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Introduction

As far as we know, the 3W Dataset was useful and is cited by the 146 works listed in this document.

There is a dedicated section below for each type of work. These sections are presented in alphabetical order. In each section the works are listed according to the years in which they were published, from the most recent to the oldest.

Our requests

If you know any other published work that cites the 3W Dataset, please let us know by commenting in this discussion.

If you use any resource published in this Git repository, we ask that it be properly cited in your work. Click on the Cite this repository link on this repository landing page to access different citation formats supported by the GitHub citation feature.

Lists of Citations

Books

  1. E. Ogasawara , R. Salles , F. Porto , E. Pacitti. Event Detection in Time Series. Springer Nature Switzerland. 2025. https://link.springer.com/book/10.1007/978-3-031-75941-3.

Conference Papers

  1. H. Leite, D. Leite, D. Rativa. Early Fault Detection and Diagnosis in Industrial Machines: An Approach Using Digital Twins. IEEE Access. 2026. https://doi.org/10.1109/ACCESS.2026.3651993.
  2. A. Petrovski, G. Abramenko, I. Kotenko . Machine Learning-Based Intelligent Measurement in Industrial Digital Twins. Springer Nature. 2026. http://doi.org/10.1007/978-3-032-13612-1_2.
  3. H. Nesse, K.J. Mann, S. Sæten, L.J. Mosser. Production Time Series Monitoring Using Vision-Language Models. SPE Europe Energy Conference and Exhibition. 2025. https://doi.org/10.2118/225545-MS.
  4. J. Lima, H. Castro, L. Oliveira, E. Paixão, L. Baroni, R. Salles, R.E.V. Vargas, E. Ogasawara. UniTED: A Unified Time Series Event Detection Repository. Anais do Brazilian e-Science Workshop (BreSci). 2025. https://doi.org/10.5753/bresci.2025.247972.
  5. J.N.E. Carneiro, R.C. Branco, J. Oliveira, J.V.B. Alves, F. Barrocas, M. Scramignon, A.A. Alves, C.V. Barreto, V.G. Silva, R.L.A. Pinto, R.E.V. Vargas, R.M.E. Barbosa, A. Melo, S. Vieira, A. Faller, A.G. Medeiros. High-Quality Synthetic Dataset Generation for Enhanced Anomaly Detection of Hydrate Plugging in Oil Wells. OTC Brasil. 2025. https://doi.org/10.4043/36170-MS.
  6. R.A. Wibawa, M. Wang, B. Jha. Exploring Modern Feature Extraction Techniques for Improved Offshore Fault Detection in Oil and Gas Operations. SPE Annual Technical Conference and Exhibition. 2025. https://doi.org/10.2118/228061-MS.
  7. G.A. Lima, J.L. Carbonera, J.C. Netto, M. Abel. Ontology-Guided Hybrid Loss for Fault Classification in Oil & Gas. IEEE 37th International Conference on Tools with Artificial Intelligence. 2025. http://doi.org/10.1109/ICTAI66417.2025.00184.
  8. S. Pakhare, S. Hegde, P. Ramasamy, A. Giri. RareDetect: A Deep Federated Learning Framework for Multi-task Learning with RAG-Enhanced Conversational Interface for Low-Frequency Event Detection in Oil Extraction Industry. IEEE International Conference on Electronics, Computing and Communication Technologies. 2025. http://doi.org/10.1109/CONECCT65861.2025.11306901.
  9. L.G. Tavares, J. Lima, M. Melo, C. Chen, J. Garibaldi, G.S. Scatena, A.H.R. Costa, E.S. Gomi, R. Salles, E. Pacitti, I.H.F. Santos, I. G. Siqueira, D. Carvalho, R. Coutinho, F. Porto, E. Ogasawara. Fuzzy-Based Ensemble Method for Robust Concept Drift Detection in Multivariate Time Series. IEEE Access. 2025. https://doi.org/10.1109/IJCNN64981.2025.11228919.
  10. I.M.N. Oliveira, E.T.L. Junior, T.M.A. Vieira, A.C.A. Silva, D.L. Ramos, P.E. Aranha. Deep Transformer Networks Applied to Anomaly Detection in Oil and Gas Wells. Offshore Technology Conference. 2025. https://doi.org/10.4043/35939-MS.
  11. K.C. Schmöckel, G. Peixer, J. Lozano, J. Barbosa. Anomaly Detection in Oil-Producing Wells using LSTM Autoencoder. 28 ABCM International Congress of Mechanical Engineering. 2025. https://doi.org/10.26678/ABCM.COBEM2025.COB2025-0441.
  12. I.M.N. Oliveira, P.E. Aranha, T.M.A. Vieira, A.C.A. Silva, D.L. Ramos, E.T.L. Junior. Advancing Anomaly Detection in Oil Production Wells with TranAD: A Deep Transformer Network Approach. XLV Ibero-Latin American Congress on Computational Methods in Engineering. 2024. http://doi.org/10.55592/cilamce.v6i06.8224.
  13. R. Villamil, J. May, R. Fallgatter, R.E.V. Vargas, A. Nakashima, G. Peixer, J. Lozano, J. Barbosa. Assessment of deep-learning techniques for anomaly detection in offshore oil wells. Brazilian Congress of Thermal Sciences and Engineering. 2024. http://dx.doi.org/10.26678/ABCM.ENCIT2024.CIT24-0603.
  14. A.O. Ifenaike, O.B. Oluwadare. Advancing Drilling Safety: Automated Anomaly Detection in Well Control Using Machine Learning Techniques. SPE Nigeria Annual International Conference and Exhibition. 2024. https://doi.org/10.2118/221626-MS.
  15. B. Cunha, D.D. Ferreira, B.H.G. Barbosa. Desenvolvimento de soft sensor para poços de petróleo usando MGGP. Congresso Brasileiro de Inteligência Computacional. 2024. http://doi.org/10.21528/CBIC2023-064.
  16. O. Khankishiyev, S. Salehi, H. Karami, V. Mammadzada. Identification of Undesirable Events in Geothermal Fluid/Steam Production using Machine Learning. 49th Workshop on Geothermal Reservoir Engineering. 2024. https://pangea.stanford.edu/ERE/db/GeoConf/papers/SGW/2024/Khankishiyev1.pdf.
  17. X. Deng; H. Yin. Industrial Process Fault Diagnosis in Case of Missing Sensor Data. Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS). 2023. https://doi.org/10.1109/SAFEPROCESS58597.2023.10295829.
  18. A. Das, A. Aiken. Prolego: Time-Series Analysis for Predicting Failures in Complex Systems. IEEE International Conference on Autonomic Computing and Self-Organizing Systems - ACSOS. 2023. https://doi.org/10.1109/ACSOS58161.2023.00025.
  19. Y. Qu, B. Zhou, A. Waaler, D. Cameron. Real-Time Event Detection with Random Forests and Temporal Convolutional Networks for More Sustainable Petroleum Industry. Lecture Notes in Computer Science. 2023. http://dx.doi.org/10.1007/978-981-99-7025-4_41.
  20. R.E.V. Vargas, R.L.A. Pinto. The 3W Project and its Strategy to Foster the Development of Data-Driven Solutions for the Offshore Sector. Offshore Technology Conference Brasil. 2023. https://doi.org/10.4043/32875-MS.
  21. A.E. Sulavko, S.A. Klinovenko, G.A. Suvyrin, P.S. Lozhnikov, L.V. Pletnev, A.E. Samotuga. Recognition of pre-emergency situations during oil wells exploitation based on telemetry analysis. 6th International Conference on Signal Processing and Information Security. 2023. https://doi.org/10.1109/ICSPIS60075.2023.10343865.
  22. Y.F. Yeung, A.P. Ajuwape, F. Tahiry, M. Furokawa, T. Hirano, K.Y. Toumi. RoSA: A Mechatronically Synthesized Dataset for Rotodynamic System Anomaly Detection. IEEE International Conference on Intelligent Robots and Systems. 2022. https://doi.org/10.1109/IROS47612.2022.9982146.
  23. E.G.S. Nascimento, I.S. Figueirêdo, L.L.N. Guarieiro. A Novel Self Deep Learning Semi-Supervised Approach to Classify Unlabeled Multivariate Time Series Data. GPU Technology Conference Digital Spring. 2022. https://www.nvidia.com/en-us/on-demand/session/gtcspring22-s41405.
  24. A. Harrouz, H. Toubakh, M.R. Kafi, S.M. Moamar, S. Hajer. Dynamic Linear Regression Models for Down Hole Safety Valve Remaining Useful Life Prediction. Annual conference of the prognostics and health management society 2022. 2022. https://doi.org/10.36001/phmconf.2022.v14i1.3227.
  25. L.D. Gessoni, L.C.F. Domingos, D.P. Franco, L.C. Souza, C.G.C. Junior. Anomaly detection in oil well operation using recurrent neural networks and data augmentation. Rio Oil & Gas Expo and Conference. 2022. https://doi.org/10.48072/2525-7579.rog.2022.307.
  26. A.S. Vargas, R. Werneck, M. Gonçalves, E.S. Pereira, L.A.D.L. Filho, S. Salavati, M. Hossain, A. Ferreira, A.D. Gomes, D.J. Schiozer, A. R. Rocha. Detecting anomalies in production data using machine learning techniques. Rio Oil & Gas Expo and Conference. 2022. http://doi.org/10.48072/2525-7579.rog.2022.298.
  27. C. Brønstad, S.L. Netto, A.L.L. Ramos. Data-driven Detection and Identification of Undesirable Events in Subsea Oil Wells. The Twelfth International Conference on Sensor Device Technologies and Applications. 2021. https://www.thinkmind.org/index.php?view=article&articleid=sensordevices_2021_1_10_28039.
  28. M.J.R. Santos, M.P.C. Fonseca, F.R. Leta, J.F.M. Araujo, G.S. Ferreira, G.B.A. Lima, C.B.C. Lima, L.C. Maia. Classificação de problemas de garantia de escoamento pormeio de algoritmos de machine learning. Series of the Brazilian Society of Computational and Applied Mathematics. 2021. https://proceedings.sbmac.org.br/sbmac/issue/view/11.
  29. I.S. Figueirêdo, T.F. Carvalho, W.J.D. Silva, L.L.N. Guarieiro, E.G.S. Nascimento. Detecting Interesting and Anomalous Patterns In Multivariate Time-Series Data in an Offshore Platform Using Unsupervised Learning. OTC Offshore Technology Conference. 2021. https://doi.org/10.4043/31297-MS.
  30. E.M. Turan, J. Jäschke. Classification of undesirable events in oil well operation. 23rd International Conference on Process Control. 2021. https://doi.org/10.1109/PC52310.2021.9447527.
  31. Y. Li, T. Ge. Imminence Monitoring of Critical Events: A Representation Learning Approach. International Conference on Management of Data. 2021. https://doi.org/10.1145/3448016.3452804.
  32. R.S.F. Nascimento, B.H.G. Barbosa, R.E.V. Vargas, I.H.F. Santos. Fault detection with Stacked Autoencoders and pattern recognition techniques in gas lift operated oil wells. XLII Ibero-Latin-American Congress on Computational Methods in Engineering. 2021. https://www.researchgate.net/publication/363279803_Fault_detection_with_Stacked_Autoencoders_and_pattern_recognition_tech-_niques_in_gas_lift_operated_oil_wells.
  33. B.G. Carvalho, R.E.V. Vargas, R.M. Salgado, C.J. Munaro, F.M. Varejão. Hyperparameter Tuning and Feature Selection for Improving Flow Instability Detection in Offshore Oil Wells. IEEE 19th International Conference on Industrial Informatics. 2021. https://doi.org/10.1109/INDIN45523.2021.9557415.
  34. B.G. Carvalho, R.E.V. Vargas, R.M. Salgado, C.J. Munaro, F.M. Varejão. Flow Instability Detection in Offshore Oil Wells with Multivariate Time Series Machine Learning Classifiers. 30th International Symposium on Industrial Electronics. 2021. https://doi.org/10.1109/ISIE45552.2021.9576310.
  35. R. Karl, J. Takeshita, T. Jung. Cryptonite: A Framework for Flexible Time-Series Secure Aggregation with Non-interactive Fault Recovery. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering. 2021. http://dx.doi.org/10.1007/978-3-030-90019-9_16.
  36. T. Lu, W. Xia, X. Zou, Q. Xia. Adaptively Compressing IoT Data on the Resource-constrained Edge. 3rd {USENIX} Workshop on Hot Topics in Edge Computing. 2020. https://www.usenix.org/system/files/hotedge20_paper_lu.pdf.
  37. Y. Li, T. Ge, C. Chen. Data Stream Event Prediction Based on Timing Knowledge and State Transitions. Very Large Data Base Endowment. 2020. http://www.vldb.org/pvldb/vol13/p1779-li.pdf.
  38. R.S.F. Nascimento, B.H.G. Barbosa, R.E.V. Vargas, I.H.F. Santos. Detecção de falhas com Stacked Autoencoders e técnicas de reconhecimento de padrões em poços de petróleo operados por gas lift. XXIII Congresso Brasileiro de Automática. 2020. https://www.sba.org.br/open_journal_systems/index.php/cba/article/view/1462/1300.
  39. L. Müller, M.R. Martins. Proposition of Reliability-based Methodology for Well Integrity Management During Operational Phase. 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference. 2020. https://doi.org/10.3850%2F978-981-14-8593-0_3682-cd.
  40. E.S.P. Sobrinho, F.L. Oliveira, J.L.R. Anjos, C. Gonçalves, M.V.D. Ferreira, L.G.O. Lopes, W.W.M. Lira, J.P.N. Araújo, T.B. Silva, L.P. Gouveia. Uma ferramenta para detectar anomalias de produção utilizando aprendizagem profunda e árvore de decisão. Rio Oil & Gas Expo and Conference. 2020. https://icongresso.ibp.itarget.com.br/arquivos/trabalhos_completos/ibp/3/final.IBP0938_20_27112020_085551.pdf.
  41. R.S.F. Nascimento, B.H.G. Barbosa, R.E.V. Vargas, I.H.F. Santos. Detecção de anomalias em poços de petróleo surgentes com Stacked Autoencoders. Simpósio Brasileiro de Automação Inteligente. 2020. https://doi.org/10.20906/sbai.v1i1.2856.
  42. W.F. Junior, R.E.V. Vargas, K.S. Komati, K.A.S. Gazolli. Detecção de anomalias em poços produtores de petróleo usando aprendizado de máquina. XXIII Congresso Brasileiro de Automática. 2020. https://www.sba.org.br/open_journal_systems/index.php/cba/article/download/1405/1005.
  43. M.J.R. Santos, A.O.S. Castro, G.S. Ferreira, A.L. D’Almeida, G.B.A. Lima, F.R. Leta, C.B.C. Lima, L.C. Maia. Utilização de modelos estatísticos para detecção precoce de falhas em poços de petróleo offshore. Rio Oil & Gas Expo and Conference. 2020. https://biblioteca.ibp.org.br/scripts/bnmapi.exe?router=upload/33989.
  44. L.H.S. Mello, M.P. Ribeiro, T.O. Santos, F.M. Varejão, A.L. Rodrigues. Metric Learning for Electrical Submersible Pump Fault Diagnosis. International Joint Conference on Neural Networks. 2020. http://doi.org/10.1109/IJCNN48605.2020.9207133.
  45. R.E.V. Vargas, C.J. Munaro, P.M. Ciarelli. A methodology for generating datasets for development of anomaly detectors in oil wells based on Artificial Intelligence techniques. I Congresso Brasileiro em Engenharia de Sistemas em Processos. 2019. https://www.ufrgs.br/psebr/wp-content/uploads/2019/04/Abstract_A019_Vargas.pdf.

Data Articles

  1. R.E.V. Vargas, A. Melo, C.J. Munaro, C.B.C. Lima, E.T.L. Junior, F. Barrocas, F.M. Varejão, G. Peixer, I.M.N. Oliveira, J. Barbosa, J. Lozano, J.C.D. Araújo, J.N.E. Carneiro, L.G.O. Lopes, L.P. Gouveia, M. Fernandes, M. Scramignon, P.M. Ciarelli, R. Branco, R.L.A. Pinto. 3W Dataset 2.0.0: a realistic and public dataset with rare undesirable real events in oil wells. arXiv. 2025. https://doi.org/10.48550/arXiv.2507.01048.

Doctoral Theses

  1. P.E. Aranha. Aranha Development of Data-Driven Methodologies for Predicting Anomalies and Ensuring Well Integrity in Offshore Oil Production Using Machine Learning Approaches. Universidade de São Paulo. 2025. https://github.com/petrobras/3W/raw/main/docs/doctoral_thesis_pedro_aranha.pdf.
  2. L.G.O. Lopes. Open-World Learning Applied to Oil Wells Using Autoencoder-Based Clustering. Universidade Federal de Alagoas. 2025. https://github.com/petrobras/3W/raw/main/docs/doctoral_thesis_lucas_lopes.pdf.
  3. E.M. Turan. Advances in Optimisation and Machine Learning for Process Systems Engineering. Norwegian University of Science and Technology. 2024. https://github.com/petrobras/3W/raw/main/docs/doctoral_thesis_evren_turan.pdf.
  4. A.P.F. Machado. Methodologies to Improve One-Class Classifier Performance Applied to Multivariate Time Series. Universidade Federal do Espírito Santo. 2024. https://github.com/petrobras/3W/raw/main/docs/doctoral_thesis_andre_machado.pdf.
  5. Y. Li. Predictive Analysis and Critical Event Monitoring in Large Dynamic Networks. University of Massachusetts Lowell. 2023. https://github.com/petrobras/3W/raw/main/docs/doctoral_thesis_yan_li.pdf.
  6. I.S. Figueirêdo. Uma nova abordagem de inteligência artificial baseada em autoaprendizagem profunda para manutenção preditiva em um ambiente de produção de petróleo e gás offshore. Centro Universitário Senai Cimatec. 2023. https://github.com/petrobras/3W/raw/main/docs/doctoral_thesis_ilan_figueiredo.pdf.
  7. A.J.M. Junior. Integração humano-máquina para o monitoramento de processos industriais baseado em dados. Universidade Federal do Rio de Janeiro. 2023. https://github.com/petrobras/3W/raw/main/docs/doctoral_thesis_afranio_junior.pdf.
  8. R.E.V. Vargas. Base de dados e benchmarks para prognóstico de anomalias em sistemas de elevação de petróleo. Universidade Federal do Espírito Santo. 2019. https://github.com/petrobras/3W/raw/main/docs/doctoral_thesis_ricardo_vargas.pdf.

Final Graduation Projects

  1. V. Benedito. Desenvolvimento de um Sistema Ensemble para a Detecção e Classificação de Anomalias em Poços de Petróleo Off-Shore. Universidade Estadual de Maringá. 2025. https://github.com/petrobras/3W/raw/main/docs/final_graduation_project_victor_benedito.pdf.
  2. A.V.S. Alves. Sensores virtuais baseados em aprendizado de máquina para poços de petróleo. Universidade de Brasília. 2023. https://github.com/petrobras/3W/raw/main/docs/final_graduation_project_arthur_alves.pdf.
  3. M.G. Proença. Modelos de aprendizado de máquina aplicados à detecção de anomalias em poços produtores de petróleo. Universidade Federal do Paraná. 2022. https://github.com/petrobras/3W/raw/main/docs/final_graduation_project_martim_proenca.pdf.
  4. R.L. Rosa. Classificação de eventos indesejaveis na produção de petróleo offshore com aplicação de técnicas de inteligência artificial. Universidade Federal Fluminense. 2020. https://github.com/petrobras/3W/raw/main/docs/final_graduation_project_renato_rosa.pdf.

Journal Articles

  1. B. Grimstad, E. Lundby, H. Andersson. ManyWells: Simulation of multiphase flow in thousands of wells. Geoenergy Science and Engineering. 2026. https://doi.org/10.1016/j.geoen.2025.214226.
  2. H.G. Brito, C.W.S.P. Maitelli, O.C. Filho . Detection of obstructions in oil and gas pipelines: machine learning techniques for hydrate classification. Springer Nature. 2026. https://doi.org/10.1007/s43153-026-00643-x.
  3. J.A.G. Camperos, C.M.R. Diaz, M.M.H. Cely, A.P. Garcia, O.M.H. Rodriguez. Classification of Dense-Gas/Liquid Flow Patterns in Horizontal Pipe Through Pressure Gradient Time Series Encoded Into Images and Deep Learning . SPE Journal. 2026. https://doi.org/10.2118/232781-PA.
  4. G. Walia, M. Kumar. Uncertainty-aware remaining useful life prediction and PPO based optimal maintenance scheduling in industrial IoT. Reliability Engineering & System Safety. 2026. https://doi.org/10.1016/j.ress.2026.112356.
  5. L.Q. Arias, C.M.R. Diaz, J.A.G. Camperos, O.M.H. Rodriguez, A.P. Garcia. Application of Transformer neural networks for the classification of two-phase oil–water flow patterns in horizontal pipelines. Frontiers in Mechanical Engineering. 2026. https://doi.org/10.3389/fmech.2025.1710934.
  6. R. Oliveira, . Sant’Anna, P. Silva. Seleção Adaptativa de Hiperparâmetros com Aprendizado por Reforço para Modelos de Detecção de Anomalias em Séries Temporais Multivariadas. Sociedade Brasileira de Inteligência Artificial. 2025. https://doi.org/10.21528/CBIC2025-1162938.
  7. D.S.D. Lara, R.A. Coelho, C.L. Castro, W.M. Caminhas. An unsupervised noise-resistant method for detection of incremental drifts. Springer Nature. 2025. https://link.springer.com/article/10.1007/s10115-025-02592-2.
  8. N.V.C. Silva, F.A. Pontes, M.S. Silva, B.C. Fialho, J.E. Delesposte, D.G.B. Souza, L.A.O. Chaves, R. Cardoso. Leveraging FMMEA for Digital Twin Development: A Case Study on Intelligent Completion in Oil and Gas. Sensors. 2025. https://doi.org/10.3390/s25185846.
  9. L.P. Oliveira, R.M.F.U. Foronda, A.V. Grillo, B.F. Santos. Development of Machine Learning Models for Sandface Pressure Prediction in Oil Well. Chemical Engineering Transactions. 2025. https://doi.org/10.3303/CET25117186.
  10. A. Vahdani, A. Daneshpour, M. Ghatee, M. Sharifi. A Hybrid Deep Learning Framework for Critical Failure Diagnosis in Offshore Oil Wells: Integrating AutoEncoders and MobileNet. SSRN. 2025. http://doi.org/10.2139/ssrn.5761405.
  11. D.A. Maharsi, S.Z. Tampi, A.P.P. Oktaviani. An Lstm-Based Anomaly Detection on Subsea Oil-Producing Well. Scientific Contributions Oil and Gas. 2025. https://doi.org/10.29017/scog.v48i4.1819.
  12. A.A.M. Azevedo, S.L. Netto, R.E.V. Vargas, E.A.B. Silva, A.J.M. Junior. Exploratory Analysis of the 3W Dataset for Detecting Operational Failures in Oil Wells Using Machine Learning Techniques. SPE Journal. 2025. https://doi.org/10.4043/36194-MS.
  13. X. Zou, S. Wang, Y. Shi, X. Chen, S. Jin, D. Tao, W. Xia. The Design of an Efficient Lossy Compressor for Time Series Databases. ACM Transactions on Architecture and Code Optimization . 2025. http://doi.org/10.1145/3767158.
  14. H.N. Noura, J.P.A. Yaacoub, O. Salman, A. Chehab. Advanced Machine Learning in Smart Grids: An Overview. Internet of Things and Cyber‑Physical Systems. 2025. https://doi.org/10.1016/j.iotcps.2025.05.002.
  15. I.S. Figueirêdo, L.L.N. Guarieiro, E.G.S. Nascimento. A novel self-learning model to classify unlabeled multivariate time-series applied to fault diagnosis. Geoenergy Science and Engineering. 2025. https://www.sciencedirect.com/science/article/abs/pii/S294989102500346X?via%3Dihub.
  16. A.P.F. Machado, C.J. Munaro, P.M. Ciarelli. Enhancing one-Class classifiers performance in multivariate time series through dynamic clustering: A case study on hydraulic system fault detection. Expert Systems with Applications. 2025. https://doi.org/10.1016/j.eswa.2025.128088.
  17. R. Karl, N. Koirala, T. Januszewicz, J. Takeshita, T. Jung. Cryptonite: A Framework for Flexible Time‑Series Secure Aggregation with Non‑interactive Fault Recovery. SN Computer Science. 2025. https://doi.org/10.1007/s42979-025-03804-w.
  18. L.M. Oliveira, M.B. Kattel, D.S. Oliveira, J.C. Vasquez, J.M. Guerrero, F.L.M. Antunes. Enhanced Adaptive Autoencoder Framework for Continuous Monitoring and Fault Management in Industrial Sensors. IEEE Sensors Journal. 2025. https://doi.org/10.1109/JSEN.2025.3565982.
  19. L.G.O. Lopes, T.M.A. Vieira, P.E. Aranha, E.T.L. Junior, W.W.M. Lira. Detection and classification of anomalies in oil well production using Open-World Learning. Engineering Applications of Artificial Intelligence. 2025. https://doi.org/10.1016/j.engappai.2025.111514.
  20. P.E. Aranha, N.A. Policarpo, M.A. Sampaio. A Transformer-based approach for anomaly detection in intelligent well completions. Petroleum Exploration and Development. 2025. https://doi.org/10.1016/S1876-3804(25)60620-3.
  21. G. Bayazitova, M. Anastasiadou, V.D. Santos. Oil and gas flow anomaly detection on offshore naturally flowing wells using deep neural networks. Geoenergy Science and Engineering. 2024. https://doi.org/10.1016/j.geoen.2024.213240.
  22. S.M.S. Aguilar, A.P.B. Sobral, M.C. Amaral, M.F.D. Vianna, F.S. Machado, M.A.C. Moreira. Analysis of candidate oil wells for workover interventions using machine learning tools. International Journal of Scientific Management and Tourism. 2024. http://doi.org/10.55905/ijsmtv10n4-041.
  23. J.V. Autran, V. Kuhn, J.P. Diguet, M. Dubois, C. Buche. AI4I-PMDI: Predictive maintenance datasets with complex industrial settings’ irregularities. 28th International Conference on Knowledge Based and Intelligent information and Engineering Systems. 2024. https://doi.org/10.1016/j.procs.2024.09.546.
  24. A. Markaj, M. Mercangöz, A. Fay. Design and implementation of an Autonomous Systems Training Environment framework for control algorithm evaluation in autonomous plant operation. Computers & Chemical Engineering. 2024. https://doi.org/10.1016/j.compchemeng.2024.108798.
  25. T.L.B. Dias, M.A. Marins, C.L. Pagliari, R.M.E. Barbosa, M.L.R. Campos, E.A.B. Silva, S.L. Netto. Development of Oilwell Fault Classifiers Using a Wavelet-Based Multivariable Approach in a Modular Architecture. SPE Journal. 2024. https://doi.org/10.2118/221463-PA.
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  36. P.E. Aranha, L.G.O. Lopes, E.S.P. Sobrinho; I.M.N. Oliveira, J.P.N. Araújo, B.B. Santos; E.T.L. Junior, T.B. Silva, T.M.A. Vieira, W.W.M. Lira, N.A. Policarpo, M.A. Sampaio. A System to Detect Oilwell Anomalies Using Deep Learning and Decision Diagram Dual Approach. SPE Journal. 2023. https://doi.org/10.2118/218017-PA.
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  41. R.M.F.U. Foronda, V.M. Fracassio, R.B. Santos, B.F. Santos. Statistical Analysis in Database of Offshore Naturally Flowing Wells with Abnormal Events. Chemical Engineering Transactions. 2023. https://doi.org/10.3303/CET2399101.
  42. E. Jovicic, D. Primorac, M. Cupic, A. Jovic. Publicly Available Datasets for Predictive Maintenance in the Energy Sector: A Review. IEEE Access. 2023. https://doi.org/10.1109/ACCESS.2023.3295113.
  43. I. Yousef, L.D. Rippon, C. Prévost, S.L. Shah, R.B. Gopaluni. The arc loss challenge: A novel industrial benchmark for process analytics and machine learning. Journal of Process Control. 2023. https://doi.org/10.1016/j.jprocont.2023.103023.
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  47. S. Casolo. Severe slugging flow identification from topological indicators. Digital Chemical Engineering. 2022. https://doi.org/10.1016/j.dche.2022.100045.
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Master's Degree Dissertations

  1. A.A.M. Azevêdo. Análise exploratória do conjunto de dados 3W para detecção de falhas de operação de poços de petróleo, usando técnicas de aprendizado de máquina. Universidade Federal do Rio de Janeiro. 2024. https://github.com/petrobras/3W/raw/main/docs/master_degree_dissertation_antonio_azevedo.pdf.
  2. R. Schena. A methodology for synthetic generation of failure data for data-driven prognostics and health management (PHM) modeling for digital twins. Universidade Federal do Rio Grande do Sul. 2023. https://lume.ufrgs.br/handle/10183/267589.
  3. W.F. Junior. Comparação de classificadores para detecção de anomalias em poços produtores de petróleo. Instituto Federal do Espírito Santo. 2022. https://github.com/petrobras/3W/raw/main/docs/master_degree_dissertation_wander_junior.pdf.
  4. B.G. Carvalho. Evaluating machine learning techniques for detection of flow instability events in offshore oil wells. Universidade Federal do Espírito Santo. 2021. https://github.com/petrobras/3W/raw/main/docs/master_degree_dissertation_bruno_carvalho.pdf.
  5. L. Müller. Proposição de metodologia baseada em confiabilidade para gerenciamento da integridade de poços em produção. Universidade de São Paulo. 2021. https://github.com/petrobras/3W/raw/main/docs/master_degree_dissertation_luiz_muller.pdf.
  6. L.E.G. Vignoli. Análise Comparativa de Métodos para Detecção de Eventos em Séries Temporais. Centro Federal de Educação Tecnológica Celso Suckow da Fonseca. 2021. https://github.com/petrobras/3W/raw/main/docs/master_degree_dissertation_luciana_vignoli.pdf.
  7. R.S.F. Nascimento. Detecção de anomalias em poços de produção de petróleo offshore com a utilização de autoencoders e técnicas de reconhecimento de padrões. Universidade Federal de Lavras. 2021. https://github.com/petrobras/3W/raw/main/docs/master_degree_dissertation_rodrigo_nascimento.pdf.
  8. C. Brønstad. Data-driven detection and identification of undesirable events in subsea oil wells. University of South-Eastern Norway. 2020. https://github.com/petrobras/3W/raw/main/docs/master_degree_dissertation_chrisander_bronstad.pdf.
  9. M.J.R. Santos. Detecção de problemas de garantia de escoamento a partir da utilização de ferramentas de aprendizado de máquina. Universidade Federal Fluminense. 2020. https://github.com/petrobras/3W/raw/main/docs/master_degree_dissertation_mayara_santos.pdf.
  10. I.M.N. Oliveira. Técnicas de inferência e previsão de dados como suporte à análise de integridade de revestimentos. Universidade Federal de Alagoas. 2020. https://github.com/petrobras/3W/raw/main/docs/master_degree_dissertation_igor_oliveira.pdf.

Other Articles

  1. M.C.K. de Oliveira, R.L.A. Pinto, J.N.E. Carneiro. A digital transformation journey in flow assurance. T&B Petroleum magazine. 2022. https://tbpetroleum.com.br/revistas/2022/41.

Repository Articles

  1. M. Fernandes, E. Gildin, M.A. Sampaio. Soft Sensor for Bottom-Hole Pressure Estimation in Petroleum Wells Using Long Short-Term Memory and Transfer Learning. arXiv. 2026. https://doi.org/10.48550/arXiv.2602.03737.
  2. C. Shyalika, R. Wickramarachchi, A. Sheth. A Comprehensive Survey on Rare Event Prediction. arXiv. 2023. https://doi.org/10.48550/arXiv.2309.11356.
  3. M.A. Farahani, M.R. McCormick, R. Harik, T. Wuest. Time-Series Classification in Smart Manufacturing Systems: An Experimental Evaluation of State-of-the-Art Machine Learning Algorithms. arXiv. 2023. https://doi.org/10.48550/arXiv.2310.02812.
  4. R. Salles, J. Lima, R. Coutinho, E. Pacitti, F. Masseglia, R. Akbarinia, C. Chen, J. Garibaldi, F. Porto, E. Ogasawara, M. Reis. SoftED: Metrics for Soft Evaluation of Time Series Event Detection. arXiv. 2023. https://doi.org/10.48550/arXiv.2304.00439.
  5. R. Olsson, C. Tran, L.V. Magnusson. Automatic Synthesis of Neurons for Recurrent Neural Nets. arXiv. 2022. https://doi.org/10.48550/arXiv.2207.03577.
  6. R. Karl, J. Takeshita, N. Koirala, T. Jung. Cryptonite: A Framework for Flexible Time-Series Secure Aggregation with Online Fault Tolerance. Cryptology ePrint Archive. 2021. https://eprint.iacr.org/2020/1561.
  7. B. Chen, X. Zeng, W. Zhang, Z. Hou, S. Xu, C. Sun, D. Han. Federated Learning for Cross-block Oil-water Layer Identification. arXiv. 2021. https://doi.org/10.48550/arXiv.2112.14359.
  8. L.G.O. Lopes, T.M.A. Vieira, W.W.M. Lira. Automatic evaluation of scientific abstracts through natural language processing. arXiv. 2021. https://doi.org/10.48550/arXiv.2112.01842.

Specialization Monographs

  1. G.G. Momm. Detecção de anomalias em sensores de poços submarinos com uso de redes neurais artificiais. Universidade de São Paulo. 2022. https://github.com/petrobras/3W/raw/main/docs/specialization_monograph_gustavo_momm.pdf.