Influence of noisy input data in accuracy of well interference coefficients by linear regression method

About the author:

Rodion M. Ganopolskij, Cand. Sci. (Phys.-Math.), Head of the Department of Modeling of Physical Processes and Systems, Institute of Physics and Technology, University of Tyumen, Tyumen, Russia, r.m.ganopolskij@utmn.ru

Abstract:

Hydrodynamic simulators are used to predict the operation of a production well. Their work demands high performance. Alternative simplified methods are appearing constantly, for example, proxy models. Firstly, these models are tuned to historical data and then they make predictions. The source data is quite inaccurate and incomplete often. It is necessary to study how an input parameter error affects quality of forecast. This article shows an algorithm for determining the well interference coefficients by linear regression method. Study was made of the stability of the obtained solution to the noise of the initial data under various conditions. A production well forecast has been obtained, which is then compared with the exact value. The error in which initial data has the greatest impact on the forecast error is analyzed. Possible options for ensuring sustainability are proposed.

References:

Danko, M. Yu., Brilliant, L. S., & Zavyzlov, A. S. (2019). Application of dynamic material balance method and CRM method (capacitance-resistive models) for reserves assessment in Achimov and Bazhenov reservoirs. Nedropolzovanie XXI vek, (4), 76–85. [In Russian]

Demidenko, E. Z. (1981). Linear and non-linear regression. Finansy i statistika. [In Russian]

Ruchkin, A. A., Stepanov, S. V., Knyazev, A. V., Stepanov, A. V., Korytov, A. V., & Avsyan­ko, I. N. (2018). Applying CRM model to study well interference. Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy, 4(4), 148–168. https://doi.org/10.21684/2411-7978-2018-4-4-148-168 [In Russian]

Stepanov, S. V., Tyrsin, A. N., Ruchkin, A. A., & Pospeiova, T. A. (2020). Using entropy modeling to analyze the effectiveness of the waterflooding system. Oil Industry, (6), 62–67. https://doi.org/10.24887/0028-2448-2020-6-62-67 [In Russian]

Stepanov, S. V., Bekman, A. D., Ruchkin, A. A., & Pospelova, T. A. (2021). Maintenance of oil field development using CRM models. Express. [In Russian]

Kim, J. S., Lake, L. W., & Edgar, T. F. (2012, May 31 – June 1). Integrated capacitance-resistance model for characterizing waterflooded reservoirs [Conference paper]. 2012 IFAC Workshop on Automatic Control in Offshore Oil and Gas Production, Trondheim, Norway. https://doi.org/10.13140/2.1.2060.0964

Olenchikov, D., & Posvyanskii, D. (2019, October 22–24). Application of CRM-like models for express forecasting and optimizing field development [Conference paper]. SPE Russian Petroleum Technology Conference, Moscow, Russia. https://doi.org/10.2118/196893-MS

Sayarpour, M. (2008). Development and application of capacitance-resistive models to water/CO2 floods [Doctoral dissertation, The University of Texas at Austin].

Sayarpour, M., Zuluaga, E., Kabir, C. S., & Lake, L. W. (2009). The use of capacitance-resistance models for rapid estimation of waterflood performance and optimization. Journal of Petroleum Science and Engineering, 69(3–4), 227–238. https://doi.org/10.1016/j.petrol.2009.09.006

Yousef, A. A., Gentil, P., Jensen, J. L., & Lake, L. W. (2006). A capacitance model to infer interwell connectivity from production- and injection-rate fluctuations. SPE Reservoir Evaluation & Engineering, 9(6), 630–646. https://doi.org/10.2118/95322-PA