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Introduction

As far as we know, the 3W Dataset was useful and is cited by the 100 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. 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. No link yet.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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. 2021. https://doi.org/10.20906/sbai.v1i1.2856.
  11. 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. 2021. https://www.sba.org.br/open_journal_systems/index.php/cba/article/view/1462/1300.
  12. 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. 2021. https://biblioteca.ibp.org.br/scripts/bnmapi.exe?router=upload/33989.
  13. 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. 2021. https://www.nvidia.com/en-us/on-demand/session/gtcspring22-s41405.
  14. 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. 2021. https://doi.org/10.36001/phmconf.2022.v14i1.3227.
  15. 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. 2021. https://doi.org/10.1109/IROS47612.2022.9982146.
  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. 2021. https://pangea.stanford.edu/ERE/db/GeoConf/papers/SGW/2024/Khankishiyev1.pdf.
  17. 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. 2021. http://dx.doi.org/10.1007/978-981-99-7025-4_41.
  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. 2021. https://doi.org/10.1109/ACSOS58161.2023.00025.
  19. 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. 2021. https://doi.org/10.4043/32875-MS.
  20. 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). 2021. https://doi.org/10.1109/SAFEPROCESS58597.2023.10295829.
  21. 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. 2021. https://doi.org/10.2118/221626-MS.
  22. 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. 2021. http://dx.doi.org/10.26678/ABCM.ENCIT2024.CIT24-0603.
  23. 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.
  24. 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.
  25. 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.
  26. 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.
  27. 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.
  28. 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.

Doctoral Theses

  1. L. Omena. Open-World Learning Applied to Oil Wells Using Autoencoder-Based Clustering. Universidade Federal de Alagoas. 2025. No link yet.
  2. Y. Li. Predictive Analysis and Critical Event Monitoring in Large Dynamic Networks. University of Massachusetts Lowell. 2021. https://github.com/petrobras/3W/raw/main/docs/doctoral_thesis_yan_li.pdf.
  3. 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. 2021. https://github.com/petrobras/3W/raw/main/docs/doctoral_thesis_ilan_figueiredo.pdf.
  4. A.J.M. Junior. Integração humano-máquina para o monitoramento de processos industriais baseado em dados. Universidade Federal do Rio de Janeiro. 2021. https://github.com/petrobras/3W/raw/main/docs/doctoral_thesis_afranio_junior.pdf.
  5. A.P.F. Machado. Methodologies to Improve One-Class Classifier Performance Applied to Multivariate Time Series. Universidade Federal do Espírito Santo. 2021. https://github.com/petrobras/3W/raw/main/docs/doctoral_thesis_andre_machado.pdf.
  6. E.M. Turan. Advances in Optimisation and Machine Learning for Process Systems Engineering. Norwegian University of Science and Technology. 2021. https://github.com/petrobras/3W/raw/main/docs/doctoral_thesis_evren_turan.pdf.
  7. 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/blob/main/docs/final_graduation_project_victor_benedito.pdf.
  2. R.L. Rosa. Classificação de eventos indesejaveis na produção de petróleo offshore com aplicação de técnicas de inteligência artificial. Universidade Federal Fluminense. 2021. https://github.com/petrobras/3W/raw/main/docs/final_graduation_project_renato_rosa.pdf.
  3. A.V.S. Alves. Sensores virtuais baseados em aprendizado de máquina para poços de petróleo. Universidade de Brasília. 2021. https://github.com/petrobras/3W/raw/main/docs/final_graduation_project_arthur_alves.pdf.
  4. M.G. Proença. Modelos de aprendizado de máquina aplicados à detecção de anomalias em poços produtores de petróleo. Universidade Federal do Paraná. 2021. https://github.com/petrobras/3W/raw/main/docs/final_graduation_project_martim_proenca.pdf.

Journal Articles

  1. H. Zhou, H. Yin, Y. Qin, C. Yuen. Industrial Fault Diagnosis With Incremental Learning Capability Under Varying Sensory Data. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2024. https://doi.org/10.1109/TSMC.2024.3500019.
  2. C. Shyalika, R. Wickramarachchi, A. P. Sheth. A Comprehensive Survey on Rare Event Prediction. Association for Computing Machinery. 2024. https://doi.org/10.1145/3699955.
  3. A.O.S. Castro, M.J.R. Santos, F.R. Leta, C.B.C. Lima, G.B.A. Lima. Unsupervised Methods to Classify Real Data from Offshore Wells. American Journal of Operations Research. 2021. https://doi.org/10.4236/ajor.2021.115014.
  4. M.J.R. Santos, A.O.S. Castro, F.R. Leta, J.F.M. Araujo, G.S. Ferreira, R.A. Santos, C.B.C. Lima, G.B.A. Lima. Statistical analysis of offshore production sensors for failure detection applications. Brazilian Journal of Development. 2021. https://doi.org/10.34117/bjdv7n8-681.
  5. M.A. Marins, B.D. Barros, I.H.F. Santos, D.C. Barrionuevo, R.E.V. Vargas, T.M. Prego, A.A. Lima, M.L.R. Campos, E.A.B. Silva, S.L. Netto. Fault detection and classification in oil wells and production/service lines using random forest. Journal of Petroleum Science and Engineering. 2021. https://doi.org/10.1016/j.petrol.2020.107879.
  6. R. Karl, J. Takeshita, N. Koirla, 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. A.S. Vargas, R. Werneck, R. Moura, P.M. Júnior, R. Prates, M. Castro, M. Gonçalves, M. Hossain, M. Zampieri, A. Ferreira, A. Davólio, B. Hamann, D.J. Schiozer, A. Rocha. A visual analytics approach to anomaly detection in hydrocarbon reservoir time series data. Journal of Petroleum Science and Engineering. 2021. https://doi.org/10.1016/j.petrol.2021.108988.
  8. I.S. Figueirêdo, T.F. Carvalho, W.J.D. Silva, L.L.N. Guarieiro, A.A.B. Santos, L.S.M. Filho, R.E.V. Vargas, E.G.S. Nascimento. Unsupervised Machine Learning for Anomaly Detection in Multivariate Time Series Data of a Rotating Machine from an Oil and Gas Platform. Journal of Systemics, Cybernetics and Informatics. 2021. https://www.iiisci.org/journal/PDV/sci/pdfs/ZA422HO21.pdf.
  9. F.M. Varejão. Diagnóstico Inteligente de Falhas em Equipamentos Industriais. Revista de Sistemas de Informação da FSMA. 2021. http://www.fsma.edu.br/si/edicao28/FSMA_SI_2021_2_04_Varejao_Final.pdf.
  10. A. Melo, M.M. Câmara, N. Clavijo, J.C. Pinto. Open benchmarks for assessment of process monitoring and fault diagnosis techniques: A review and critical analysis. Computers & Chemical Engineering. 2021. https://doi.org/10.1016/j.compchemeng.2022.107964.
  11. S.V. Tsyplenkov, E.D. Agafonov. The concept of an integrated system of energy efficiency control of artifical oil lift. Power Engineering Research Equipment Technology. 2021. http://dx.doi.org/10.30724/1998-9903-2021-23-4-180-196.
  12. T. Hafeez, L. Xu, G. Mcardle. Edge Intelligence for Data Handling and Predictive Maintenance in IIOT. IEEE Access. 2021. https://ieeexplore.ieee.org/document/9387301.
  13. M.A. Sahraoui, C. Rahmoune, M. Zair, F. Gougam, A. Damou. Enhancing fault diagnosis of undesirable events in oil & gas systems: A machine learning approach with new criteria for stability analysis and classification accuracy. Journal of Process Mechanical Engineering. 2021. https://doi.org/10.1177/09544089231213778.
  14. 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. 2021. https://doi.org/10.3303/CET2399101.
  15. E. Jovicic, D. Primorac, M. Cupic, A. Jovic. Publicly Available Datasets for Predictive Maintenance in the Energy Sector: A Review. IEEE Access. 2021. https://doi.org/10.1109/ACCESS.2023.3295113.
  16. A. Harrouz, H. Salem, H. Toubakh, R.M. Kafi, M.S. Mouchaweh. Fault prognosis of subsurface safety valve system with limited real data using self-adaptive neural network. Evolving Systems. 2021. https://doi.org/10.1007/s12530-023-09525-w.
  17. 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. 2021. https://doi.org/10.1016/j.jprocont.2023.103023.
  18. B. Chen, X. Zeng, W. Zhang, L. Fan, S. Cao, J. Zhou. Knowledge sharing-based multi-block federated learning for few-shot oil layer identification. Energy. 2021. https://doi.org/10.1016/j.energy.2023.128406.
  19. W.F. Junior, K.S. Komati, K.A.S. Gazolli. Anomaly detection in oil-producing wells: a comparative study of one-class classifiers in a multivariate time series dataset. Journal of Petroleum Exploration and Production Technology. 2021. https://doi.org/10.1007/s13202-023-01710-6.
  20. P.E. Aranha, N.A. Policarpo, M.A. Sampaio. Unsupervised machine learning model for predicting anomalies in subsurface safety valves and application in offshore wells during oil production. Journal of Petroleum Exploration and Production Technology. 2021. https://doi.org/10.1007/s13202-023-01720-4.
  21. D. Leite, A. Martins, D. Rativa, J.F.L. de Oliveira, A.M.A. Maciel. An Automated Machine Learning Approach for Real-Time Fault Detection and Diagnosis. Sensors. 2021. https://doi.org/10.3390%2Fs22166138.
  22. A.L. D’Almeida, N.C.R. Bergiante, G.S. Ferreira, F.R. Leta, C.B.C. Lima, G.B.A. Lima. Digital transformation: a review on artificial intelligence techniques in drilling and production applications. The International Journal of Advanced Manufacturing Technology. 2021. https://doi.org/10.1007/s00170-021-08631-w.
  23. A.P.F. Machado, R.E.V. Vargas, P.M. Ciarelli, C.J. Munaro. Improving performance of one-class classifiers applied to anomaly detection in oil wells. Journal of Petroleum Science and Engineering. 2021. https://doi.org/10.1016/j.petrol.2022.110983.
  24. N. Aslam, I.U. Khan, A. Alansari, M. Alrammah, A. Alghwairy, Rahaf Alqahtani, Razan Alqahtani, M. Almushikes, M.A. Hashim. Anomaly Detection Using Explainable Random Forest for the Prediction of Undesirable Events in Oil Wells. Applied Computational Intelligence and Soft Computing. 2021. https://doi.org/10.1155/2022/1558381.
  25. F. Gatta, F. Giampaolo, D. Chiaro, F. Piccialli. Predictive maintenance for offshore oil wells by means of deep learning features extraction. Expert Systems. 2021. https://doi.org/10.1111/exsy.13128.
  26. S. Casolo. Severe slugging flow identification from topological indicators. Digital Chemical Engineering. 2021. https://doi.org/10.1016/j.dche.2022.100045.
  27. Y.S.A. ElWahab, M.M. Nasr, F.K.A. Sheref. An intelligent oil accident predicting and classifying system using deep learning techniques. Indonesian Journal of Electrical Engineering and Computer Science. 2021. https://doi.org/10.11591/ijeecs.v29.i1.pp460-471.
  28. L.V. Magnusson, J.R. Olsson, C. Tran. Recurrent Neural Networks for Oil Well Event Prediction. IEEE Intelligent Systems. 2021. https://doi.org/10.1109/MIS.2023.3252446.
  29. L.H.S. Mello, T.O. Santos, F.M. Varejão, M.P. Ribeiro, A.L. Rodrigues. Ensemble of metric learners for improving electrical submersible pump fault diagnosis. Journal of Petroleum Science and Engineering. 2021. https://doi.org/10.1016/j.petrol.2022.110875.
  30. P.E. Coutinho, L.H.M. Silveira, M. Cataldi, F.R. Leta, A.O.S. Castro, C.B.C. Lima, G.B.A. Lima. Wavelet Transform Processing in Detecting Failures in Offshore Well Production. Latin American Journal of Energy Research. 2021. https://doi.org/10.21712/lajer.2022.v9.n1.p1-11.
  31. A. Melo, M.M. Câmara, J.C. Pinto. Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey. Processes. 2021. https://doi.org/10.3390/pr12020251.
  32. F.M. Varejão, L.H.S. Mello, M.P. Ribeiro, T.O. Santos, A.L. Rodrigues. An open source experimental framework and public dataset for vibration-based fault diagnosis of electrical submersible pumps used on offshore oil exploration. Knowledge-Based Systems. 2021. https://doi.org/10.1016/j.knosys.2024.111452.
  33. A.P.F. Machado, C.J. Munaro, P.M. Ciarelli, R.E.V. Vargas. Time series clustering to improve one-class classifier performance. Expert Systems with Applications. 2021. https://doi.org/10.1016/j.eswa.2023.122895.
  34. 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. 2021. https://doi.org/10.2118/221463-PA.
  35. 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. Robotics and Computer-Integrated Manufacturing. 2021. https://doi.org/10.1016/j.rcim.2024.102839.
  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. 2021. https://doi.org/10.2118/218017-PA.
  37. L. Liu, J. Li, Z. Niu, W. Zhang, J.C. Xue, H. Xu. Efficient Time-Series Data Delivery in IoT with Xender. IEEE Transactions on Mobile Computing. 2021. https://doi.org/10.1109/TMC.2023.3296608.
  38. A. Melo, T.S.M. Lemos, R.M. Soares, D. Spina, N. Clavijo, L.F.O. Campos, M.M. Câmara, T. Feital, T.K. Anzai, P.H. Thompson, F.C. Diehl, J.C. Pinto. BibM2on: An open source Python package for process monitoring, soft sensing, and fault diagnosis. Digital Chemical Engineering. 2021. https://doi.org/10.1016/j.dche.2024.100182.
  39. 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. 2021. https://doi.org/10.1016/j.geoen.2024.213240.
  40. D. Semwogerere, S. Sangesland, J. Vatn, A. Pavlov, D. Colombo. Well integrity and late life extension - A current industry state of practice and literature review. Geoenergy Science and Engineering. 2021. https://doi.org/10.1016/j.geoen.2024.213419.
  41. 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. 2021. https://doi.org/10.1016/j.compchemeng.2024.108798.
  42. J. Liu, J. Gu, H. Li, K.H. Carlson. Machine learning and transport simulations for groundwater anomaly detection. Journal of Computational and Applied Mathematics. 2020. https://doi.org/10.1016/j.cam.2020.112982.

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. C. Brønstad. Data-driven detection and identification of undesirable events in subsea oil wells. University of South-Eastern Norway. 2021. https://github.com/petrobras/3W/raw/main/docs/master_degree_dissertation_chrisander_bronstad.pdf.
  3. M.J.R. Santos. Detecção de problemas de garantia de escoamento a partir da utilização de ferramentas de aprendizado de máquina. Universidade Federal Fluminense. 2021. https://github.com/petrobras/3W/raw/main/docs/master_degree_dissertation_mayara_santos.pdf.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. W.F. Junior. Comparação de classificadores para detecção de anomalias em poços produtores de petróleo. Instituto Federal do Espírito Santo. 2021. https://github.com/petrobras/3W/raw/main/docs/master_degree_dissertation_wander_junior.pdf.
  9. 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. 2021. https://lume.ufrgs.br/handle/10183/267589.
  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. 2021. https://tbpetroleum.com.br/revistas/2022/41.

Repository Articles

  1. 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.
  2. R. Olsson, C. Tran, L.V. Magnusson. Automatic Synthesis of Neurons for Recurrent Neural Nets. arXiv. 2021. https://doi.org/10.48550/arXiv.2207.03577.
  3. 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. 2021. https://doi.org/10.48550/arXiv.2304.00439.
  4. C. Shyalika, R. Wickramarachchi, A. Sheth. A Comprehensive Survey on Rare Event Prediction. arXiv. 2021. https://doi.org/10.48550/arXiv.2309.11356.
  5. 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. 2021. https://doi.org/10.48550/arXiv.2310.02812.
  6. 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.

Specialization Monograph

  1. G.G. Momm. Detecção de anomalias em sensores de poços submarinos com uso de redes neurais artificiais. Universidade de São Paulo. 2021. https://github.com/petrobras/3W/raw/main/docs/specialization_monograph_gustavo_momm.pdf.