The digital twin (DT), which involves creating a virtual replica of a physical asset or system, has emerged as a transformative set of tools across various industries. In the oil and gas (O&G) industry, the development of DTs represents a significant evolution in how companies manage complex operations, enhance safety, and optimize decision-making processes. Despite these significant advancements, the underlying tools, technologies, and frameworks for developing DTs in O&G applications remain non-standardized and unfamiliar to many O&G practitioners, highlighting the need for a systematic literature review (SLR) on the topic. Thus, this paper offers an SLR of the existing literature on DT development for O&G from 2018 onwards, utilizing Scopus and Web of Science Core Collection. We provide a comprehensive overview of this field, demonstrate how it is evolving, and highlight standard practices and research opportunities in the area. We perform broad classifications of the 98 studies, categorizing the DTs by their development methodologies, implementation objectives, data acquisition, asset digital development, data integration and preprocessing, data analysis and modeling, evaluation and validation, and deployment tools. We also include a bibliometric analysis of the selected papers, highlighting trends and key contributors. Given the increasing number of new DT developments in O&G and the many new technologies available, we hope to provide guidance on the topic and promote knowledge production and growth concerning the development of DTs for O&G.

Goal: to propose a hybrid method, combining AHP and Fuzzy, for project portfolio management. Originality/value: this paper meets the positive characteristics of both methods, adequately weighing the criteria and contemplating process subjectivity. Limitations that exist in both methods, such as the maximum number of alternatives and the difficulty of inserting new alternatives at the end of the process, are overcome. Design/methodology/approach: the AHP is applied for determining the criteria weights and fuzzy is used to compare the alternatives for each criterion. Results: the results show that the proposed hybrid method allows the ranking of many alternatives and provides higher reliability for decision makers. It must be noted that the system is fed by performance indicators, which minimize the subjectivity in decision-making—an important characteristic in the technology management. Moreover, the results provide the possibility of changing the number of projects at any time without influencing the outcome. Practical implications: the proposed hybrid method can be used in different problems that have many alternatives or subjectivity.

Network parameter errors may come from many different sources, such as: imprecise data provided by manufacturers, poor estimation of transmission line lengths, changes in the transmission network design which are not adequately updated in the corresponding database, etc. Network parameter data are used by almost all power system analysis tools, from real time monitoring to long term planning. Parameter errors may contaminate the obtained results and compromise decision making processes. This work proposes a methodology that combines genetic algorithms and power system state estimation to correct single or multiple network parameter errors. Simulations with the IEEE 14-bus test system are performed to illustrate the proposed method.

Anais do Simpósio de Engenharia, Gestão e Inovação (2674-5526) - Proposta De Um Modelo Matemático Para O Problema De Programação De Equipes De Manutenção Industrial Em Uma Empresa De Óleo E Gás