Influence of noisy input data in accuracy of well interference coefficients by linear regression method-Reference-Cited by-同舟云学术

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

Published:2023-04-14 Issue:1 Volume:9 Page:107-115
ISSN:2500-3526
Container-title:Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy
language:
Short-container-title:TSU Herald. Phys Math Model. Oil, Gas, Energy

Author:

Ganopolskij Rodion M.¹

Affiliation:

1. University of Tyumen

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.

Publisher

Tyumen State University

Reference10 articles.

1. 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]

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

3. 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]

4. 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]

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