David Cameron

• Qianhang Lyu, Baifan Zhou, Martin Georg Skjæveland, Eduard Kamburjan, Andreas Schüller & David B. Cameron [Show all 9 contributors for this article] (2026). A system information modelling method in the cross-disciplinary project collaboration context for industrial digitalisation. Scientific Reports. 16. doi: 10.1038/s41598-026-51522-x. Full text in Research Archive Show summary

  Industrial digitalisation for project management (PM) is a promising strategy for organising knowledge, skills, tools, and techniques to successfully complete projects. However, system information modelling (SIM), as a key approach that enables the digitalisation of industrial systems, is often applied separately without a PM context. This situation results in fragmentation of project information and hinders collaboration among stakeholders. Limited research has explored on integrating SIM technologies and PM principles. To bridge this gap, this paper proposes IMF-PM, a SIM methodology that integrates the Information Modelling Framework (IMF) with Project Management Body of Knowledge (PMBOK) standards for digitalisation in the context of cross-disciplinary project collaboration. IMF-PM provides an engineer-friendly, multi-aspect language that enables cross-disciplinary modelling within a unified framework, while also supporting Resource Description Framework (RDF) serialisation for digitalisation. We present an industrial case study of a gas cooling system, providing exploratory practical evidence of function-implementation consistency and non-destructive integration. The case study and comparative analysis provide exploratory evidence of the practical utility of IMF-PM for interdisciplinary coordination, traceability, and project information integration in cross-disciplinary industrial projects. This research provides a new paradigm for project information digitalisation in the industry.

• Harish Kumar Pakala, Bianca Wiesmayr, Christian Diedrich & David B. Cameron (2026). A DSL for Integrating Engineering Artifacts and Behavior into the Asset Administration Shell. Scitepress. ISSN 2184-4348. 1, p. 135–149. doi: 10.5220/0014634700004058. Full text in Research Archive Show summary

  Discrete and process automation (DPA) systems span multiple engineering domains. They also evolve across different stages of the system life cycle, which leads to semantic heterogeneity and challenges for model-based systems engineering (MBSE). The Asset Administration Shell (AAS) defines a generic meta-model that enables semantic annotation via external vocabularies for information representation. However, AAS primarily supports static, hierarchical representations and lacks dynamic and behavioral constructs needed to capture workflows, orchestration, and batch production strategies. In this regard, this paper introduces SAFSL, the Semantically Annotated Functional Specification Language, which extends the AAS meta-model with dynamic and behavioral abstractions, thus, supporting modular, reusable, and semantically enriched specifications. SAFSL enables assume-guarantee reasoning and provides a foundation for automatic code generation, thereby reducing errors and improving consistenc y in DPA system modeling. The approach is demonstrated through a batch process plant case study, highlighting a) semantic integration of DEXPI, the digital P&ID representation, with AAS submodel templates, b) functional specification of batch-phase operations, c) the need for integrating electrical CAD diagrams with AAS submodel templates, and d) potential connections between DEXPI and ECAD representations. These insights reveal open challenges in bridging semantic gaps across heterogeneous submodel templates while illustrating the applicability of SAFSL for MBSE of DPA systems.

• Rafael Petry, Nicolau Santos, Fabrício Rodrigues, Haroldo Silva, Régis Romeu & David B. Cameron [Show all 8 contributors for this article] (2024). Interoperability in petroleum production plants: A case study on the DEXPI P&ID specification. CEUR Workshop Proceedings. 3882. doi: https:/ceur-ws.org/Vol-3882/#fomi-6. Full text in Research Archive Show summary

  This work investigates semantic and syntactic interoperability capabilities on a Piping and Instrumentation Diagram (P&ID) model of engineering plants. Our analysis extends to a case study involving the Data Exchange in the Process Industry (DEXPI) specification, emphasizing the need for well-founded ontological relationships. By mapping P&ID classes of the DEXPI representation model to the Offshore Petroleum Production Plant Ontology (O3PO), we aim to clarify and regulate the semantics of P&ID entities and structuring relations. While technical interoperability between DEXPI P&IDs and BFO-based domain ontologies is achievable, we identify challenges in semantic interoperability of the standard, including issues related to clarity, conciseness, extensibility, consistency, and essence. These challenges could bottleneck seamless system integration and pose adoption barriers for the DEXPI P&ID specification beyond the information handover use case.

• David B. Cameron, Laura Ann Slaughter & Nilla Karlsen-Davies (2024). Building a broad-based collaborative partner programme in digital technologies: The dScience Partner Programme. In Arno Meerman (Eds.), Academic and Practitioner Proceedings of the 2024 UIIN Conference: Challenges and solutions for fostering entrepreneurial universities and collaborative innovation. UIIN. ISSN 9789491901669. p. 36–47. Full text in Research Archive Show summary

  The Centre for Computational and Data Science (dScience) at the University of Oslo was set up in 2021. It is supported by a partner programme, in which leading Norwegian companies make a financial contribution and participate in its activities. The partner programme is managed and enabled by a corps of external engagement advisors who act as bridge builders (boundary spanners) between researchers and partners. The programme implements a project pipeline that will generate funded projects that advance the aims of the university and its partners. The pipeline is enabled through regular partner interactions and thematic working groups that scope research areas and build mutual understanding. We then use a shortterm project model, called an Accelerated Impact Study, for prototype-driven research, feasibility studies and preparation of large-scale research initiatives. This paper presents the centre, its partner programme and describes the methods we have used thus far to build the programme.

• David B. Cameron, Wilhelm Otten, Heiner Temmen, Monica Hole & Gregor Tolksdorf (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 Show summary

  DEXPI Process is a proposed standard for modelling information about process design, as it is presented on block flow and process flow diagrams. It was developed by the DEXPI+ working group and builds upon the DEXPI (Data Exchange in the Process Industry) standard for piping and instrumentation diagrams. Digitalization is making increasing demands on the exchange of information in the process facility lifecycle. Industry 4.0 methods require shared terminology and knowledge models to exchange information. Standards, such as ISO15926, CFIHOS and DEXPI, try to address this need. All these focus on the physical plant items, as shown on a PID or 3D model. There is a lack of standards for early-phase, top-down process design. DEXPI Process fills this gap. This paper presents the development of DEXPI Process in the context of knowledge modelling of process systems and previews how the model is a foundation for applications of automated reasoning and decision support for design and operations.

• Rodrigo Fernandes Calhau, Satyanarayana (Satya) Kokkula, David B. Cameron, Giancarlo Guizzardi & João Paulo A. Almeida (2023). Modeling Competence Framework Elements with an Ontology-based Approach. IEEE Conference on Business Informatics (CBI). ISSN 2378-1963. 25, p. 17–26. doi: 10.1109/CBI58679.2023.10187498. Full text in Research Archive 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.

• Nataliia Korotkova, Josephus Gerardus Benders, Patrik Mikalef & David Cameron (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 Show summary

  IT vendors’ promises are likely to meet sound skepticism from prospective clients. If a particular technology is in vogue and seen as a “hype,” clients are under pressure to buy the technology while, at the same time, skepticism about its claimed benefits might be reinforced. Finding a balance between optimism and skepticism is essential. In this qualitative case study, we examine how an oil and gas supplier company in Norway deals with different pressures when adopting and subsequently implementing digital twin (DT) technologies. DTs offer the promise of creating a digital representation of the physical assets that can keep production facilities operating efficiently and optimally and, as such, have been heralded as enabling the next frontier of productivity improvements. Our results reveal a set of different pressures promoting the decision to adopt the hyped technology. Yet, when descended to the local context, the hype status of DTs evokes multilevel perception segmentation that hype interpreters maneuver by building trust. Based on this analysis, we propose a framework of trust-building mechanisms that contribute to a more nuanced understanding of adoption of hyped technologies and enable practitioners to deal with hype-induced perception obstacles.

• Eduard Kamburjan, Vidar Norstein Klungre, Silvia Lizeth Tapia Tarifa, Rudolf Schlatte, Martin Giese & David B. Cameron [Show all 7 contributors for this article] (2023). Emerging Challenges in Compositionality and Correctness for Digital Twins. CEUR Workshop Proceedings. 3507. Full text in Research Archive Show summary

  A digital twin is an information system that analyzes the behavior of a physical or digital system by connecting streams of observations to dynamic (e.g., simulation) and static (e.g., asset management) models of this twinned system. In large-scale industrial settings, the digital twin will often need to manage a multitude of models for subsystems reflecting different engineering disciplines, vendors, etc. To analyze such complex systems, digital twins must ensure the correct composition of these models and their correct exposure to the user. For the integration and transfer of information between models, digital twins may profit from a formalization of domain knowledge using ontologies, which have proven effective to unify data models. However, it is an open challenge to formalize and verify the correctness of digital twins. This paper discusses this problem for digital twins and illustrates challenges for formal methods with a focus on the composition of heterogeneous dynamic models.

• Yuanwei Qu, Baifan Zhou, Arild Torolv Søetorp Waaler & David B. Cameron (2023). Real-Time Event Detection with Random Forests and Temporal Convolutional Networks for More Sustainable Petroleum Industry. In Fenrong Liu, Arun Anand Sadanandan, Duc Nghia Pham, Petrus Mursanto & Dickson Lukose (Ed.), 20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023 (PRICAI 2023): Trends in Artificial Intelligence. Springer. ISSN 9789819970247. p. 466–473. doi: 10.1007/978-981-99-7025-4_41. Full text in Research Archive Show summary

  The petroleum industry is crucial for modern society, but the production process is complex and risky. During the production, accidents or failures, resulting from undesired production events, can cause severe environmental and economic damage. Previous studies have investigated machine learning (ML) methods for undesired event detection. However, the prediction of event probability in real-time was insufficiently addressed, which is essential since it is important to undertake early intervention when an event is expected to happen. This paper proposes two ML approaches, random forests and temporal convolutional networks, to detect undesired events in real-time. Results show that our approaches can effectively classify event types and predict the probability of their appearance, addressing the challenges uncovered in previous studies and providing a more effective solution for failure event management during the production.

• Rodrigo Fernandes Calhau, Tiago Prince Sales, Ítalo Oliveira, Satyanarayana (Satya) Kokkula, Luís Ferreira Pires & David B. Cameron [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. 3–20. doi: 10.1007/978-3-031-46587-1_1. Full text in Research Archive 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.

• David B. Cameron, Arild Torolv Søetorp Waaler, Erlend Fjøsna, Monica Hole & Foivos Psarommatis Giannakopoulos (2022). A semantic systems engineering framework for zero-defect engineering and operations in the continuous process industries. Frontiers in Manufacturing Technology. 2. doi: 10.3389/fmtec.2022.945717. Full text in Research Archive Show summary

  The on-going twin transition demands that the continuous process industry builds and operates their facilities in a more sustainable way. This change affects the entire supply-chain. The market demands new ways of engineering, procuring and constructing plants that assure quality at each step of the process. Petroleum and petrochemical producers must reduce their waste and environmental footprint and find ways of migrating to sustainable production. There is zero tolerance for waste, emissions or process malfunctions. Engineering contractors need to transfer their skills to new processes and produce series, non-custom facilities for new applications like offshore wind energy, modular production and industrial symbiosis. This is leading to a convergence in methods with discrete manufacturing, especially the automotive industries. In this climate, this sector can benefit from applying Zero-defect Manufacturing (ZDM) to both engineering design and operations. This work defines a framework for implementing ZDM in the process industry supply chain. The framework brings together modelling techniques and models from the following disciplines: system engineering, computer-aided process engineering, automation (especially Industry 4.0) and semantic technologies. These contributions are synthesised into an information fabric that allows engineering firms to work in new ways. Operators and contractors can use the fabric to move from document-driven engineering to data-based processes. The fabric captures requirements and intent in design so that facilities can be delivered and started-up and operated with zero defects in the design and construction. The information is also a vital support for safe and efficient operations and maintenance. We call this zero-defect O&M. The framework combines a systems engineering break-down of facilities, based on ISO/IEC81346, with implementation in SysML, with semantic interoperability frameworks from the process industries (ISO15926). We build upon and synthesise the results of recent standardization initiatives from the industry, notably CFIHOS, DEXPI and READI. We draw on results from process systems engineering, the OntoCAPE ontology and the CAPE-OPEN standards. The framework is illustrated by application to a non-proprietary process system, namely the Tennessee-Eastman process. This example is used to show the modelling approach and indicate how the fabric supports zero-defect practices.

• David Cameron, Arnljot Skogvang, Mihaly Fekete, Henrik Martinsson, Morten Strand & Marcel Castro [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. 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.

• Maryam Ghadrdan & David B. Cameron (2022). An Overview of the Evolution of Oil and Gas 4.0. In Mahdi Sharifzadeh (Eds.), Industry 4.0 Vision for Energy and Materials: Enabling Technologies and Case Studies. John Wiley & Sons. ISSN 9781119695936. p. 241–268. doi: 10.1002/9781119695868.ch9. Full text in Research Archive 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.

• Evgeny Kharlamov, Francisco Martin-Recuerda, Brandon Perry, David B. Cameron, Roar Arne Fjellheim & Arild Torolv Søetorp Waaler (2019). Towards Semantically Enhanced Digital Twins. IEEE International Conference on Big Data (Big Data). ISSN 2639-1589. p. 4189–4193. doi: 10.1109/BigData.2018.8622503. Full text in Research Archive
• David B. Cameron, Arild Waaler & Tiina M. Komulainen (2018). Oil and Gas digital twins after twenty years. How can they be made sustainable, maintainable and useful? Linköping Electronic Conference Proceedings. ISSN 1650-3686. p. 9–16. doi: 10.3384/ecp181539. Full text in Research Archive 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.

• David B. Cameron & Thomas Østerlie (2018). Sirius: Collaboration across the digital divides in the oil and gas supply chain. In Arno Meerman (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. 91–101. Full text in Research Archive 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.

• David B. Cameron, Martin G Skjæveland, Martin Giese, Dag Hovland, Arild Waaler & Eldar Bjørge [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 9781613994597. doi: 10.2118/181111-MS. Full text in Research Archive
• David Bruce Cameron & I. David. L. Bogle (2002). CAPE Tools for Off-Line Simulation, Design and Analysis. In Rafiqul Gani (Eds.), Software Architectures and Tools for Computer-Aided Process Engineering. Elsevier. ISSN 9780444508270. p. 373–392. Full text in Research Archive
• David B. Cameron, William R. Paterson & William Morton (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–C20. Full text in Research Archive

View all works in NVA

• Gregor Tolksdorf, David B. Cameron & Manfred Theißen (2025). DEXPI 2.0: Synergistic Integration of PFD and P&ID in a Unified Digital Model. Chemie Ingenieur Technik. ISSN 0009-286X. 97(11-12), p. 1065–1069. doi: 10.1002/cite.70009. Full text in Research Archive Show summary

  With DEXPI 2.0, the DEXPI association aims to release its specifications for BFDs/PFDs (DEXPI Process) and P&IDs (DEXPI Plant) in one overarching package the first time. This comes with a new simplified serialization and handover format being extensible for future needs and use cases. This short communication based on the PEMT 2024 presentation names the main motivating vision for going this way with the DEXPI specifications: “seamless information handover from simulation to PFD” and “integration of PFD with P&ID”. As an outlook, ideas about the future mechanisms allowing explicit information integration with DEXPI 2.x are sketched.

• David B. Cameron, Laura Ann Slaughter & Nilla Karlsen-Davies (2024). Building a broad-based collaborative partner programme in digital technologies: The dScience Partner Programme. Full text in Research Archive
• Yuanwei Qu, Zhuoxun Zheng, Baifan Zhou, Yan Zhou, Nicolau Santos & Ognjen Savkovic [Show all 8 contributors for this article] (2023). Towards Data Integrity Verification for More Sustainable Petroleum Industry. Full text in Research Archive
• David Cameron (2023). Developments in the DEXPI and IMF that support digital engineering. Full text in Research Archive
• Armin Fricke, Gregor Tolksdorf, David Cameron & Daniel Oliveira de Medeiros (2023). A concept for the implementation of DEXPI+ in DWSIM. Full text in Research Archive
• Armin Fricke, David B. Cameron, Gregor Tolksdorf & D Oliveira de Medeiros (2023). A concept for the implementation of DEXPI+ in DWSIM. Full text in Research Archive
• Kathleen Aviso, Dongda Zhang, David B. Cameron & Jin Xuan (2022). Editorial: Machine learning for chemical processes. Digital Chemical Engineering. 5. doi: 10.1016/j.dche.2022.100062. Full text in Research Archive
• David Cameron (2022). Introduction to the READI Information Modelling Framework. Full text in Research Archive
• David B. Cameron (2021). Digital Twins for Science. The Science of Digital Twins. Full text in Research Archive
• Ingrid Chieh Yu, David B. Cameron, Ann Vølstad & Åshild Hanne Larsen (2020). Industrial mentoring for junior researchers: An enabler for personal development and innovation. Full text in Research Archive 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.

• David Cameron (2020). READI, DSYNE and digital requirements. Full text in Research Archive
• David B. Cameron (2020). Digitale tvillinger. Hva er de? Hvordan kan de brukes? Full text in Research Archive Show summary

  Begrepet «digital tvilling» har blitt et moteord i løpet av de siste årene. Gartnergruppens årlige gjennomgang av slike ord har plassert begrepet på toppen av hypekurven i både 2018 og 2019. Mange leverandører tilbyr systemer som lover å være tvilling til et system, by, anlegg eller menneske. Dette foredraget har målet til å gi en nøktern oversikt over hva en digital tvilling er og hvordan den kan brukes i industriell praksis. Med utgangspunktet i petroleumsindustrien ser vi på hvordan forskning innen informatikk kan bidra til implementeringen av nyttige, kostnadseffektive og skalerbare digitale tvillinger.

• David B. Cameron (2020). Boundary spanning in the interfaces between academic computer science, the IT industry and the extractive industries. Full text in Research Archive
• David B. Cameron, Arild Waaler, Martin G Skjæveland, Anders Gjerver & Christian Mahesh Hansen (2019). The Whole Plant Digital Twin: What Can Semantic Technologies Contribute? Full text in Research Archive
• David B. Cameron (2019). SIRIUS Digital Twins, Semantics and Petrobras. Full text in Research Archive
• David B. Cameron (2019). SIRIUS: Innovation-driven research? Research-driven innovation? Or both together? Full text in Research Archive
• David B. Cameron, Arild Waaler, Knut Sebastian Tungland & Elisabeth Nøst (2019). Using the Industry Collaboration Canvas in the Mid-Term Review. Experience in applying the framework in the oil and gas industry. Full text in Research Archive 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 Frølund, 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.

• David B. Cameron, Arild Waaler & Mara Abel (2019). Digital Twins as a Platform For Artificial Intelligence in the Petroleum Supply Chain. Full text in Research Archive 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 – 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.

• David B. Cameron (2019). Digital Twin Research – University of Oslo. [Internet]. FutureDistributed.org. Full text in Research Archive 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 – 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?

• David B. Cameron (2018). Digitalization perspectives from the oil and gas domain. Full text in Research Archive
• David B. Cameron (2018). Scalable, Useful and Maintainable Digital Twins: Cross-Sector Experience from the Oil and Gas Sector. Full text in Research Archive
• David B. Cameron, Arild Waaler & Tiina M. Komulainen (2018). Oil and Gas digital twins after twenty years. How can they be made sustainable, maintainable and useful? Full text in Research Archive 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.

• David B. Cameron & Thomas Østerlie (2018). Sirius: Collaboration across the digital divides in the oil and gas supply chain. Full text in Research Archive 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.

• David B. Cameron, Johan Wilhelm Kluwer & Bjørn Berli (2017). Masterclass on Digital Engineering. Full text in Research Archive
• David B. Cameron (2017). Scalable data access is necessary for successful digitalisation. Full text in Research Archive
• David B. Cameron (2017). Scalable data access: Lower-cost higher-impact environmental compliance. Full text in Research Archive
• David Bruce Cameron (2017). Digitalization: How can we mix the ”new oil” and the old oil? The role of IT research. Full text in Research Archive 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.

• David Bruce Cameron (2017). Getting the Dinosaurs to Dance: Innovation culture meets academia and the oil and gas industry. Full text in Research Archive
• David Bruce Cameron (2017). Digitalization in the Natural Resources Industry: A Challenge and an Opportunity. Full text in Research Archive
• David Bruce Cameron, Arild Waaler, Dag Hovland & Martin G Skjæveland (2017). Practical Knowledge Representation for Data Access to Subsurface Data: The Achievements and Potential of the Optique Platform. Full text in Research Archive
• David Bruce Cameron (2017). Successful big data projects. Full text in Research Archive
• David B. Cameron (2016). Presentation of SIRIUS Centre. Full text in Research Archive
• David B. Cameron (2016). Analytics and scalable data access: the future of industrial information technology. Full text in Research Archive
• David B. Cameron (2016). Industry 4.0 for the oil and gas sector. Research and Innovation challenges and mechanisms. Full text in Research Archive
• David B. Cameron, Martin G Skjæveland, Martin Giese, Dag Hovland, Arild Waaler & Eldar Bjørge [Show all 7 contributors for this article] (2016). Optique: Simple, Oil & Gas-oriented access to big data in exploration. Full text in Research Archive
• David B. Cameron (2016). Masterclass on Big Data. Full text in Research Archive
• David Bruce Cameron (2014). Big Data in Exploration and Production: Silicon Snake-Oil, Magic Bullet, or Useful Tool? Full text in Research Archive
• Tiina Komulainen, Rasmus Enemark-Rasmussen, Gürkan Sin, John P. Fletcher & David Cameron (2012). Experiences on dynamic simulation software in chemical engineering education. Education for Chemical Engineers. doi: 10.1016/j.ece.2012.07.003. Full text in Research Archive Show summary

  Commercial process simulators are increasing interest in the chemical engineer education. In this paper, the use of commercial dynamic simulation software, D-SPICE® and K-Spice®, for three different chemical engineering courses is described and discussed. The courses cover the following topics: basic chemical engineering, operability and safety analysis and process control. User experiences from both teachers and students are presented. The benefits of dynamic simulation as an additional teaching tool are discussed and summarized. The experiences confirm that commercial dynamic simulators provide realistic training and can be successfully integrated into undergraduate and graduate teaching, laboratory courses and research.

• Rasmus Enemark-Rasmussen, David Cameron, Per Bagge Angelo & Gürkan Sin (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. Full text in Research Archive
• David B. Cameron, Kristin Falk & Satyanarayana (Satya) Kokkula (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 – 10th of February 2021.

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