Identificação de Estáticas em Poços de Petróleo utilizando Motifs
Resumo
A identificação de estáticas é uma etapa da análise de dano de formação em poços de petróleo considerada trabalhosa e susceptível a erros. Neste artigo é discutida uma abordagem para esta etapa baseada na busca por Motifs em séries temporais de dados de pressão de fundo de poço. No estudo foram avaliados três métodos distintos: um algoritmo de detecção de Motifs puro e combinações do algoritmo de detecção de Motifs com o método da Derivada e com o método do Filtro de Convolução. Os testes foram realizados com dez conjuntos de dados sintéticos. O método que obteve o melhor desempenho na métrica F0.5 foi o algoritmo de detecção de Motifs combinado com o método da derivada.
Referências
ANP (2022). Anuário estatístico brasileiro do petróleo, gás natural e biocombustíveis 2022.
Athichanagorn, S., Horne, R. N., Kikani, J., et al. (1999). Processing and interpretation of long-term data from permanent downhole pressure gauges. In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers.
Bennion, D. B. et al. (2002). An overview of formation damage mechanisms causing a reduction in the productivity and injectivity of oil and gas producing formations. Journal of Canadian Petroleum Technology, 41(11).
Chang, O., Pan, Y., Dastan, A., Teague, D., Descant, F., et al. (2019). Application of machine learning in transient surveillance in a deep-water oil field. In SPE Western Regional Meeting. Society of Petroleum Engineers.
Civan, F. (2016). Reservoir formation damage: fundamentals, modeling, assessment, and mitigation. Gulf Professional Publishing, an imprint of Elsevier.
Fair, C., Flores, R., Hakim, B., Nande, S., et al. (2014). Using the results from automated petroleum engineering calculations to accelerate decision workflows. In SPE Asia Pacific Oil & Gas Conference and Exhibition. Society of Petroleum Engineers.
Haddad, S., Proano, E., Patel, Y., et al. (2004). A method to diagnose depletion, skin, and drive mechanism effects using reservoir monitoring data. In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers.
Hampel, F. R. (1974). The influence curve and its role in robust estimation. Journal of the american statistical association, 69(346):383–393.
Holicek, R., Adachi, J., Viloria, L., Mese, A., and Traore, Y. (2008). Surveillance and diagnostics of permanent bottomhole gauge data coupled with geomechanical modeling to identify sources of formation damage. volume All Days of U.S. Rock Mechanics/Geomechanics Symposium. ARMA-08-322.
Houze, O., Viturat, D., Fjaere, O. S., et al. (2020). Dynamic Data Analysis: The Theory and Practice of Pressure Transient, Rate Transient Analysis, Formation Testing, Production Logging and the use of Permanent Downhole, volume v5.30. Kappa Engineering, Paris.
Houze, O. P., Allain, O. F., Josso, B., et al. (2011). New methods enhance the processing of permanent-gauge data. In SPE Middle East Oil and Gas Show and Conference. Society of Petroleum Engineers.
Kodinariya, T. M. and Makwana, P. R. (2013). Review on determining number of cluster in k-means clustering. International Journal, 1(6):90–95.
Law, S. M. (2019). STUMPY: A Powerful and Scalable Python Library for Time Series Data Mining. The Journal of Open Source Software, 4(39):1504.
Lin, J., Keogh, E., Lonardi, S., and Patel, P. (2002). Finding motifs in time series. In Proceedings of Workshop on Temporal Data Mining, 2002, pages 53–68.
Mueen, A., Keogh, E., Zhu, Q., Cash, S., and Westover, B. (2009). Exact discovery of time series motifs. In Proceedings of the 2009 SIAM international conference on data mining, pages 473–484. SIAM.
Olsen, S., Nordtvedt, J.-E., et al. (2005). Automatic filtering and monitoring of real-time reservoir and production data. In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers.
Proano, E., Patel, Y., Thawer, R., Al-Harthy, S., Gadiyar, B., John, C., Morales, H., et al. (2004). Life-of-well approach for optimizing production-gom case histories. In SPE International Symposium and Exhibition on Formation Damage Control. Society of Petroleum Engineers.
Rossi, D. F., Komati, K. S., and da Costa, M. C. B. (2021). Identificaçao de estáticas em poços de petróleo via sensor de pressao de fundo. In Simpósio Brasileiro de Automação Inteligente-SBAI, volume 1.
Suzuki, S., Chorneyko, D. M., et al. (2009). Automatic detection of pressure-buildup intervals from permanent downhole pressure data using filter convolution. In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers.
Suzuki, S. et al. (2018). Using similarity-based pattern detection to automate pressure transient analysis. In Abu Dhabi International Petroleum Exhibition & Conference. Society of Petroleum Engineers.
van den Burg, G. J. and Williams, C. K. (2020). An evaluation of change point detection algorithms. arXiv preprint arXiv:2003.06222.
Viberti, D., Verga, F., Delbosco, P. F., et al. (2007). An improved treatment of long-term pressure data for capturing information. SPE Reservoir Evaluation & Engineering, 10(04):359–366.
Yeh, C.-C. M., Zhu, Y., Ulanova, L., Begum, N., Ding, Y., Dau, A., Silva, D., Mueen, A., and Keogh, E. (2016). Matrix profile I: All pairs similarity joins for time series: A unifying view that includes motifs, discords and shapelets. pages 1317–1322.
Zhu, Y., Zimmerman, Z., Senobari, N. S., Yeh, C.-C. M., Funning, G., Mueen, A., Brisk, P., and Keogh, E. (2016). Matrix profile ii: Exploiting a novel algorithm and gpus to break the one hundred million barrier for time series motifs and joins. In 2016 IEEE 16th International Conference on Data Mining (ICDM), pages 739–748.
Athichanagorn, S., Horne, R. N., Kikani, J., et al. (1999). Processing and interpretation of long-term data from permanent downhole pressure gauges. In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers.
Bennion, D. B. et al. (2002). An overview of formation damage mechanisms causing a reduction in the productivity and injectivity of oil and gas producing formations. Journal of Canadian Petroleum Technology, 41(11).
Chang, O., Pan, Y., Dastan, A., Teague, D., Descant, F., et al. (2019). Application of machine learning in transient surveillance in a deep-water oil field. In SPE Western Regional Meeting. Society of Petroleum Engineers.
Civan, F. (2016). Reservoir formation damage: fundamentals, modeling, assessment, and mitigation. Gulf Professional Publishing, an imprint of Elsevier.
Fair, C., Flores, R., Hakim, B., Nande, S., et al. (2014). Using the results from automated petroleum engineering calculations to accelerate decision workflows. In SPE Asia Pacific Oil & Gas Conference and Exhibition. Society of Petroleum Engineers.
Haddad, S., Proano, E., Patel, Y., et al. (2004). A method to diagnose depletion, skin, and drive mechanism effects using reservoir monitoring data. In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers.
Hampel, F. R. (1974). The influence curve and its role in robust estimation. Journal of the american statistical association, 69(346):383–393.
Holicek, R., Adachi, J., Viloria, L., Mese, A., and Traore, Y. (2008). Surveillance and diagnostics of permanent bottomhole gauge data coupled with geomechanical modeling to identify sources of formation damage. volume All Days of U.S. Rock Mechanics/Geomechanics Symposium. ARMA-08-322.
Houze, O., Viturat, D., Fjaere, O. S., et al. (2020). Dynamic Data Analysis: The Theory and Practice of Pressure Transient, Rate Transient Analysis, Formation Testing, Production Logging and the use of Permanent Downhole, volume v5.30. Kappa Engineering, Paris.
Houze, O. P., Allain, O. F., Josso, B., et al. (2011). New methods enhance the processing of permanent-gauge data. In SPE Middle East Oil and Gas Show and Conference. Society of Petroleum Engineers.
Kodinariya, T. M. and Makwana, P. R. (2013). Review on determining number of cluster in k-means clustering. International Journal, 1(6):90–95.
Law, S. M. (2019). STUMPY: A Powerful and Scalable Python Library for Time Series Data Mining. The Journal of Open Source Software, 4(39):1504.
Lin, J., Keogh, E., Lonardi, S., and Patel, P. (2002). Finding motifs in time series. In Proceedings of Workshop on Temporal Data Mining, 2002, pages 53–68.
Mueen, A., Keogh, E., Zhu, Q., Cash, S., and Westover, B. (2009). Exact discovery of time series motifs. In Proceedings of the 2009 SIAM international conference on data mining, pages 473–484. SIAM.
Olsen, S., Nordtvedt, J.-E., et al. (2005). Automatic filtering and monitoring of real-time reservoir and production data. In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers.
Proano, E., Patel, Y., Thawer, R., Al-Harthy, S., Gadiyar, B., John, C., Morales, H., et al. (2004). Life-of-well approach for optimizing production-gom case histories. In SPE International Symposium and Exhibition on Formation Damage Control. Society of Petroleum Engineers.
Rossi, D. F., Komati, K. S., and da Costa, M. C. B. (2021). Identificaçao de estáticas em poços de petróleo via sensor de pressao de fundo. In Simpósio Brasileiro de Automação Inteligente-SBAI, volume 1.
Suzuki, S., Chorneyko, D. M., et al. (2009). Automatic detection of pressure-buildup intervals from permanent downhole pressure data using filter convolution. In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers.
Suzuki, S. et al. (2018). Using similarity-based pattern detection to automate pressure transient analysis. In Abu Dhabi International Petroleum Exhibition & Conference. Society of Petroleum Engineers.
van den Burg, G. J. and Williams, C. K. (2020). An evaluation of change point detection algorithms. arXiv preprint arXiv:2003.06222.
Viberti, D., Verga, F., Delbosco, P. F., et al. (2007). An improved treatment of long-term pressure data for capturing information. SPE Reservoir Evaluation & Engineering, 10(04):359–366.
Yeh, C.-C. M., Zhu, Y., Ulanova, L., Begum, N., Ding, Y., Dau, A., Silva, D., Mueen, A., and Keogh, E. (2016). Matrix profile I: All pairs similarity joins for time series: A unifying view that includes motifs, discords and shapelets. pages 1317–1322.
Zhu, Y., Zimmerman, Z., Senobari, N. S., Yeh, C.-C. M., Funning, G., Mueen, A., Brisk, P., and Keogh, E. (2016). Matrix profile ii: Exploiting a novel algorithm and gpus to break the one hundred million barrier for time series motifs and joins. In 2016 IEEE 16th International Conference on Data Mining (ICDM), pages 739–748.
Publicado
06/08/2023
Como Citar
ROSSI, Daniel Folador; KOMATI, Karin Satie; COSTA, Mateus Conrad Barcellos da.
Identificação de Estáticas em Poços de Petróleo utilizando Motifs. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 50. , 2023, João Pessoa/PB.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 308-319.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2023.230748.
