Geração de Perfis Sônicos Sintéticos para Poços Perfurados na Bacia de Sergipe: Uma Análise de Linearidade Usando Regressão
Resumo
A predição de perfis sônicos é essencial na indústria petrolífera, pois permite estimar propriedades geológicas sem medições diretas, muitas vezes caras ou inviáveis. Este trabalho aplica regressão linear para gerar perfis sônicos sintéticos com base em outros perfis geofísicos da base de dados da ANP na Bacia Sergipe Terrestre. A metodologia utiliza correlação de Pearson para selecionar variáveis relevantes à predição. O modelo obteve r2 de 0,77, indicando sua viabilidade para reduzir custos ao substituir medições diretas por predições estatísticas.
Referências
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Akinyemi, O. D., Elsaadany, M., Siddiqui, N. A., Elkurdy, S., Olutoki, J. O., and Islam, M. M. (2023). Machine learning application for prediction of sonic wave transit time-a case of niger delta basin. Results in Engineering, 20:101528.
Alhelfia, L. M., Ali, H. M., and Ahmed, S. H. (2021). P-wave sonic log predictive modeling with optimal artificial neural networks topology. Journal of Al-Qadisiyah for computer science and mathematics, 13(3):Page–142.
Archie, G. E. (1950). Introduction to petrophysics of reservoir rocks. AAPG bulletin, 34(5):943–961.
Augusto, F. d. O. A. and Martins, J. L. (2009). A well-log regression analysis for p-wave velocity prediction in the namorado oil field, campos basin. Revista Brasileira de Geofisica, 27:595–608.
Cao, J., Shi, Y., Wang, D., and Zhang, X. (2017). Acoustic log prediction on the basis of kernel extreme learning machine for wells in gjh survey, erdos basin. Journal of Electrical and Computer Engineering, 2017(1):3824086.
Cranganu, C. and Breaban, M. (2013). Using support vector regression to estimate sonic log distributions: a case study from the anadarko basin, oklahoma. Journal of Petroleum Science and Engineering, 103:1–13.
Ellis, D. V. and Singer, J. M. (2007). Well logging for earth scientists, volume 692. Springer.
Gressling, T. (2020). Data Science in chemistry: artificial intelligence, big data, chemometrics and quantum computing with jupyter. Walter de Gruyter GmbH & Co KG.
Montgomery, D. C., Peck, E. A., and Vining, G. G. (2021). Introduction to linear regression analysis. John Wiley & Sons.
Nero, C., Aning, A. A., Danuor, S. K., and Mensah, V. (2023). Prediction of compressional sonic log in the western (tano) sedimentary basin of ghana, west africa using supervised machine learning algorithms. Heliyon, 9(9).
Ogbamikhumi, A., Salami, S., and Uwadiae, W. (2020). Neural network prediction of p-wave log for reservoir characterization in the niger delta basin. Nigerian Journal of Technological Development, 17(1):28–32.
Pratikna, K., Rahman, M., Torabi, A., and Mondol, N. (2022). Machine learning application for compressional wave velocity log prediction in sleipner co2 storage, offshore norway. In 83rd EAGE Annual Conference & Exhibition, volume 2022, pages 1–5. European Association of Geoscientists & Engineers.
Selley, R. C. (1998). Elements of petroleum geology. Gulf Professional Publishing.
SOUSA, A. (2024). Validação externa dos agrupamentos de eletrofácies com aprendizado não supervisionado utilizando litologias interpretadas em poços de petróleo. 107 páginas.
Tissot, B. P. and Welte, D. H. (2013). Petroleum formation and occurrence. Springer Science & Business Media.
Akinyemi, O. D., Elsaadany, M., Siddiqui, N. A., Elkurdy, S., Olutoki, J. O., and Islam, M. M. (2023). Machine learning application for prediction of sonic wave transit time-a case of niger delta basin. Results in Engineering, 20:101528.
Alhelfia, L. M., Ali, H. M., and Ahmed, S. H. (2021). P-wave sonic log predictive modeling with optimal artificial neural networks topology. Journal of Al-Qadisiyah for computer science and mathematics, 13(3):Page–142.
Archie, G. E. (1950). Introduction to petrophysics of reservoir rocks. AAPG bulletin, 34(5):943–961.
Augusto, F. d. O. A. and Martins, J. L. (2009). A well-log regression analysis for p-wave velocity prediction in the namorado oil field, campos basin. Revista Brasileira de Geofisica, 27:595–608.
Cao, J., Shi, Y., Wang, D., and Zhang, X. (2017). Acoustic log prediction on the basis of kernel extreme learning machine for wells in gjh survey, erdos basin. Journal of Electrical and Computer Engineering, 2017(1):3824086.
Cranganu, C. and Breaban, M. (2013). Using support vector regression to estimate sonic log distributions: a case study from the anadarko basin, oklahoma. Journal of Petroleum Science and Engineering, 103:1–13.
Ellis, D. V. and Singer, J. M. (2007). Well logging for earth scientists, volume 692. Springer.
Gressling, T. (2020). Data Science in chemistry: artificial intelligence, big data, chemometrics and quantum computing with jupyter. Walter de Gruyter GmbH & Co KG.
Montgomery, D. C., Peck, E. A., and Vining, G. G. (2021). Introduction to linear regression analysis. John Wiley & Sons.
Nero, C., Aning, A. A., Danuor, S. K., and Mensah, V. (2023). Prediction of compressional sonic log in the western (tano) sedimentary basin of ghana, west africa using supervised machine learning algorithms. Heliyon, 9(9).
Ogbamikhumi, A., Salami, S., and Uwadiae, W. (2020). Neural network prediction of p-wave log for reservoir characterization in the niger delta basin. Nigerian Journal of Technological Development, 17(1):28–32.
Pratikna, K., Rahman, M., Torabi, A., and Mondol, N. (2022). Machine learning application for compressional wave velocity log prediction in sleipner co2 storage, offshore norway. In 83rd EAGE Annual Conference & Exhibition, volume 2022, pages 1–5. European Association of Geoscientists & Engineers.
Selley, R. C. (1998). Elements of petroleum geology. Gulf Professional Publishing.
SOUSA, A. (2024). Validação externa dos agrupamentos de eletrofácies com aprendizado não supervisionado utilizando litologias interpretadas em poços de petróleo. 107 páginas.
Tissot, B. P. and Welte, D. H. (2013). Petroleum formation and occurrence. Springer Science & Business Media.
Publicado
12/08/2025
Como Citar
CARVALHO, José Gabriel da S.; SIMON, Vitor Hugo; GARCIA, Rodolfo B. de B..
Geração de Perfis Sônicos Sintéticos para Poços Perfurados na Bacia de Sergipe: Uma Análise de Linearidade Usando Regressão. In: ESCOLA REGIONAL DE COMPUTAÇÃO BAHIA, ALAGOAS E SERGIPE (ERBASE), 25. , 2025, Lagarto/SE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 298-307.
DOI: https://doi.org/10.5753/erbase.2025.13784.
