{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T10:45:59Z","timestamp":1769165159184,"version":"3.49.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T00:00:00Z","timestamp":1715126400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T00:00:00Z","timestamp":1715126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100014266","name":"Shell Brasil","doi-asserted-by":"publisher","award":["ANP 21870-1"],"award-info":[{"award-number":["ANP 21870-1"]}],"id":[{"id":"10.13039\/501100014266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014266","name":"Shell Brasil","doi-asserted-by":"publisher","award":["ANP 21870-1"],"award-info":[{"award-number":["ANP 21870-1"]}],"id":[{"id":"10.13039\/501100014266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s00521-024-09834-4","type":"journal-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T08:01:51Z","timestamp":1715155311000},"page":"14527-14541","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Few-shot learning and modeling of 3D reservoir properties for predicting oil reservoir production"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8194-5097","authenticated-orcid":false,"given":"Gabriel","family":"Cirac","sequence":"first","affiliation":[]},{"given":"Guilherme Daniel","family":"Avansi","sequence":"additional","affiliation":[]},{"given":"Jeanfranco","family":"Farfan","sequence":"additional","affiliation":[]},{"given":"Denis Jos\u00e9","family":"Schiozer","sequence":"additional","affiliation":[]},{"given":"Anderson","family":"Rocha","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,8]]},"reference":[{"issue":"2","key":"9834_CR1","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/S1876-3804(20)60057-X","volume":"47","author":"A Shahkarami","year":"2020","unstructured":"Shahkarami A, Mohaghegh S (2020) Applications of smart proxies for subsurface modeling. Petrol Explor Develop 47(2):400\u2013412","journal-title":"Petrol Explor Develop"},{"issue":"3","key":"9834_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3386252","volume":"53","author":"Y Wang","year":"2021","unstructured":"Wang Y, Yao Q, Kwok JT, Lionel M (2021) Generalizing from a few examples: a survey on few-shot learning. ACM Comput Surv 53(3):1\u201334","journal-title":"ACM Comput Surv"},{"key":"9834_CR3","doi-asserted-by":"crossref","unstructured":"Hospedales TM, Antoniou A, Micaelli P, Storkey AJ (2021) Meta-learning in neural networks: a survey. IEEE Trans Pattern Anal Mach Intell, p 1\u201330","DOI":"10.1109\/TPAMI.2021.3079209"},{"issue":"34","key":"9834_CR4","doi-asserted-by":"publisher","first-page":"24055","DOI":"10.1007\/s00521-023-09100-z","volume":"35","author":"A Makhlouf","year":"2023","unstructured":"Makhlouf A, Maayah M, Abughanam N, Catal C (2023) The use of generative adversarial networks in medical image augmentation. Neural Comput Appl 35(34):24055\u201324068","journal-title":"Neural Comput Appl"},{"issue":"34","key":"9834_CR5","doi-asserted-by":"publisher","first-page":"24369","DOI":"10.1007\/s00521-023-08989-w","volume":"35","author":"Z Li","year":"2023","unstructured":"Li Z, Shi L, Wang J, Cristea AI, Zhou Y (2023) Sim-GAIL: a generative adversarial imitation learning approach of student modelling for intelligent tutoring systems. Neural Comput Appl 35(34):24369\u201324388","journal-title":"Neural Comput Appl"},{"issue":"3","key":"9834_CR6","doi-asserted-by":"publisher","first-page":"2431","DOI":"10.1007\/s11053-021-09844-2","volume":"30","author":"NCS Wui","year":"2021","unstructured":"Wui NCS, Jahanbani GA, Nait AM, Ole T (2021) Smart proxy modeling of a fractured reservoir model for production optimization: implementation of metaheuristic algorithm and probabilistic application. Nat Resour Res 30(3):2431\u20132462","journal-title":"Nat Resour Res"},{"issue":"2","key":"9834_CR7","doi-asserted-by":"publisher","first-page":"85","DOI":"10.3390\/fluids4020085","volume":"4","author":"G Vida","year":"2019","unstructured":"Vida G, Shahab MD, Mohammad M (2019) Smart proxy modeling of SACROC CO2-EOR. Fluids 4(2):85","journal-title":"Fluids"},{"key":"9834_CR8","unstructured":"Kahneman D (2013) Thinking, fast and slow. Farrar, straus and giroux, New York, 1st pbk. ed edition, OCLC: ocn834531418"},{"key":"9834_CR9","doi-asserted-by":"crossref","unstructured":"Pievatolo A, Ruggeri F (2018) Bayes linear uncertainty analysis for oil reservoirs based on multiscale computer experiments, vol 1. Oxford University Press, Oxford","DOI":"10.1093\/oxfordhb\/9780198703174.013.10"},{"key":"9834_CR10","doi-asserted-by":"crossref","unstructured":"Ferreira C, Vernon I, Schiozer DJ, Goldstein M (2014) Use of emulator methodology for uncertainty reduction quantification. In: Day 3 Fri, May 23, 2014, page D031S021R002, Maracaibo, Venezuela, . SPE","DOI":"10.2118\/169405-MS"},{"key":"9834_CR11","doi-asserted-by":"crossref","unstructured":"Jahanbakhsh A, ElSheikh A, Sohrabi M (2016) Application of ensemble smoother and multiple-data assimilation for estimating relative permeability from coreflood experiments. In: Application of ensemble smoother and multiple-data assimilation for estimating relative permeability from coreflood experiments, Amsterdam, Netherlands","DOI":"10.3997\/2214-4609.201601816"},{"key":"9834_CR12","doi-asserted-by":"crossref","unstructured":"Panja P, Pathak M, Velasco R, Deo M (2016) Least square support vector machine: an emerging tool for data analysis. In: All days, pp SPE\u2013180202\u2013MS, Denver, Colorado, USA, SPE","DOI":"10.2118\/180202-MS"},{"key":"9834_CR13","doi-asserted-by":"crossref","unstructured":"Sabatino R, Viviani E, Della\u00a0Rossa E, Sala C,Maffioli A (2014) Structural uncertainty integration within reservoir risk analysis and history matching. In All days, Amsterdam, The Netherlands, SPE","DOI":"10.2118\/170761-MS"},{"key":"9834_CR14","doi-asserted-by":"crossref","unstructured":"Da Silva LM, Ferreira LM, Avansi GD, Schiozer DJ, Alves-Souza SN (2022) Selection of a dimensionality reduction method: An application to deal with high-dimensional geostatistical realizations in oil reservoirs. SPE Reser Eval Eng 10:1\u201319","DOI":"10.2118\/212299-PA"},{"issue":"3","key":"9834_CR15","doi-asserted-by":"publisher","first-page":"123","DOI":"10.3390\/fluids4030123","volume":"4","author":"M Ansari","year":"2019","unstructured":"Ansari M, Shahnam D (2019) Modeling average pressure and volume fraction of a fluidized bed using data-driven smart proxy. Fluids 4(3):123","journal-title":"Fluids"},{"issue":"04","key":"9834_CR16","doi-asserted-by":"publisher","first-page":"1343","DOI":"10.2118\/203828-PA","volume":"23","author":"SLM Da","year":"2020","unstructured":"Da SLM, Daniel AG, Jos\u00e9 SD (2020) Support vector regression for petroleum reservoir production forecast considering geostatistical realizations. SPE Reser Eval Eng 23(04):1343\u20131357","journal-title":"SPE Reser Eval Eng"},{"issue":"5","key":"9834_CR17","doi-asserted-by":"publisher","first-page":"2951","DOI":"10.1016\/j.petsci.2023.04.001","volume":"20","author":"D Shu-Yi","year":"2023","unstructured":"Shu-Yi D, Zhao X-G, Xie C-Y, Zhu J-W, Wang J-L, Yang J-S, Song H-Q (2023) Data-driven production optimization using particle swarm algorithm based on the ensemble-learning proxy model. Petrol Sci 20(5):2951\u20132966","journal-title":"Petrol Sci"},{"issue":"2","key":"9834_CR18","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\u2014recurrent neural network proxy for robust optimization and closed-loop reservoir management. Comput Geosci 27(2):179\u2013202","journal-title":"Comput Geosci"},{"issue":"03","key":"9834_CR19","doi-asserted-by":"publisher","first-page":"1314","DOI":"10.2118\/205000-PA","volume":"26","author":"Z Zhong","year":"2021","unstructured":"Zhong Z, Sun AY, Ren B, Wang Y (2021) A deep-learning-based approach for reservoir production forecast under uncertainty. SPE J 26(03):1314\u20131340","journal-title":"SPE J"},{"key":"9834_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2020.107574","volume":"194","author":"Z Zhong","year":"2020","unstructured":"Zhong Z, Sun AY, Wang Y, Ren B (2020) Predicting field production rates for waterflooding using a machine learning-based proxy model. J Petrol Sci Eng 194:107574","journal-title":"J Petrol Sci Eng"},{"key":"9834_CR21","unstructured":"von Hohendorff Filho J, Victorino I, Castro M, Schiozer D (2024) Investigating proxy models for a production system in integrated simulations with oil reservoir. J Petrol Scie Technol"},{"key":"9834_CR22","doi-asserted-by":"crossref","unstructured":"Singh V, Pandey RK, Ruwali N, Kumar A (2023) Forecasting cumulative oil recovery from waterflood using a deep proxy model. In 2023 14th International conference on computing communication and networking technologies (ICCCNT), pp 1\u20134, Delhi, India, IEEE","DOI":"10.1109\/ICCCNT56998.2023.10307505"},{"key":"9834_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.jgsce.2024.205219","volume":"122","author":"B Yan","year":"2024","unstructured":"Yan B, Zhong Z, Bai B (2024) A convolutional neural network-based proxy model for field production prediction and history matching. Gas Sci Eng 122:205219","journal-title":"Gas Sci Eng"},{"key":"9834_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2020.103767","volume":"94","author":"CCB Cavalcante","year":"2020","unstructured":"Cavalcante CCB, Maschio C, Schiozer D, Rocha A (2020) A stochastic learning-from-data approach to the history-matching problem. Engi Appl Artif Intell 94:103767","journal-title":"Engi Appl Artif Intell"},{"key":"9834_CR25","volume-title":"Petroleum reservoir simulation","author":"K Aziz","year":"1979","unstructured":"Aziz K, Settari A (1979) Petroleum reservoir simulation. Applied Science Publishers, London"},{"issue":"10","key":"9834_CR26","doi-asserted-by":"publisher","first-page":"36","DOI":"10.22161\/ijaers.710.5","volume":"7","author":"SLM Da","year":"2020","unstructured":"Da SLM, Daniel AG, Jos\u00e9 SD (2020) Development of proxy models for petroleum reservoir simulation: a systematic literature review and state-of-the-art. Int J Adv Eng Res Sci 7(10):36\u201362","journal-title":"Int J Adv Eng Res Sci"},{"key":"9834_CR27","unstructured":"Gui J, Sun Z, Wen Y, Tao D, Ye J (2020) A review on generative adversarial networks: algorithms, theory, and applications. [cs, stat], arXiv: 2001.06937"},{"key":"9834_CR28","doi-asserted-by":"crossref","unstructured":"Abusitta A, Wahab OA, Fung BCM (2021) Virtualgan: Reducing mode collapse in generative adversarial networks using virtual mapping. In: 2021 International joint conference on neural networks (IJCNN), pp 1\u20136","DOI":"10.1109\/IJCNN52387.2021.9533656"},{"key":"9834_CR29","unstructured":"Wang YA (2020) Mathematical introduction to generative adversarial nets (GAN). [cs, math, stat], arXiv: 2009.00169"},{"issue":"24","key":"9834_CR30","doi-asserted-by":"publisher","first-page":"18271","DOI":"10.1007\/s00521-020-04954-z","volume":"32","author":"K Liu","year":"2020","unstructured":"Liu K, Qiu G (2020) Lipschitz constrained GANs via boundedness and continuity. Neural Comput Appl 32(24):18271\u201318283","journal-title":"Neural Comput Appl"},{"key":"9834_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110972","volume":"149","author":"C Gabriel","year":"2023","unstructured":"Gabriel C, Jeanfranco F, Daniel AG, Jos\u00e9 SD, Anderson R (2023) Cross-Domain Feature learning and data augmentation for few-shot proxy development in oil industry. Appl Soft Comput 149:110972","journal-title":"Appl Soft Comput"},{"key":"9834_CR32","unstructured":"McInnes L, Healy J, Melville J (2020) UMAP: Uniform manifold approximation and projection for dimension reduction. [cs, stat], arXiv: 1802.03426"},{"key":"9834_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110972","volume":"149","author":"G Cirac","year":"2023","unstructured":"Cirac G, Farfan J, Avansi GD, Schiozer DJ, Rocha A (2023) Cross-Domain Feature learning and data augmentation for few-shot proxy development in oil industry. Appl Soft Compu 149:110972","journal-title":"Appl Soft Compu"},{"key":"9834_CR34","doi-asserted-by":"crossref","unstructured":"Haupt RL (2000) Optimum population size and mutation rate for a simple real genetic algorithm that optimizes array factors. In: IEEE antennas and propagation society international symposium. transmitting waves of progress to the next millennium. 2000 Digest. Held in conjunction with: USNC\/URSI National Radio Science Meeting (C, vol\u00a02, pp 1034\u20131037","DOI":"10.1109\/APS.2000.875398"},{"issue":"17","key":"9834_CR35","doi-asserted-by":"publisher","first-page":"5792","DOI":"10.3390\/app10175792","volume":"10","author":"B Petrovska","year":"2020","unstructured":"Petrovska B, Atanasova-Pacemska T, Corizzo R, Mignone P, Lameski P, Zdravevski E (2020) Cross-domain feature learning and data augmentation for few-shot proxy development in oil industry. Appl Sci 10(17):5792","journal-title":"Appl Sci"},{"issue":"1","key":"9834_CR36","doi-asserted-by":"publisher","first-page":"8","DOI":"10.3390\/fi13010008","volume":"13","author":"Sagar Kora Venu and Sridhar Ravula","year":"2020","unstructured":"Sagar Kora Venu and Sridhar Ravula (2020) Evaluation of deep convolutional generative adversarial networks for data augmentation of chest x-ray images. Future Internet 13(1):8","journal-title":"Future Internet"},{"key":"9834_CR37","unstructured":"Karras T, Aila T, Laine S, Lehtinen J (2018) Progressive growing of GANs for improved quality, stability, and variation. [cs, stat], arXiv: 1710.10196"},{"issue":"4","key":"9834_CR38","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1109\/TETCI.2020.2991774","volume":"4","author":"Z Pan","year":"2020","unstructured":"Pan Z, Weijie Yu, Wang B, Xie H, Sheng VS, Lei J, Kwong S (2020) Loss functions of generative adversarial networks (GANs): opportunities and challenges. IEEE Trans Emerg Top Comput Intell 4(4):500\u2013522","journal-title":"IEEE Trans Emerg Top Comput Intell"},{"issue":"5","key":"9834_CR39","doi-asserted-by":"publisher","first-page":"2314","DOI":"10.3390\/app11052314","volume":"11","author":"A Jierula","year":"2021","unstructured":"Jierula A, Wang S, Tae-Min O, Wang P (2021) Study on accuracy metrics for evaluating the predictions of damage locations in deep piles using artificial neural networks with acoustic emission data. Appl Sci 11(5):2314","journal-title":"Appl Sci"},{"key":"9834_CR40","unstructured":"Kumbure MM, Luukka P (2021) A generalized fuzzy k-nearest neighbor regression model based on Minkowski distance. Granular Comput"},{"issue":"03","key":"9834_CR41","doi-asserted-by":"publisher","first-page":"795","DOI":"10.2118\/214688-PA","volume":"26","author":"C Maschio","year":"2023","unstructured":"Maschio C, Avansi GD, Schiozer DJ (2023) Data assimilation using principal component analysis and artificial neural network. SPE Reser Eval Eng 26(03):795\u2013812","journal-title":"SPE Reser Eval Eng"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09834-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09834-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09834-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T10:15:52Z","timestamp":1724062552000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09834-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,8]]},"references-count":41,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["9834"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09834-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,8]]},"assertion":[{"value":"15 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 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"}}]}}