v. 36 n. 3 (2023): Revista ION
Artigos

Viabilidade técnico-econômica de implementação de um sensor virtual para produção em poços de um campo no Valle Medio del Magdalena na Colômbia

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  • Giovanni Vizcaya-Cedeño ,
  • Fernando Enrique Calvete González,
  • Giovanni Morales Medina
Giovanni Vizcaya-Cedeño
ECOPETROL S.A.
Fernando Enrique Calvete González
Universidad Industrial de Santander
Giovanni Morales Medina
Universidad Industrial de Santander
DOI: https://doi.org/10.18273/revion.v36n3-2023006

Publicado 2023-12-12

Palavras-chave

  • Sensor virtual,
  • Rede neural artificial,
  • Produção de poço de petróleo,
  • Valle Medio del Magdalena,
  • Neuralnet,
  • CAPEX
    • ...Mais
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Como Citar

Vizcaya-Cedeño , G. ., Calvete González, F. E. ., & Morales Medina, G. (2023). Viabilidade técnico-econômica de implementação de um sensor virtual para produção em poços de um campo no Valle Medio del Magdalena na Colômbia. REVISTA ION, 36(3), 63–74. https://doi.org/10.18273/revion.v36n3-2023006

Copyright (c) 2023 Giovanni Vizcaya-Cedeño , Fernando Enrique Calvete González, Giovanni Morales Medina

Creative Commons License
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.

Resumo

Os profissionais de produção utilizam dados de desempenho de poços provenientes de sensores físicos para identificar poços que requerem manutenção corretiva para normalizar os fluxos de produção. O anterior corresponde ao apoio à concretização dos objetivos corporativos. No entanto, os custos dos sensores físicos podem restringir o acesso às informações necessárias em tempo real. Para isso, sensores virtuais (SV) surgem como uma alternativa de baixo custo, que podem prever fluxos de produção, com base em dados disponíveis de sensores físicos e em procedimentos embarcados em redes neurais artificiais (RNA). Apresentamos aqui uma análise de viabilidade técnico-econômica da implantação de um SV de produção em poços com sistema de elevação artificial Cavidades Progressivas (PCP) em um campo no Valle Medio del Magdalena da Colômbia (VMM). Para isso, foram utilizados dados de produção com 15 variáveis no treinamento e validação de diferentes arquiteturas de RNA, conforme os códigos da biblioteca Neuralnet do ambiente de software livre R. Da mesma forma, foi realizada uma avaliação do impacto econômico derivado da implementação do VS em poços do campo VMM é divulgado. De acordo com os resultados, a RNA com camada interna de 23 neurônios e funções de ativação logística relatou o melhor desempenho de predição, com erros de ± 9,6 com 95 % de confiança. Por outro lado, a aplicação de um VS baseado no RNA para os poços com PCP do campo VMM levaria a um benefício econômico favorável, com valor presente líquido de $25,5 MMusd, considerando um fluxo de caixa de 10 anos.

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Referências

  1. Doğan B, Erol D. The Future of Fossil and Alternative Fuels Used in Automotive Industry. En: 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT); 2019 October 11-13; Ankara, Turkey; IEEE; 2019. doi.org/10.1109/ISMSIT.2019.8932925
  2. Kreps BH. The Rising Costs of Fossil-Fuel Extraction: An Energy Crisis That Will Not Go Away. Am. J. Econ. Sociol. 2020;79(3):695–717. doi.org/10.1111/ajes.12336
  3. Kremieniewski M. Improving the Efficiency of Oil Recovery in Research and Development. Energies. 2022;15(2):4488. doi.org/10.3390/en15124488
  4. Soares FDV, Secchi AR, de Souza MBJr. Development of a nonlinear model predictive control for stabilization of a gas-lift oil well. Ind. Eng. Chem. Res. 2022;61(24):8411–8421. doi.org/10.1021/acs. iecr.1c04728
  5. García A, Almeida I, Singh G, Purwar S, Monteiro M, Carbone L, et al. An Implementation of On-line Well Virtual Metering of Oil Production. En: SPE Intelligent Energy Conference and Exhibition; 2010 March 23-25; Utrecht, The Netherlands; OnePetro; 2010. doi.org/10.2118/127520-ms
  6. Amin A. Evaluation of Commercially Available Virtual Flow Meters (VFMs). En: Offshore Technology Conference; 2015 May 4-7; Houston, Texas; OnePetro; 2015. doi.org/10.4043/25764-ms
  7. Bravo CE, Saputelli L, Rivas F, Pérez AG, Nickolaou M, Zangl G, et al. State of the Art of Artificial Intelligence and Predictive Analytics in the E&P Industry: A Technology Survey. SPE J. 2014;19(04):547–563. doi.org/10.2118/150314-pa
  8. Varyan R, Haug RK, Fonnes DG. Investigation on the Suitability of Virtual Flow Metering System as an Alternative to the Conventional Physical Flow Meter. En: SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition; 2015 October 20-22; Nusa Dua, Bali, Indonesia; OnePetro; 2015. doi.org/10.2118/176432-ms
  9. Varyan R. Cost Saving of Implementing Virtual Flow Metering at Various Fields and Engineering Phases - A Case Study. En: Offshore Technology Conference Asia; 2016 March 22-25; Kuala Lumpur, Malaysia; OnePetro; 2016. doi.org/10.4043/26637-ms
  10. Bikmukhametov T, Jäschke J. First principles and machine learning Virtual Flow Metering: A literature review. J. Pet. Sci. Eng. 2020;184:106487. doi.org/10.1016/j.petrol.2019.106487
  11. Hansen LS, Pedersen S, Durdevic P. MultiPhase Flow Metering in Offshore Oil and Gas Transportation Pipelines: Trends and Perspectives. Sensors. 2019;19(9):2184. doi.org/10.3390/s19092184
  12. Ursini F, Rossi R, Castelnuovo L, Perrone A, Bendari A, Pollero M. The Benefits of Virtual Meter Applications on Production Monitoring and Reservoir Management. En: SPE Reservoir Characterisation and Simulation Conference and Exhibition; 2019 September 17-19; Abu Dhabi, UAE; OnePetro; 2019. doi.org/10.2118/196654-ms
  13. George A. Predicting Oil Production Flow Rate Using Artificial Neural Networks - The Volve Field Case. En: SPE Nigeria Annual International Conference and Exhibition; 2021 August 2-4; Lagos, Nigeria; OnePetro; 2021. doi.org/10.2118/208258-MS
  14. Bello O, Ade-Jacob S, Yuan K. Development of Hybrid Intelligent System for Virtual Flow Metering in Production Wells. En: SPE Intelligent Energy Conference & Exhibition; 2014 April 1-3; Utrecht, The Netherlands; OnePetro; 2014. doi.org/10.2118/167880-MS
  15. Cai S, Toral H, Sinta D, Tajak M. Experience in field tuning and operation of a multiphase meter based on neural net characterization of flow conditions FLOMEKO. En: Proc. 12th Int. Conf. on Flow Measurement; 2004 September 14-17; Guilin, China; 2004.
  16. Mirzaei-Paiaman A, Salavati S. The Application of Artificial Neural Networks for the Prediction of Oil Production Flow Rate. Energ. Source Part A. 2010;34(19):1834-1843. doi.org/10.1080/15567036.2010.492386
  17. Al-Jasmi AK, Goel HK, Nasr H, Querales M, Rebeschini J, Villamizar MA, et al. ShortTerm Production Prediction in Real Time Using Intelligent Techniques. En: EAGE Annual Conference & Exhibition Incorporating SPE Europ; 2013 June 10-13; London, UK; OnePetro; 2013. doi.org/10.2118/164813-ms
  18. Ahmadi MA, Ebadi M, Shokrollahi A, Majidi SMJ. Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir. Appl. Soft Comput. 2013;13(2):1085-1098. doi.org/10.1016/j.asoc. 2012.10.009
  19. Liu G, Wang L, Qu H, Shen H, Zhang X, Zhang S, et al. Artificial neural network approaches on composition–property relationships of jet fuels based on GC–MS. Fuel. 2007;86(16):2551-2559. doi.org/10.1016/j.fuel.2007.02.023
  20. Berry MJA, Linoff G. Data mining techniques. Nueva York: John Wiley & Sons; 1987.
  21. Swingler K. Applying Neural Networks, A Practical Guide. London: Press Limited Oval Road; 1996.
  22. Zhang Z. Neural Networks: further insights into error function, generalized weights and others. Ann Transl Med. 2016;4(16):300. doi.org/10.21037/atm.2016.05.37
  23. Thorn R, Johansen GA, Hjertarek BT. Threephase flow measurement in the petroleum industry. Meas. Sci. Technol. 2013;24:012003. doi.org/10.1088/0957-0233/24/1/012003
  24. Carvajal G, Maucec M, Cullick S. Intelligent digital oil and gas fields. Concepts, collaboration, and Right-Time Decisions. USA: Gulf Professional Publishing, Elsevier; 2018.
  25. Kluth ELE, Varnham MP, Clowes JR, Kutlik RL, Crawley CM, Heming RF. Advanced Sensor Infrastructure for Real Time Reservoir Monitoring. En: SPE European Petroleum Conference; 2000 October 24-25; Paris, France; OnePetro; 2000. doi.org/10.2118/65152-MS
  26. Kemp CE, Ravikumar AP, Brandt AR. Comparing Natural Gas Leakage Detection Technologies Using an Open-Source “Virtual Gas Field” Simulator. Environ. Sci. Technol. 2016;50(8):4546−4553. doi.org/10.1021/acs.est.5b06068
  27. Khan MY. Theory & Problems in Financial Management. Boston: McGraw Hill Higher Education; 1999.
  28. Carmalt SW. The Economics of Oil. A primer Including Geology, Energy, Economics, Politics. Switzerland: Springer; 2017.
  29. Brealey R, Myers S. Principles of Corporate Finance. New York: McGraw-Hill; 2000.
  30. Isasi P, Galvan I. Redes neuronales artificiales: enfoque práctico. Madrid: Pearson; 2004.
  31. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. New York: Springer Inc; 2009.
  32. Sadri M, Shariatipour S, Hunt A, Ahmadinia M. Effect of systematic and random flow measurement errors on history matching: a case study on oil and wet gas reservoirs. J Petrol Explor Prod Technol 2019;9:2853–2862. doi.org/10.1007/s13202-019-0665-2
  33. Falcone G, Hewitt GF, Alimonti C, Harrison B. Multiphase Flow Metering: Current Trends and Future Developments. J. Pet. Technol. 2002;54(04):77–84. doi.org/10.2118/74689-jpt
  34. Andrade GMP, de Menezes DQF, Soares RM, Lemos TSM, Teixeira AF, Ribeiro LD, et al. Virtual flow metering of production flow rates of individual wells in oil and gas platforms through data reconciliation, J. Pet. Sci. Eng. 2022;208(Part E):109772. doi.org/10.1016/j.petrol.2021.109772
  35. Gevrey M, Dimopoulus I, Lek S. Review and comparison of methods to study to contribution of variables in artificial neural network models. Ecol. Modell. 2003;160(3):349-264. doi.org/10.1016/S0304-3800(02)00257-0
  36. Luíza da Costa N, Dias de Lima M, Barbosa R. Evaluation of feature selection methods based on artificial neural network weights. Expert Syst. Appl. 2021;168:114312. doi.org/10.1016/j.eswa.2020.114312
  37. Zuluaga-Álvarez W.J. Evaluación económica de la actualización del sistema de monitoreo remoto en los pozos con inyección de agua, bloques I y II, del campo Casabe (Tesis de maestría). Bucaramanga, Colombia: Universidad Industrial de Santander; 2020.