«Automation and Informatization of the fuel and energy complex»

Estimation of reservoir filtration parameters using production data without shutting down wells for pressure transient testing

UDC: 622.276.3:532.546:004.942
DOI: -

Authors:

¹ National University of Oil and Gas "Gubkin University", Moscow, Russia

Keywords: pressure transient testing, reservoir filtration properties, superposition method, generalization of the Horner method, input data processing

Annotation:

The article presents a method for assessing reservoir filtration properties based on production data without shutting down wells for conducting hydrodynamic studies. In case of a rather long-term period of multiphase production, estimates are proposed not only for permeability, initial reservoir pressure and skin factor change, but also phase permeability. The proposed approach is a generalization of the well-known Horner method. The calculations are based on daily production data for liquid, gas, water cut and gas factor as well as bottom hole pressure, previously filtered using the DBSCAN method. Examples of calculations for two wells are given, in the first case well products are characterized by a high gas factor, in the second one – by a large water cut. The change of the medium permeability for a multiphase flow depending on water cut and gas factor is shown.

Bibliography:

1. Ipatov A.I., Kremenetskiy M.I. Gidrodinamicheskie i promyslovo-tekhnologicheskie issledovaniya skvazhin. – M.: MAKS Press, 2008. – 476 s.
2. Podzemnaya gidromekhanika / K.S. Basniev, N.M. Dmitriev, R.D. Kanevskaya, V.M. Maksimov. – M.-Izhevsk: In-t komp'yuter. issled., 2005. – 496 s.
3. Interesnye algoritmy klasterizatsii. Chast' vtoraya: DBSCAN. – 2017. – URL: https://habr.com/ru/articles/322034/ (data obrashcheniya 01.04.2023).
4. Podkhody mashinnogo obucheniya bez prismotra dlya obnaruzheniya vybrosov vo vremennykh ryadakh s ispol'zovaniem Python. – 2019. – URL: https://machinelearningmastery.ru/unsupervised-machine-learning-approaches-for-outlier-detection-in-time-series-using-python-5759c6394e19 (data obrashcheniya 01.05.2023).
5. Scikit-learn. – URL: https://scikit-learn.org/stable/index.html (data obrashcheniya 01.05.2023).