{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T06:32:58Z","timestamp":1770532378348,"version":"3.49.0"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"36","license":[{"start":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T00:00:00Z","timestamp":1727395200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T00:00:00Z","timestamp":1727395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s00521-024-10424-7","type":"journal-article","created":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T08:02:59Z","timestamp":1727424179000},"page":"22805-22827","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Controlled time series forecasting for oil reservoir management"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9758-715X","authenticated-orcid":false,"suffix":"Jr.","given":"Alexandre","family":"de Souza","sequence":"first","affiliation":[]},{"given":"Juan A. R.","family":"Tueros","sequence":"additional","affiliation":[]},{"given":"Mateus G.","family":"Machado","sequence":"additional","affiliation":[]},{"given":"Rafael F. V. C.","family":"Santos","sequence":"additional","affiliation":[]},{"given":"Ramiro B.","family":"Willmersdorf","sequence":"additional","affiliation":[]},{"given":"Silvana M. B.","family":"Afonso","sequence":"additional","affiliation":[]},{"given":"Leonardo C.","family":"Oliveira","sequence":"additional","affiliation":[]},{"given":"Bernardo","family":"Horowitz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,27]]},"reference":[{"key":"10424_CR1","unstructured":"Abadi M, Agarwal A, Barham P (2015) TensorFlow: large-scale machine learning on heterogeneous distributed systems. fonte: http:\/\/download.tensorflow.org\/paper\/whitepaper2015.pdf"},{"issue":"03","key":"10424_CR2","doi-asserted-by":"publisher","first-page":"0992","DOI":"10.2118\/201193-PA","volume":"23","author":"A Alakeely","year":"2020","unstructured":"Alakeely A, Horne RN (2020) Simulating the behavior of reservoirs with convolutional and recurrent neural networks. SPE Reserv Eval Eng 23(03):0992\u20131005","journal-title":"SPE Reserv Eval Eng"},{"key":"10424_CR3","unstructured":"Brownlee J (2018) Deep learning for time series forecasting, Machine Learning Mastery"},{"key":"10424_CR4","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1016\/j.neucom.2019.07.034","volume":"363","author":"M Canizo","year":"2019","unstructured":"Canizo M, Triguero I, Conde A, Onieva E (2019) Multi-head CNN\u2013RNN for multi-time series anomaly detection: an industrial case study. Neurocomputing 363:246\u2013260","journal-title":"Neurocomputing"},{"key":"10424_CR5","doi-asserted-by":"crossref","unstructured":"Cao F (2014) Development of a two-phase flow coupled capacitance resistance model. Em PhD Dissertation. The University of Texas at Austin, USA","DOI":"10.2118\/169485-MS"},{"key":"10424_CR6","unstructured":"Chollet F (2015) Keras. Dispon\u00edvel em: https:\/\/github.com\/fchollet\/keras"},{"key":"10424_CR7","volume-title":"Deep learning with python","author":"F Chollet","year":"2021","unstructured":"Chollet F (2021) Deep learning with python. Manning Publications Co"},{"key":"10424_CR8","unstructured":"Computer Modeling Group LTD (2022) IMEX. Em User\u2019s Guide. Calgary, Canada"},{"key":"10424_CR9","doi-asserted-by":"publisher","DOI":"10.1002\/9780470770801","volume-title":"Engineering design via surrogate modelling: a practical guide","author":"A Forrester","year":"2008","unstructured":"Forrester A, Sobester A, Keane A (2008) Engineering design via surrogate modelling: a practical guide. Wiley"},{"key":"10424_CR10","volume-title":"Description of OLYMPUS reservoir model for optimization challenge","author":"RM Fonseca","year":"2017","unstructured":"Fonseca RM, Geel CR, Leeuwenburgh O (2017) Description of OLYMPUS reservoir model for optimization challenge. Integrated Systems Approach to Petroleum Production, Netherlands"},{"key":"10424_CR11","unstructured":"G\u00e9ron A (2023) Hands-on machine learning with Scikit-learn, Keras, and TensorFlow. O\u2019Reilly Media, Inc."},{"key":"10424_CR12","first-page":"800","volume-title":"An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. MIT Press, p 800"},{"key":"10424_CR13","doi-asserted-by":"crossref","unstructured":"Graves A, Graves A (2012) Long short-term memory. Supervised sequence labeling with recurrent neural network, pp 37\u201345","DOI":"10.1007\/978-3-642-24797-2_4"},{"issue":"15","key":"10424_CR14","doi-asserted-by":"publisher","first-page":"5293","DOI":"10.3390\/app10155293","volume":"10","author":"RY Hamad","year":"2020","unstructured":"Hamad RY (2020) Joint learning of temporal models to handle imbalanced data for human activity recognition. Appl Sci 10(15):5293","journal-title":"Appl Sci"},{"key":"10424_CR15","doi-asserted-by":"crossref","unstructured":"Holanda RW, Gildin E (2015) Improved waterflood analysis using the capacitance-resistance model within a control systems framework. Em Society of Petroleum Engineers","DOI":"10.2118\/177106-MS"},{"key":"10424_CR16","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1016\/j.petrol.2013.11.006","volume":"112","author":"B Horowitz","year":"2013","unstructured":"Horowitz B, Afonso SMB, De Mendo\u00e7a CVP (2013) Surrogate based optimal waterflooding management. J Petrol Sci Eng 112:206\u2013219","journal-title":"J Petrol Sci Eng"},{"key":"10424_CR17","doi-asserted-by":"publisher","first-page":"3039","DOI":"10.3390\/en14113039","volume":"14","author":"B Ibrahim","year":"2021","unstructured":"Ibrahim B, Rabelo LA (2021) Deep learning approach for peak load forecasting: a case study on Panama. Energies 14:3039","journal-title":"Energies"},{"key":"10424_CR18","doi-asserted-by":"publisher","first-page":"623","DOI":"10.3390\/w15040623","volume":"15","author":"Y Jiang","year":"2023","unstructured":"Jiang Y, Shen W, Zhang H, Zhang K, Wang J, Zhang L (2023) An interpretable recurrent neural network for waterflooding reservoir flow disequilibrium analysis. Water 15:623\u2013639","journal-title":"Water"},{"key":"10424_CR19","unstructured":"Katz H, Horowitz B, Tueros J (2017) Numerical experience using capacitance resistance multilayered models. Ibero-latin American Congress in Computational Methods in Engineering (CILAMCE). Florianopolis, Brazil"},{"issue":"04","key":"10424_CR20","doi-asserted-by":"publisher","first-page":"1837","DOI":"10.2118\/203980-PA","volume":"26","author":"YD Kim","year":"2021","unstructured":"Kim YD, Durlofsky LJ (2021) A recurrent neural network\u2013based proxy model for well-control optimization with nonlinear output constraints. SPE J 26(04):1837\u20131857","journal-title":"SPE J"},{"issue":"02","key":"10424_CR21","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/s10596-022-10189-9","volume":"27","author":"YD Kim","year":"2023","unstructured":"Kim YD, Durlofsky LJ (2023) Convolutional - recurrent neural network proxy for robust optimization and closed-loop reservoir management. Comput Geosci 27(02):179\u2013202","journal-title":"Comput Geosci"},{"key":"10424_CR22","unstructured":"Kingma DP, Ba J (2015) A method for stochastic optimization. In: International conference on learning representations (ICLR)"},{"issue":"4","key":"10424_CR23","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"L Cun","year":"1989","unstructured":"Cun L et al (1989) Backpropagation applied to handwritten zip code recognition. Em Neural Comput 1(4):541\u2013551","journal-title":"Em Neural Comput"},{"key":"10424_CR24","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/BF02478259","volume":"5","author":"W McCulloch","year":"1943","unstructured":"McCulloch W, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115\u2013133","journal-title":"Bull Math Biophys"},{"key":"10424_CR25","volume-title":"Neural networks and deep learning","author":"MA Nielsen","year":"2015","unstructured":"Nielsen MA (2015) Neural networks and deep learning. Determination Press"},{"key":"10424_CR26","doi-asserted-by":"publisher","first-page":"7","DOI":"10.2516\/ogst\/2020090","volume":"76","author":"SD Oliveira","year":"2021","unstructured":"Oliveira SD, Horowitz B (2021) Ensemble-based method with combined fractional flow model for waterflooding optimization. Em Oil Gas Sci Technol Rev IFP Energ Nouv 76:7","journal-title":"Em Oil Gas Sci Technol Rev IFP Energ Nouv"},{"key":"10424_CR27","doi-asserted-by":"publisher","first-page":"128701","DOI":"10.1016\/j.energy.2023.128701","volume":"284","author":"S Pan","year":"2023","unstructured":"Pan S et al (2023) Oil well production prediction based on CNN-LSTM model with self-attention mechanism. Energy 284:128701","journal-title":"Energy"},{"key":"10424_CR28","doi-asserted-by":"crossref","unstructured":"Peters E, Arts RJ, Brouwer GK, Geel CR (2009) Results of the brugge benchmark study for flooding optimisation and history matching. Paper SPE 119094 presented at the SPE Reservoir Simulation Symposium, The Woodlands, Texas, USA, 2\u20134 February","DOI":"10.2118\/119094-MS"},{"key":"10424_CR29","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1007\/s00521-023-09056-0","volume":"36","author":"LAM Ramalho","year":"2024","unstructured":"Ramalho LAM, Tueros JAR, Horowitz B (2024) Machine learning applied to evaluation of reservoir connectivity. Neural Comput Appl 36:731\u2013746","journal-title":"Neural Comput Appl"},{"key":"10424_CR30","doi-asserted-by":"publisher","first-page":"1943","DOI":"10.1007\/s10596-019-09854-3","volume":"24","author":"VLS Silva","year":"2020","unstructured":"Silva VLS, Cardoso MA, Oliveira DFB et al (2020) Stochastic optimization strategies applied to the OLYMPUS benchmark. Comput Geosci 24:1943\u20131958","journal-title":"Comput Geosci"},{"key":"10424_CR31","doi-asserted-by":"publisher","unstructured":"Souza A, Castro A, Dall\u2019Aqua M, Tueros J, Horowitz B, Gildin E (2020) Nonlinear State Constraints Handling in Waterflooding Optimization Through Reduced Order Models. https:\/\/doi.org\/10.3997\/2214-4609.202035070","DOI":"10.3997\/2214-4609.202035070"},{"key":"10424_CR32","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1016\/j.petrol.2018.06.089","volume":"170","author":"JAR Tueros","year":"2018","unstructured":"Tueros JAR, Horowitz B, Willmersdorf RB, De Oliveira DFB (2018) Non-distance-based localization techniques for ensemble-based waterflooding optimization. J Petrol Sci Eng 170:440\u2013452","journal-title":"J Petrol Sci Eng"},{"key":"10424_CR33","doi-asserted-by":"publisher","first-page":"202","DOI":"10.2118\/102913-PA","volume":"14","author":"GM Van Essen","year":"2009","unstructured":"Van Essen GM (2009) Robust waterflooding optimization of multiple geological scenarios. Em SPE J 14:202\u2013210","journal-title":"Em SPE J"},{"issue":"04","key":"10424_CR34","doi-asserted-by":"publisher","first-page":"865","DOI":"10.3390\/w11040865","volume":"11","author":"D Zhang","year":"2019","unstructured":"Zhang D, Peng Q, Lin J, Wang D, Liu X, Zhuang J (2019) Simulating reservoir operation using a recurrent neural network algorithm. Water 11(04):865\u2013882","journal-title":"Water"},{"issue":"1","key":"10424_CR35","first-page":"5467956","volume":"2023","author":"L Zhang","year":"2023","unstructured":"Zhang L et al (2023) CNN-LSTM model optimized by bayesian optimization for predicting single-well production in water flooding reservoir. Geofluids 2023(1):5467956","journal-title":"Geofluids"},{"key":"10424_CR36","doi-asserted-by":"publisher","first-page":"121249","DOI":"10.1016\/j.apenergy.2023.121249","volume":"344","author":"G Zhou","year":"2023","unstructured":"Zhou G et al (2023) A CNN-BiGRU-AM neural network for AI applications in shale oil production prediction. Appl Energy 344:121249","journal-title":"Appl Energy"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10424-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10424-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10424-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T20:04:16Z","timestamp":1732651456000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10424-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,27]]},"references-count":36,"journal-issue":{"issue":"36","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["10424"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10424-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,27]]},"assertion":[{"value":"29 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The authors declare to make available the data that were used to generate this work when required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Data availability"}}]}}