for innovation and research ideas. We call this scoping. These then mature through standard stage-gate, with mature ideas resulting in collaborative piloting and innovation projects. This approach has been set in place and tried out for two years. It has generated a promising portfolio of industrially relevant project ideas, a first tranche of aligned doctoral projects and a clear view of what SIRIUS should deliver. We will use the SIRIUS¡¯ ongoing Geological Assistant project to illustrate how these two elements play together in practice. This project emerged from a series of separate scoping workshops between the cen-tre's academic researchers, a vendor company and an operating company. This project then moved through a process of feasibility assessment with all stakeholders. This process of developing understand-ing and securing corporate commitment has resulted in an innovation project that is now started. Using the feasibility study, the vendor representative and academic project leader worked with the networked organization to mobilize academics resources. This involved both formulating a vision for the innovation, as well as returning to the basic question: "what is in this for you?" The latter is necessary to secure inter-est and committed resources from the academic researchers.

• Cameron, David B.; Waaler, Arild & Komulainen, Tiina M. (2018). Oil and Gas digital twins after twenty years. How can they be made sustainable, maintainable and useful? Link?ping Electronic Conference Proceedings. ISSN 1650-3686. p. 9¨C16. doi: 10.3384/ecp181539. Show summary

  The digital twin offers a potentially powerful way of using simulation to support business and change the way industrial operations are done. The idea of the digital twin is not new but recent changes in information technology make implementation of digital twins a natural next step in the application of simulation technologies. Simulation practitioners will find that their models are increasingly embedded in complex systems that combine simulations with operational data to solve a business problem. However, the successful adoption of this approach is challenging. This paper asks the question: ¡°How can digital twins be made sustainable, maintainable and useful?¡±. We focus primarily on the development of twins in the oil and gas industry. Most academic work in this area has been done in the manufacturing industries. We review this literature and propose a simple model of digital twins. This allows us to identify challenges with current implementations and propose a research agenda that will allow future twins to be sustainable, maintainable and usable.

• Cameron, David B.; Skj?veland, Martin G; Giese, Martin; Hovland, Dag; Waaler, Arild & Bj?rge, Eldar [Show all 7 contributors for this article] (2016). Optique: Simple, Oil & Gas-oriented access to big data in exploration, SPE Intelligent Energy Conference and Exhibition, Aberdeen, Scotland, UK, 6-8 September 2016 - Conference Proceedings. Society of Petroleum Engineers. ISSN 978-1-61399-459-7. doi: 10.2118/181111-MS.
• Cameron, David Bruce & Bogle, I. David. L. (2002). CAPE Tools for Off-Line Simulation, Design and Analysis. In Gani, Rafiqul (Eds.), Software Architectures and Tools for Computer-Aided Process Engineering. Elsevier. ISSN 0444508279. p. 373¨C392.
• Cameron, David B.; Paterson, William R. & Morton, William (1991). Framework for modelling of metallurgical processes. Mineral Processing and Extractive Metallurgy: Transactions of the Institutions of Mining and Metallurgy: Section C. ISSN 0371-9553. 100, p. C11¨CC20.

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• Cameron, David B.; Slaughter, Laura Ann & Karlsen-Davies, Nilla (2024). Building a broad-based collaborative partner programme in digital technologies: The dScience Partner Programme.
• Cameron, David (2023). Developments in the DEXPI and IMF that support digital engineering .
• Fricke, Armin; Tolksdorf, Gregor; Cameron, David & Oliveira de Medeiros, Daniel (2023). A concept for the implementation of DEXPI+ in DWSIM.
• Fricke, Armin; Cameron, David B.; Tolksdorf, Gregor & Oliveira de Medeiros, D (2023). A concept for the implementation of DEXPI+ in DWSIM.
• Qu, Yuanwei; Zheng, Zhuoxun; Zhou, Baifan; Zhou, Yan; Santos, Nicolau & Savkovic, Ognjen [Show all 8 contributors for this article] (2023). Towards Data Integrity Verification for More Sustainable Petroleum Industry.
• Cameron, David (2022). Introduction to the READI Information Modelling Framework.
• Aviso, Kathleen; Zhang, Dongda; Cameron, David B. & Xuan, Jin (2022). Editorial: Machine learning for chemical processes. Digital Chemical Engineering. ISSN 2772-5081. 5. doi: 10.1016/j.dche.2022.100062.
• Cameron, David B. (2021). Digital Twins for Science. The Science of Digital Twins.
• Cameron, David (2020). READI, DSYNE and digital requirements.
• Cameron, David B. (2020). Boundary spanning in the interfaces between academic computer science, the IT industry and the extractive industries.
• Cameron, David B. (2020). Digital Twins. What are they? How can they be used? Show summary

  The term "digital twin" has become a buzzword during the last few years. Gartner group's annual review of such words has placed the term at the top of the hype curve in both 2018 and 2019. Many suppliers offer systems that promise to be the twin of a system, town, facility or person. This person. This lecture aims to give a responsible overview of what a digital twin is and how it can be used in industrial practice. Starting in the petroleum industry, we see how research in computer science can contribute to the implementation of useful, cost-effective and scalable digital twins.

• Yu, Ingrid Chieh; Cameron, David B.; V?lstad, Ann & Larsen, ?shild Hanne (2020). Industrial mentoring for junior researchers: An enabler for personal development and innovation . Show summary

  The SIRIUS Centre for Research-Based Innovation, based at the University of Oslo, is a collaborative organization that addresses challenges of digitalisation in, and beyond, the oil and gas industry. It aims to produce innovation that solves operational challenges in industry through application of high-quality com-puter science research. The centre brings together academics from the three universities, two research in-stitutes, large companies in the oil and gas sector, IT vendors, both global and local, and a group of spe-cialized companies. It is a complex task to generate industrial innovation and excellent research simultaneously. The centre brings together a diverse group of participants, with widely differing backgrounds and motivations. Effort must be made to bridge the gaps between our academic researchers and their industrial counterparts. As part of this effort, SIRIUS ran a mentoring program for junior academics in 2017 and 2018. This paper describes the program, its results and practical experience that was derived from the program. Each of the authors brings their own perspective to the paper. The lead author is the deputy director of the centre. She defined the objectives, led and ran the mentoring program. The second author was the manager in the centre with responsibility for relationships with the centre¡¯s industrial partners. He was the sponsor of the program. The third author works for AFF, a consultancy that prepared the content of the program and facilitated the meetings in the program. The final author is one of the mentors. She is also a C-level executive in the energy company Equinor.

• Cameron, David B. (2019). SIRIUS: Innovation-driven research? Research-driven innovation? Or both together?
• Cameron, David B.; Waaler, Arild & Abel, Mara (2019). Digital Twins as a Platform For Artificial Intelligence in the Petroleum Supply Chain. Show summary

  The digital twin offers a potentially powerful way of using simulation to support business and change the way field development and petroleum operations are done. The concept is hyped, being at the very top of the Gartner hype curve. The idea of the digital twin is also not new in the petroleum and process industries. We have been building simulations and linking them to design and operational data for as long as computers have been used. However recent changes in information technology make implementation of digital twins a natural next step in the application of analytical methods of all types to problems in the petroleum supply chain. We argue that a well-structured digital twin is essential for the scalable, effective implementation of artificial intelligence. Digital twins are complex systems that combine simulations with design and operational data to solve a business problem. However, the successful adoption of this approach is challenging. We need to answer the question: ¡°How can digital twins be made sustainable, maintainable and useful?¡±. We focus here on the development of twins in the oil and gas industry. Most academic work in this area has been done in the manufacturing industries. We review this literature and propose a simple model of digital twins. This allows us to identify challenges with current implementations and propose a research agenda that will allow future twins to be sustainable, maintainable and usable. We believe that such a digital twin will need the development and use of a semantic backbone ¨C an application-independent description of the facility of system to be twinned. This back-bone consists of several elements. The first is an asset model that describes the facility, how it is built up and what it is made of. The second element is a set of requirements, that captures the purpose of the facility and tracks the compliance of the facility with these requirements. Third, we need to model the lifecycle of the facility, allowing for an asset model and its requirements to evolve from conceptual design, through engineering, procurement, construction & commissioning, to operation, maintenance & decommissioning. This structuring of data and information provides a secure, scalable and foundation for all types of AI application. This paper presents examples of each of these types of semantic model. We also describe our plans for developing a new best practice for digital twins in collaboration with leading oil companies.

• Cameron, David B. (2019). SIRIUS Digital Twins, Semantics and Petrobras.
• Cameron, David B.; Waaler, Arild; Skj?veland, Martin G; Gjerver, Anders & Hansen, Christian Mahesh (2019). The Whole Plant Digital Twin: What Can Semantic Technologies Contribute?
• Cameron, David B. (2019). Digital Twin Research ¨C University of Oslo . [Internet]. FutureDistributed.org. Show summary

  This week¡¯s show is all about the DIGITAL TWIN! I sat down with David Cameron, Coordinator at the Sirius Centre for Research-based Innovation, at the University of Oslo. This research lab focuses on the digitalisation of oil and gas operations and is (in my opinion) one of the most advanced research programs on infrastructure digital twins ¨C mainly because the oil and gas sector has been doing ¡®digital-twin¡¯-type applications for more than 20 years. In this episode, we¡¯ll be answering some big question like: What can we learn from the Oil and Gas sector who have been developing ¡®digital twins¡¯ for over 20 years? What are some of the current research challenges being tackled by the Sirius Centre? How is the centre collaborating with industrial partners to put this expertise into practice?

• Cameron, David B.; Waaler, Arild; Tungland, Knut Sebastian & N?st, Elisabeth (2019). Using the Industry Collaboration Canvas in the Mid-Term Review. Experience in applying the framework in the oil and gas industry. Show summary

  The SIRIUS Centre for Scalable Data Access in the Oil and Gas Domain is funded by the Research Council of Norway and hosted by the University of Oslo. It pursues fundamental research that drives innovations related to digitalisation of the oil & gas industry. The centre consists of researchers from four institutions (University of Oslo, NTNU, Simula Research Centre and University of Oxford) and fourteen companies in the oil and gas supply chain. It is funded for eight years from 2015 and is now undergoing a mid-term review that assesses the centre¡¯s progress and determines whether it will be funded for its full term. To prepare for the review, it was necessary to have a structured discussion with each industrial partner. This was done to determine how successful the partnership had been to date, see if any changes needed to be made and sketch a plan for the last half of the centre¡¯s life. The assessment required a tool that was simple to use, easy to understand and flexible. We chose to use the Industry Collaboration Canvas of Fr?lund, Murray and Riedel (2018). This paper describes the process and results of this work, from the perspective of the centre management and two of the larger partner companies, Equinor and TechnipFMC. Two workers were trained in the use of the canvas in March 2018. The process was explained to the partners at the centre¡¯s general assembly and it was agreed that we would conduct these workshops. The workshops were then held in the second half of 2018. SIRIUS management visited each partner¡¯s site to hold the meeting. However, in some cases, videoconferencing had to be used due to the geographical factors. The use of the canvas was considered to be successful. Feedback from all participants was positive and the information gathered provided factual material for preparing the documents needed for the mid-term re-view. In all cases, we identified concrete actions that will improve the value of collaboration between the researchers and each industrial partner. The canvas identified gaps and potential problems for collaboration. In these cases we were able to agree on necessary corrective actions. The canvas is now shared between centre management and the partner and is a de-facto agreement on how the partnership will continue over the next 5 years. The canvas is an effective and powerful tool for assessing and planning interactions between researchers and industrial companies. It is simple to use, easy to understand and brings rigour and structure to the discussions around current and new collaborations. We are actively promoting the use of this tool in other parts of the University.

• Cameron, David B. & ?sterlie, Thomas (2018). Sirius: Collaboration across the digital divides in the oil and gas supply chain. Show summary

  This paper describes how we have succeeded in setting up industry-university collaboration in SIRIUS, a centre for research-based innovation on digitalization in the oil and gas industry. Funded through the Norwegian Research Council's programmes for centres of excellence, it is comparable to what Champenois and Etzkowitz (2017) refer to as a hybrid autonomous organization. The centre consists of academic researchers from two of Norway's largest universities and the University of Oxford. The indus-try partners represent the ecosystem of companies in the oil and gas industry, with one major operator, a major oil service company, and a set of vendor companies who specialize in different aspects of digitali-zation. Centres for research-based innovation have two masters: they must deliver world class research for the universities and funding bodies and useful innovation for the partner companies. Drawing upon Gibbons et al.'s (1994) insight that knowledge is produced in industry-university collaborations only to the extent that the interests of all stakeholders are included, we therefore find that a key to succeeding in SIRIUS begins with explicitly acknowledging that research institutions and companies participate in the centre to further on-going activities on digitalization within their respective organizations. Another key to success is that we combine the strengths of the centre's networked organization with a formalized project qualification pipeline. This requires investment of intellectual resources in extensive prospecting for innovation and research ideas. We call this scoping. These then mature through standard stage-gate, with mature ideas resulting in collaborative piloting and innovation projects. This approach has been set in place and tried out for two years. It has generated a promising portfolio of industrially relevant project ideas, a first tranche of aligned doctoral projects and a clear view of what SIRIUS should deliver. We will use the SIRIUS¡¯ ongoing Geological Assistant project to illustrate how these two elements play together in practice. This project emerged from a series of separate scoping workshops between the cen-tre's academic researchers, a vendor company and an operating company. This project then moved through a process of feasibility assessment with all stakeholders. This process of developing understand-ing and securing corporate commitment has resulted in an innovation project that is now started. Using the feasibility study, the vendor representative and academic project leader worked with the networked organization to mobilize academics resources. This involved both formulating a vision for the innovation, as well as returning to the basic question: "what is in this for you?" The latter is necessary to secure inter-est and committed resources from the academic researchers.

• Cameron, David B. (2018). Scalable, Useful and Maintainable Digital Twins: Cross-Sector Experience from the Oil and Gas Sector.
• Cameron, David B.; Waaler, Arild & Komulainen, Tiina M. (2018). Oil and Gas digital twins after twenty years. How can they be made sustainable, maintainable and useful? Show summary

  The digital twin offers a potentially powerful way of using simulation to support business and change the way industrial operations are done. The idea of the digital twin is not new but recent changes in information technology make implementation of digital twins a natural next step in the application of simulation technologies. Simulation practitioners will find that their models are increasingly embedded in complex systems that combine simulations with operational data to solve a business problem. However, the successful adoption of this approach is challenging. This paper asks the question: ¡°How can digital twins be made sustainable, maintainable and useful?¡±. We focus primarily on the development of twins in the oil and gas industry. Most academic work in this area has been done in the manufacturing industries. We review this literature and propose a simple model of digital twins. This allows us to identify challenges with current implementations and propose a research agenda that will allow future twins to be sustainable, maintainable and usable.

• Cameron, David B. (2018). Digitalization perspectives from the oil and gas domain.
• Cameron, David B.; Kluwer, Johan Wilhelm & Berli, Bj?rn (2017). Masterclass on Digital Engineering.
• Cameron, David B. (2017). Scalable data access is necessary for successful digitalisation.
• Cameron, David B. (2017). Scalable data access: Lower-cost higher-impact environmental compliance.
• Cameron, David Bruce; Waaler, Arild; Hovland, Dag & Skj?veland, Martin G (2017). Practical Knowledge Representation for Data Access to Subsurface Data: The Achievements and Potential of the Optique Platform.
• Cameron, David Bruce (2017). Successful big data projects.
• Cameron, David Bruce (2017). Getting the Dinosaurs to Dance: Innovation culture meets academia and the oil and gas industry .
• Cameron, David Bruce (2017). Digitalization in the Natural Resources Industry: A Challenge and an Opportunity.
• Cameron, David Bruce (2017). Digitalization: How can we mix the ¡±new oil¡± and the old oil? The role of IT research. Show summary

  This talk starts with a brief review of what digitalisation and what it means, using the models of Machines, Platforms and Crowds (Brynjolfsson, MIT), Industrie 4.0 and The World Economic Forum initiative in oil and gas digitalisation. We them give some cautions are then given about digitalisation: Machines are not magic, rubbish in results in rubbish out, the challenge of the data swamp, the danger of the data scientist as guru and the social nature of exploration and operations. We then describe how we are working in SIRIUS to bring computer science to bear on oil and gas digitalisation. (a) Bridging gaps in the digitalisation supply chain (end-users, vendors, academics) (b) Interdisciplinary research with computer science (c) Interdisciplinary research between computer science, engineering and geosciences. (d) The need for interpreters. We then conclude with three examples: (1) opening up exploration databases and national data repositories (2) using semantics to create paper-free engineering, procurement and construction and (3) applying formal methods to operational planning.

• Cameron, David B. (2016). Analytics and scalable data access: the future of industrial information technology.
• Cameron, David B. (2016). Industry 4.0 for the oil and gas sector. Research and Innovation challenges and mechanisms.
• Cameron, David B. (2016). Presentation of SIRIUS Centre.
• Cameron, David B.; Skj?veland, Martin G; Giese, Martin; Hovland, Dag; Waaler, Arild & Bj?rge, Eldar [Show all 7 contributors for this article] (2016). Optique: Simple, Oil & Gas-oriented access to big data in exploration.
• Cameron, David B. (2016). Masterclass on Big Data.
• Cameron, David Bruce (2014). Big Data in Exploration and Production: Silicon Snake-Oil, Magic Bullet, or Useful Tool?
• Enemark-Rasmussen, Rasmus; Cameron, David; Angelo, Per Bagge & Sin, G¨¹rkan (2012). A simulation based engineering method to support HAZOP studies. Computer-aided chemical engineering. ISSN 1570-7946. doi: 10.1016/B978-0-444-59506-5.50085-7.
• Komulainen, Tiina; Enemark-Rasmussen, Rasmus; Sin, G¨¹rkan; Fletcher, John P. & Cameron, David (2012). Experiences on dynamic simulation software in chemical engineering education. Education for Chemical Engineers. ISSN 1749-7728. doi: 10.1016/j.ece.2012.07.003.
• Cameron, David B.; Falk, Kristin & Kokkula, Satyanarayana (Satya) (2021). Towards Digital Requirements for Transformation in the Natural Resources Industries White Paper from the DSYNE Network Workshop, 9th-10th February 2021 . SIRIUS Centre for Research-Based Innovation. Full text in Research Archive Show summary

  The DSYNE network is a collaboration of universities and companies in Norway, Brazil, and the United States. Its aim is to promote adoption and use of digitally represented requirements in engineering and system development. The participants in this network believe that by using digital requirements, the natural resources industries can reduce the capital cost of assets and improve operational performance. The state-of-the-art in building capital facilities is a process where operating companies, engineering firms and equipment suppliers exchange paper or unstructured documents containing the requirements and their verification methods. This costs money and reduces the quality of engineering. Current research has shown that it is possible to use semantic technologies to describe requirements in a truly digital way, so that they can be shared in databases and used for automated reasoning and validation. This can change the way requirements are managed and transmitted in the natural resources industries. However, if this is to happen, we need to (1) build international awareness of these methods, (2) build knowledge and skills in this technology and methods, both in computer scientists and engineers and (3) bring together domain knowledge in engineering with computer science to build a foundation for creating useable, scalable tools for managing requirements. The DSYNE project started in autumn 2020. It will run until 2024. Originally, the project planned for face-to-face workshops. However, due to the pandemic, the first workshop has been replaced by shorter workshops and seminars that use videoconferencing. The first of these workshops was held on 9th ¨C 10th of February 2021.

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