Modelo en series de tiempo para la tasa de penetración de un pozo de petróleo de referencia: Caso Puerto Boyacá - Colombia
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Keywords
tasa de penetración, proceso de memoria larga, modelo ARFIMA, optimización
Resumen
En este trabajo se identificó un modelo en series de tiempo para el control de la tasa de penetración (ROP) en un pozo de referencia denominado V∗∗∗ que pertenece al campo en desarrollo VEL que está ubicado en la cuenca del Valle del Magdalena Medio (VMM), puntualmente en el municipio de Puerto Boyacá -Colombia. Los valores ajustados por el modelo identificado y su intervalo de confianza del 95 % pueden ser empleados como guía en la perforación de pozos vecinos dentro del mismo campo en desarrollo para disminuir la incertidumbre en los tiempos de operación del proyecto. Se hace necesario hacer un análisis comparativo entre la ROP de diferentes procesos de perforación para verificar si la metodología presentada en este trabajo puede ser empleada en el campo completo.
MSC: 62F86, 62L12, 62M10, 62P30, 80M50
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Referencias
[2] R. Caenn, H. C. H. Darley, and G. R. Gray, Composition and Properties of Drilling and Completion Fluids, 6th ed. 225 Wyman Street, Waltham, MA 02451, USA: Elsevier, 2011.
[3] I. D. R. Bradford, W. A. Aldred, J. M. Cook, E. F. M. Elewaut, J. A. Fuller, T. G. Kristiansen, and T. R. Walsgrove, “When rock mechanics met drilling: Effective implementation of real-time wellbore stability control.” Society of Petroleum Engineers.
[4] G. E. Box, G. M. Jenkins, and G. C. Reinsel, Time Series Analysis: Forecasting and Control, 4th ed. 111 River Street, Hoboken, NJ 07030, USA: John Wiley and Sons, 2008.
[5] E. Castaño, K. Gómez, and S. Gallón, “Una nueva prueba para el parámetro de diferenciación fraccional,” Revista Colombiana de
Estadística, vol. 31, no. 1, pp. 67–84, 2008. [Online]. Available: http://www.bdigital.unal.edu.co/30708/
[6] D. F. Lemus and E. Castaño, “Prueba de hipótesis sobre la existencia de una raíz fraccional en una serie de tiempo no estacionaria,” Lecturas de economía, vol. 78, no. 1, pp. 151–184, 2013.
[7] J. Beran, Statistics for long-memory processes, 1st ed. One Penn Plaza New York, NY 10119: Chapman & Hall, 1994.
[8] V. M. Guerrero, Análisis estadístico de series de tiempo económicas, 2nd ed. International Thomson Editores, S. A. de C. V., 2003. 155
[9] W. W. S. Wei, Time Series Analysis : Univariate and Multivariate Methods, 2nd ed. Addison Wesley Pub Co Inc, 2005.
[10] P. J. Brockwell and R. A. Davis, Time Series: Theory and Methods, 2nd ed. 233 Spring Street, New York, NY 10013, USA: Springer Science + Business Media, 2006.
[11] J. a. Beran, “Maximum likelihood estimation of the differencing parameter for invertible short and long memory autoregressive integrated moving average models,” Journal of the Royal Statistical Society, vol. 57, no. 4, pp. 659–672, 1995.
[12] J. R. M. Hosking, “Fractional differencing,” Biometrika, vol. 68, no. 1, pp. 165–176, 1981.
[13] W. Palma, Long-Memory Time Series. Theory and Methods, 1st ed. 111 River Street, Hoboken, NJ 07030, USA: Jhon Wiley and Sons, Inc., 2007.
[14] D. A. Dickey and W. A. Fuller, “Distribution of the estimators for autoregressive time series with a unit root,” Journal of the American Statistical Association, vol. 74, no. 1, pp. 427–431, 1979.
[15] J. G. MacKinnon, “Critical values for cointegration tests,” 2010. 157 [16] J. G. a. MacKinnon, “Approximate asymptotic distribution functions for unit root and cointegration tests,” Journal of Business and Economic Statistics, vol. 12, no. 2, pp. 167–176, 1994.
[17] P. C. B. Phillips and P. Perron, “Testing for a unit root in time series regression,” Biometrika, vol. 75, no. 2, pp. 335–346, 1988. 157
[18] J. Geweke and S. Porter-Hudak, “The estimation and application of longmemory time series models,” Journal of Time Series Analysis, vol. 4, no. 4, pp. 221–238, 1983.
[19] P. M. Robinson, “Log-periodogram regression of time series with long range dependence,” The Annals of Statistics, vol. 23, no. 3, pp. 1048–1072, 1995.
[20] C. S. Kim and P. C. B. Phillips, “Log periodogram regression: The nonstationary case,” University of Yale, Tech. Rep., 2006. 159
[21] C. S. Kim, “Log periodogram estimation with nonstationary process,” Journal of Economic Theory and Econometrics, vol. 19, no. 3, pp. 1–23, 2008.
[22] P. C. B. Phillips and K. Shimotsu, “Pooled log-periodogram regression,” Journal of Time Series Analysis, vol. 23, no. 1, pp. 57–93, 2002.
[23] D. W. K. Andrews and P. Guggenberger, “A bias-reduced log-periodogram regression estimator for the long-memory parameter,” Econometrica, vol. 71, no. 2, pp. 675–712, 2003.
[24] C. Velasco, “Non-gaussian log-periodogram regression,” Econometric Theory, vol. 16, no. 1, pp. 44–79, 2000.
[25] M. S. Raymond and W. L. Leffler, Oil and Gas Production in Nontechnical Language, 1st ed. 1421 S Sheridan Rd, Tulsa, Oklahoma, Estados Unidos: PennWell Corporation, 2005.
[26] C. J. Wright and R. A. Gallun, Fundamentals of Oil and Gas Accounting, 5th ed. 1421 S Sheridan Rd, Tulsa, Oklahoma, Estados Unidos: PennWell Corporation, 2008.
[27] F. X. Diebold and G. Rudebusch, “On the power of dickey-fuller tests against fractional alternatives,” Economics Letters, vol. 35, no. 2, pp. 155–160, 1991.
[28] U. Hassler and J. Wolters, “On the power of unit root tests against fractional alternatives,” Economics Letters, vol. 45, no. 1, pp. 1–5, 1994.