One of the important tasks of analyzing oil field development is predicting well performance. For this purpose, displacement characteristics are often used, which represent the dependence of some indicators on others. To determine the parameters of these dependencies, regression analysis of historical data is used. Dependences of the choice of watering production wells with water pumped into injection wells, water or the law of the exhausted aquifer. A feature of displacement characteristics is generally considered to be that they can only be used when fluid flows in the formation are established. This is due to the fact that with the classical approach, displacement of characteristics is not observed in the explicit form of well interference. Therefore, the search for displacement characteristics, with the help of which we can talk about the mutual influence of wells, is an important factor. This is the subject of this work. Water cut and water-oil ratio (WOR) are related by a well-known formula. The paper proposes regression models for WOR. They obtained the result taking into account the classical logic of the WOR from accumulated oil production. Water cut is calculated from water saturation. The proposed regression models of water saturation are based on the analysis of equations of theories of two-phase filtration in difference form. 11 watering models were studied, two including classical ones and 9 new ones. Dependencies for reservoir and bottomhole pressures were also developed. The proposed models are intended to analyze the operation of wells during the development of an oil reservoir in an elastic-water-pressure mode. The models were tested on a real field and their effectiveness was analyzed. Some new models perform well in a selection of tests. In particular, all the proposed models give better results than the classical model: the logarithm of the water-oil ratio from the accumulation of oil production.
production analysis, production optimization, regression analysis, water-oil displacement characteristics, elastic water drive, forecast of production indicators
- Afanaskin I.V. (2016). Address efficiency evaluation of implemented systems of oil fields development (Russian). Geologiya, geofizika i razrabotka neftyanykh i gazovykh mestorozhdenii = Geology, geophysics and development of oil and gas fields, 8, pp. 44–54. (In Russ.)
- Afanaskin I.V., Kolevatov A.A., Akhapkin M.Yu., Korolev A.V., Kundin A.S., Mironov D.T., Solopov D.V. (2022). Technology of analysis, forecasting and optimization of a group of producing wells operation by means of regression analysis and fluid’s displacement characteristics. Geologiya, geofizika i razrabotka neftyanykh i gazovykh mestorozhdenii = Geology, geophysics and development of oil and gas fields, (11), pp. 60–70. https://doi.org/10.33285/2413-5011-2022-11(371)-60-70
- Arps J.J. (1945). Analysis of decline curves. Transactions of the AIME, 160(1), pp. 228–247. https://doi.org/10.2118/945228-G
- Aziz, K., Settari A. (1979). Petroleum Reservoir Simulation. London: Appl. Sci. Publ., 476 p.
- Bondar V.V., Blasingame T.A. (2002). Analysis and Interpretation of Water-Oil-Ratio Performance. SPE Annual Technical Conference and Exhibition, SPE 77569. https://blasingame.engr.tamu.edu/0_TAB_Public/TAB_Publications/SPE_077569_(Bondar)_WOR_Analysis.pdf
- Can B., Kabir C.S. (2014). Simple tools for forecasting waterflood performance. Journal of Petroleum Science and Engineering, 120, pp. 111–118. https://doi.org/10.1016/j.petrol.2014.05.028
- Cheng C., Li K. (2014). Comparison of models correlating cumulative oil production and water cut. Journal of Energy Resources Technology, 136(3), 032901. https://doi.org/10.1115/1.4026459
- Craig F. (1971). The reservoir engineering aspects of waterflooding. Society of Petroleum Engineers of AIME, 134 p.
- Dake L.P. (2001). The Practice of Reservoir Engineering. Elsevier, 572 p.
- Elkin S.V., Aleroev A.A., Veremko N.A., Chertenkov M.V. (2016). Model for express calculation of the fluid flow rate of a horizontal well depending on the number of hydraulic fractures, taking into account the anisotropy of the formation. Inzhenernaya praktika = Engineering practice, (7), pp. 82–88. (In Russ.)
- Elmabrouk S.Kh., Mahmud W.M. (2022). Production data analysis techniques for the evaluation of the estimated ultimate recovery (EUR) in oil and gas reservoirs. HighTech and Innovation Journal, 3(1), pp. 85–101. http://doi.org/10.28991/HIJ-2022-03-01-09
- Ershaghi I., Abdassah D. (1984). A prediction technique for immiscible processes using field performance data. Journal of Petroleum Technology, 36(4), pp. 664–670. https://doi.org/10.2118/10068-PA
- Ershaghi I., Omorigie O. (1978). A method for extrapolation of cut vs recovery curves. Journal of Petroleum Technology, 30(2), pp. 203–204. https://doi.org/10.2118/6977-PA
- Guo B., Tu X. (2008). A simple and accurate mathematical model for predicting productivity of multifractured horizontal wells. CIPC/SPE Gas Technology Symposium 2008 Joint Conference, SPE-114452-MS. https://doi.org/10.2118/114452-MS
- Liu B. (2021). February. Application of water drive characteristic curve in oil field development planning index prediction. IOP Conference Series: Earth and Environmental Science, 651, 032074. http://doi.org/10.1088/1755-1315/651/3/032074
- Mirzadzhanzade A.Kh., Khasanov M.M., Bakhtizin R.N. (1999). Studies on modeling complex oil production systems: Nonlinearity, nonequilibrium, uncertainty. Ufa: Gilem, 462 p. (In Russ.)
- Olenchikov D., Posvyanskii D. (2019). Application of CRM-like models for express forecasting and optimizing field development. SPE Russian Petroleum Technology Conference, SPE-196893-MS. https://doi.org/10.2118/196893-MS
- Ruchkin А.А., Stepanov S.V., Knyazev А.V., Stepanov А.V., Korytov А.V., Avsyanko I.N. (2018). Applying CRM model to study well interference. Vestnik Tyumenskogo gosudarstvennogo universiteta. Fiziko-matematicheskoe modelirovanie. Neft’, gaz, energetika, 4(4), pp. 148–168. (In Russ.) https://doi.org/10.21684/2411-7978-2018-4-4-148-168
- Savelev V.A., Tokarev M.A., Chinarov A.S. (2008). Geological and field methods for forecasting oil recovery. Izhevsk: Udmurtskij universitet, 146 p. (In Russ.)
- Sayarpour M. (2008). Development and application of capacitance-resistive models to water/CO2 dioxide floods: Ph.D. Diss. The University of Texas at Austin. https://doi.org/10.13140/RG.2.1.1798.3847
- Sayarpour M., Kabir C.S., Lake L.W. (2009a). Field applications of capacitance-resistance models in waterfloods. SPE Reservoir Evaluation & Engineering, 12(6), pp. 853–864. https://doi.org/10.2118/114983-PA
- Sayarpour M., Kabir C.S., Sepehrnoori K., Lake L.W. (2011). Probabilistic history matching with the capacitance-resistance model in waterfloods: A precursor to numerical modeling. Journal of Petroleum Science and Engineering, 78(1), pp. 96–108. https://doi.org/10.1016/j.petrol.2011.05.005
- Sayarpour M., Zuluaga E., Kabir C.S., Lake L.W. (2009b). The use of capacitance-resistance models for rapid estimation of waterflood performance and optimization. Journal of Petroleum Science and Engineering, 69(3–4), pp. 227–238. https://doi.org/10.1016/j.petrol.2009.09.006
- Sergeev V.L., Naimushin A.G., Long Ch.N. (2014). Integrated systems for identifying displacement characteristics in monitoring and control of oil field development. Doklady Tomskogo gosudarstvennogo universiteta sistem upravleniya i radioelektroniki = Proceedings of TUSUR University, (3), pp. 152–158. (In Russ.)
- Sevost’yanov D.V., (2005). Assessment of the effectiveness of geological and technical measures using the method of integrated models. Problems of geology of subsoil development: Proc. IX International Symposium. Tomsk: “TPU” Publ., pp. 449–451. (In Russ.)
- Smith J.T., Cobb W.M. (1997). Waterflooding. Dallas: Midwest Office of the Petroleum Technology Transfer Council.
- Sun W. (2021). Two kinds of water drive characteristic curve control systems are recoverable reserves of water drive oilfield. Advances in Intelligent Systems and Computing, 1384. https://doi.org/10.1007/978-3-030-74811-1_127
- Willhite G.P. (1986). Waterflooding. Dallas: SPE, 326 p.
- Wolcott D. (2009). Applied Waterflood Field Development. Houston: Energy Tribune Publ., 417 p.
- Yang Z. (2009). Analysis of production decline in waterflood reservoirs. SPE Annual Technical Conference and Exhibition, SPE-124613-MS. https://doi.org/10.2118/124613-MS
- Yang Z. (2017). Clarifying and improving the application of waterflood analytical methods in X-plot conditions – from empirical approach to analytical approach. SPE Western Regional Meeting, SPE-185726-MS. https://doi.org/10.2118/185726-MS
- Yang Z., Ershaghi I. (2005). A method for pattern recognition of WOR plots in waterflood management. SPE Western Regional Meeting, SPE-93870-MS. https://doi.org/10.2118/93870-MS
- Yortsos Y.C., Choi Y., Yang Z., Shah P.C. (1999). Analysis and interpretation of water-oil ratio in waterfloods. SPE Journal, 4(4), pp. 413–424. https://doi.org/10.2118/59477-PA
- Yulmukhametov D.R. (2017). A Method of processing source data for the water-oil ratio vs recovery semi-log plot in unstable well stock operation conditions. Neftyanoe Khozyaystvo = Oil Industry, 11, pp. 44–47. (In Russ.) https://doi.org/10.24887/0028-2448-2017-11-44-47
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Ivan V. Afanaskin – Cand Sci. (Engineering), Leading Researcher, Institute for System Analysis of the Russian Academy of Sciences
e-mail: ivan@afanaskin.ru
Sergej G. Volpin – Cand Sci. (Engineering), Head of Department, Institute for System Analysis of the Russian Academy of Sciences
Valerij A. Yudin – Cand Sci. (Physics and Mathematics), Leading Researcher, Institute for System Analysis of the Russian Academy of Sciences
Pavel V. Kryganov – Cand Sci. (Engineering), Leading Researcher, Institute for System Analysis of the Russian Academy of Sciences
36, Build. 1, Nakhimovsky ave., Moscow, 117218, Russian Federation
Aleksej A. Glushakov – Junior Researcher, Institute for System Analysis of the Russian Academy of Sciences
Afanaskin I.V., Volpin S.G., Yudin V.A., Kryganov P.V., Glushakov A.A. (2023). Modeling of well performance during oil reservoir development on the elastic-water-drive mode using regression analysis. Georesursy = Georesources, 25(4), pp. 267–285. https://doi.org/10.18599/grs.2023.4.21