This article highlights the partnership between CNOOC, CESAR, and UFAL to revolutionize offshore exploration in the Buzios pre-salt field. The project allocates R$18.5 million to CESAR for the development of advanced solutions using digital twins and AI, enabling the anticipation of operational failures and the creation of scalable technologies for the global oil and gas market

This work presents a modular and orchestrated pipeline that automates document ingestion, extraction, semantic enrichment, and indexing. The proposed framework integrates Artificial Intelligence (AI) and Natural Language Processing (NLP) models to extract and enrich semantic metadata from multimodal sources, including text, images, and tables.

The presented solution estimates fatigue damage based on commonly measured parameters, specifically BOP stack and riser angles. Combined with riser, BOP, and wellhead data, it obtains a time series of bend moment and shear force at the wellhead, and then a transfer-function approach based on FEA of just the wellhead system to obtain stress time history at critical locations along it. The whole process is automated through a web-based application that integrates internal databases to quickly retrieve data necessary for the assessment, perform all necessary analysis, and present results through user-friendly dashboards.

This paper reports a case in which a collaborative glossary was developed during user immersion and adopted as a guiding artifact for AI design and engineering through participatory sessions involving domain experts, designers, software engineers, and data scientists. The resulting living document defined technical and operational terms while translating everyday practices, metaphors, and user needs; it also mapped synonyms, terminological variations, and contexts of use to align ambiguous expressions with real intentions.

This study investigates the application of Information Design and Human Factors in redesigning Job Safety Analysis (JSA) in Petrobras refineries, aiming to improve risk communication in highly hazardous industrial environments.

This article presents the results of a design project aimed at including deaf employees in a Petrobras refinery, focusing on overcoming communication barriers in the industrial environment.

This article details the evolution of SAVIA, presenting results from the retraining process of machine learning models based on an updated database that included projects executed between 2023 and 2024. During the retraining process, in addition to the previously used models, the XGBoost model was introduced into the experimentation process.

This article presents an approach to the use of RISC-V in high-performance computing, outlining its main characteristics, applications, and current development.

This work evaluates machine learning models for predicting the execution time of oil reservoir simulation jobs in HPC clusters.

This study aims to understand the challenges of applying SSI to the constrained IoT through a Systematic Mapping Study (SMS). The results showed that although there is a limited number of identified articles, research has grown in the last six years, with more than half of the studies published in the last two years.

This article presents a digital application and a method developed to estimate and monitor the consumption of spare parts for industrial equipment, aiming to solve the lack of information for defining these items.

This article presents an application of the Automatic Query Expansion (AQE) technique for information retrieval in a collection of geoscientific documents in Portuguese. The proposed AQE uses a knowledge base composed of thesauri, glossaries, and lists of entities related to the Oil and Gas domain, selected with the help of experts in the geoscientific domain.

The article presents SAVIA (Selection of Alternatives via Artificial Intelligence), an AI-based solution developed under Petrobras' Internal Startups Program in partnership with CESAR, aimed at innovating the well design process known as SELEPOÇO. The tool assists well designers by suggesting alternative well configurations for investment projects, with a focus on improving technology integration and cost optimization.

In this work we study the performance and accuracy of machine learning models trained for this task, and analyzed the impact of their deployment in a microcontroller-based System-on-Chip used by IoT devices, along with the impact of TinyML-based optimizations, such as vectorization and quantization.

This article discusses the development of an intelligent lighting control system based on the Internet of Things (IoT), focusing on Industry 4.0. The system aims to optimize energy consumption in industrial environments, promoting greater efficiency and sustainability.

This article presents a practical example of the use of digital twins in a TV production line, where different neural network models and computer vision were used for data analysis.

This article describes a monitoring and control system for industrial tanks, focusing on the precise measurement of tank level and the quality of the stored liquid. Ultrasonic, temperature, and gas sensors were used, allowing the system a comprehensive view of the tank conditions.

This work presents the development and implementation of a GPS tracker based on LoRaWAN technology as a solution to overcome these limitations. The proposal uses IoT devices equipped with LoRa modules and GPS receivers, integrated into a LoRaWAN communication infrastructure, to track inputs in real time, even in industrial environments with high levels of Wi-Fi network noise.

The purpose of this study is to develop a project for detecting toxic and flammable gases with applications in various environments. A prototype will be created using commercially available sensors, namely the MQ-2 sensor, capable of detecting concentrations of combustible gases and smoke in the air; the MQ-3 sensor, capable of detecting alcohol and ethanol vapors (a sensor commonly used in breathalyzer projects); and the MQ-135 sensor, capable of detecting concentrations of gases harmful to air quality.

This paper proposes automatic monitoring of the quality of multimedia content transmitted between a smartphone and a digital TV via a wireless transmission channel. The methodology combines two tools: the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM).

The present study aims to explore the familiarity of managers, leaders, and developers with software visualization tools and how these tools are employed to facilitate the technical onboarding of new team members. Method. To address the research problem, we built upon the insights gained through the literature and embraced a sequential exploratory approach. This approach incorporated quantitative and qualitative analyses of data collected from practitioners using questionnaires and semi-structured interviews.

This paper investigates the integration of generative AI tools within software development, focusing on understanding their uses, benefits, and challenges to software professionals, in particular, looking at aspects of productivity. Through a pilot case study involving software practitioners working in different roles, we gathered valuable experiences on the integration of generative AI tools into their daily work routines.

This article discusses the adoption of technology in Brazil, where U˜4.0 (CESAR and partners) creates digital twins of ovens and heaters, reducing gas consumption by up to 15% using sensors and machine learning, generating savings and competitiveness.

This work presents a precise methodology to define the common requirements that should be present in any IoT platform. After defining these requirements, a detailed survey is carried out among 32 specialists to prioritize these requirements, identifying which of them are essential. As a result, among a set of 64 mapped requirements, 26 are discussed regarding their relevance.

This work presents the doctoral research for the design of a meta-architecture for Internet of Things platforms, that addresses the interoperability challenges and the complexity in building IoT solutions.

This chapter explores the applications of Digital Twin (DT) technology in the Brazilian energy sector and its impact on businesses and society. It highlights how DT applications have contributed to cost reduction, human error mitigation, operational optimization, and technical failure prediction. The chapter also discusses the implementation process and the requirements for developing these systems.

This chapter presents a summary of how the most diverse industry sectors such as smart cities, oil, gas sector, energy and other areas are applying digital twins as a tool to support the digitization of companies.

This work aims to propose a study to develop an electronic device using Arduino capable of monitoring the operation of a three-phase induction motor in industry.

This article proposes the development of a data manipulation and visualization layer for thermal sensors in BIM models, represented in a virtual reality environment. The methodology includes the creation of these models to allow real-time monitoring. The results indicate that the system assists in the analysis of thermal comfort conditions in indoor environments, contributing to the efficient management of facilities and the well-being of users, integrating objective data with virtual immersion.

This work proposes a framework based on the following strategies, widely used in the literature: Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Linear Programming, besides our Heuristic approach. Furthermore, we restricted the resource allocation optimization to a real-time scenario, facilitating its use in an industrial process.