Desarrollo de modelos estadísticos multinivel para determinar la presión de flujo de fondo en operación de pozos petroleros
DOI:
https://doi.org/10.54139/revinguc.v28i1.3Palabras clave:
pozo de producción, presión de flujo del fondo del pozo, técnicas de determinación de BHFP, modelo estadístico multivariable, modelado multinivel.Resumen
Una de las principales tareas del monitoreo de las operaciones de los pozos de producción es determinar la presión de flujo del fondo del pozo. La inmensa mayoría de los pozos en Perm krai reciben servicio mediante bombas de pozo, lo que dificulta la toma de mediciones directas de la presión de flujo del fondo del pozo. La presión de flujo de fondo en estos pozos se determina muy a menudo recalculando los parámetros medidos en la boca del pozo (presión anular, profundidad del nivel de fluido dinámico). El recálculo se realiza mediante procedimientos basados en la determinación analítica de las características de la mezcla gas-líquido en el pozo, que es muy inconsistente de realizar debido al complejo comportamiento de la mezcla. Este artículo propone un enfoque esencialmente diferente para las mediciones de la presión de flujo del fondo del pozo, que se basa en el procesamiento matemático de los hallazgos de más de 4000 investigaciones paralelas de boca y profundidad de los pozos de producción de petróleo de una gran región de producción de petróleo. Como resultado, se elaboran modelos matemáticos multivariados que permiten determinar de manera confiable la presión de flujo de fondo de los pozos productores de petróleo en operación.
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L. Nazarova and E. Nechaeva, "The analysis of influence of bottomhole pressure decrease under saturation pressure on the oil recovery," Oil Industry, no. 1, pp. 83-85, 2014.
Y. Kashnikov and S. Yakimov, "Geomechanical and hydrodynamic estimation of the bottom-hole pressure influence on the well performance," Oil and Gas Business, vol. 11, pp. 111-115, 2019. https://doi.org/10.24887/0028-2448-2019-11-111-115
T. Bikmukhametov and J. Jä¨schke, "Oil Production Monitoring using Gradient Boosting Machine Learning Algorithm," IFAC-Papers online, vol. 52, no. 1, pp. 514-519, 2019. https://doi.org/10.1016/j.ifacol.2019.06.114
S. Natarajan, K. Ghosh, and R. Srinivasan, "Collaborative Multi - Agent based Process Monitoring System for Offshore Oil and Gas Production. Comput," Computed Aided Chemical Engineering, vol. 27, pp. 1227-1232, 2009. https://doi.org/10.1016/S1570-7946(09)70595-4
D. Martyushev, "Determination of the rational bottomhole pressure of producing wells in the development of carbonate reservoirs," Drilling and oil, no. 11, pp. 22-24, 2014.
D. Martyushev and V. Mordvinov, "Change in the flow rate of wells in an oil and gas condensate field with a decrease in reservoir and bottomhole pressures," Oil Industry, no. 167-69, 2014.
A. Codas, B. Ordoñez, and U. Moreno, "Sucker-Rod Pumping System Fault Detection and Isolation Method Using Bottom Hole Pressure Measurement," IFAC Proccedings Volumes, vol. 42, no. 8, pp. 1031-1036, 2009. https://doi.org/10.3182/20090630-4-ES-2003.00170
V. Mordvinov, A. Lekomtsev, and D. Martyushev, "Determination of pressure at the intake of electric centrifugal pumps when pumping out low-foam carbonated oil," Oil industry, no. 61-63, 2014.
M. Ali Ahmadi, M. Galedarzadeh, and S. Reza-Shadizadeh, "Low parameter model to monitor bottom hole pressure in vertical multiphase flow in oil production wells," Petroleum, vol. 2, no. 3, pp. 258-266, 2016. https://doi.org/10.1016/j.petlm.2015.08.001
E. Levitina, "Influence of density gas liquid phase on parameters of well pressure," Oil and Gas Stud, no. 1, pp. 35-40, 2010.
H. Yang, J. Li, G. Liu, X. Xing, H. Jiang, and C. Wang, "The effect of interfacial mass transfer of slip-rising gas bubbles on two-phase flow in the vertical wellbore/pipeline," International Journal of heat and mass transfer, vol. 150, no. 119326, 2020. https://doi.org/10.1016/j.ijheatmasstransfer.2020.119326
A. Hasan and C. Kabir, "A Study of Multiphase Flow Behavior in Vertical Wells," SPE Production Engineering, vol. 3, no. 2, pp. 263-272, 1988. https://doi.org/10.2118/15138-PA
J. Brill and H. Mukherjee, Multiphase Flow in Wells. Richardson: SPE, 1999.
A. Lekomtsev, E. Zhelanov, and I. Chernykh, "Statistical approach to the evaluation of bottomhole pressure in producing wells," Oil and Gas Business, vol. 10, pp. 98-101, 2016.
A. Lekomtsev and D. Martyushev, "Comparative analysis of methods for determining BHP during well test," Oil Industry, vol. 6, 2014.
S. Bikbulatov and A. Pashali, "Analisys and selection of methods for calculating the pressure gradient in the wellbore," Oil and Gas Business, vol. 21, no. 2, pp. 1-12, 2005.
I. Chernykh, V. Galkin, and I. Ponomareva, "Comparative analysis of the methods for defining bottom hole pressure at well operation of Shershnevsky field," A Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, vol. 328, no. 8, pp. 41-47, 2017.
V. Galkin, I. Ponomareva, I. Chernykh, and E. Filippov, "Methodology for estimating downhole pressure using multivariate model," Oil Industry, vol. 1, pp. 40-43, 2019. https://doi.org/10.24887/0028-2448-2019-1-40-43
D. Martyushev and Y. Slushkina A, "Assessment of informative value in determination of reservoir filtration parameters based on interpretation of pressure stabilization curves," Bulletin of the Tomsk Politechnic University, vol. 330, no. 9, pp. 26-32, 2019.
M. Nait Amar, N. Zeraibi, and K. Redouane, "Bottom hole pressure estimation using hybridization neural networks and grey wolves optimization," Petroleum, vol. 4, no. 4, pp. 419-429, 2018. https://doi.org/10.1016/j.petlm.2018.03.013
W. Chen, Q. Di, F. Ye, J. Zhang, and W. Wang, "Flowing bottomhole pressure prediction for gas wells based on support vector machine and random samples selection," International Journal of Hydrogen Energy, vol. 42, no. 29, pp. 18 333-18 342, 2017. https://doi.org/10.1016/j.ijhydene.2017.04.134
A. Sánchez-Fernández, F. Baldán, G. Sainz-Palmero, J. Benítez, and M. Fuente, "Fault detection based on time series modeling and multivariate statistical process control," Chemometrics and Intelligent Laboratory Systems, vol. 118, pp. 57-69, 2018. https://doi.org/10.1016/j.chemolab.2018.08.003
C. Kumar Singha, A. Kumar, S. Shashtrib, A. Kumar, P. Kumar, and J. Mallick, "Multivariate statistical analysis and geochemical modeling for geochemical assessment of groundwater of Delhi, India," Journal of Geochemical Exploration, vol. 175, pp. 59-71, 2017. https://doi.org/10.1016/j.gexplo.2017.01.001
D. Cecconet, S. Bolognesi, S. Daneshgar, A. Callegari, and A. Capodaglio, "Improved process understanding and optimization by multivariate statistical analysis of Microbial Fuel Cells operation," International Journal of Hydrogen Energy, vol. 43, no. 34, pp. 16 719-16 727, 2018. https://doi.org/10.1016/j.ijhydene.2018.07.056
J. Hua, J. Li, M. Ouyang, L. Lu, and L. Xu, "Proton exchange membrane fuel cell system diagnosis based on the multivariate statistical method," International Journal of Hydrogen Energy, vol. 36, no. 16, pp. 9896-9905, 2011. https://doi.org/10.1016/j.ijhydene.2011.05.075
S. Barkovskiy, V. Zakharov, A. Lukashov, and A. Nurutdinova, Multidimensional Data Analysis by Applied Statistics Techniques. Kazan: KSTU, 2010.
R. Kissell and J. Poserina, Optimal Sports Math, Statistics, and Fantasy. Academic Press, 2017. [28] E. Wentzel, Operations Research. Moscow: Mir Publishers, 1983.
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