«Automation and Informatization of the fuel and energy complex»

Data reconciliation and errors minimization when measuring and evaluating process parameters

UDC: 001.891.573
DOI: -

Authors:

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

Keywords: data reconciliation, technological process, random error, imbalance, the least squares method, SWOT analysis

Annotation:

The authors of the article describe the process of data reconciliation at oil and gas facilities. Since the measured errors data can significantly worsen installations operations followed by their failures as well as negatively affect the quality of analysis and management decisions, it is important to evaluate the reliability of technological process data based on information provided by initial measurements. Data reconciliation is one of the main stages when assessing the state of the process. An example of this procedure implementation is shown. After data reconciliation, a graph was obtained showing the normal distribution of imbalances before and after reconciliation. The least squares method was used to minimize the linear model parameters. A SWOT analysis is proposed, including identification of strengths, weaknesses, opportunities, and threats for determining the main directions of development and effectiveness of introducing the strategy to minimize errors and improve data reconciliation in the technological process.

Bibliography:

1. Botev L., Johnson P. Applications of statistical process control in the management of unaccounted for gas // J. of Natural Gas Science and Engineering. – 2020. – Vol. 76. – P. 103194. – DOI: 10.1016/j.jngse.2020.103194
2. Кувыкин В.И., Логунов П.Л. Согласование данных материального баланса нефтеперерабатывающего завода // Автоматизация и информатизация ТЭК. – 2022. – № 2(583). – С. 41–48. – DOI: 10.33285/2782-604X-2022-2(583)-41-48
3. Badings T.S., van Putten D.S. Data validation and reconciliation for error correction and gross error detection in multiphase allocation systems // J. of Petroleum Science and Engineering. – 2020. – Vol. 195. – P. 107567. – DOI: 10.1016/j.petrol.2020.107567
4. A novel robust data reconciliation method for industrial processes / Xie Sen, Yang Chunhua, Yuan Xiaofeng [et al.] // Control Engineering Practice. – 2019. – Vol. 83. – P. 203–212. – DOI: 10.1016/j.conengprac.2018.11.006
5. Optimal combination forecasts for hierarchical time series / R.J. Hyndman, R.A. Ahmed, G. Athanasopoulos, Han Lin Shang // Computational Statistics & Data Analysis. – 2011. – Vol. 55, Issue 9. – P. 2579–2589. – DOI: 10.1016/j.csda.2011.03.006
6. Forecasting with Temporal Hierarchies / G. Athanasopoulos, R.J. Hyndman, N. Kourentzes, F. Petropoulos // European J. of Operational Research. – 2017. – Vol. 262, Issue 1. – P. 60–74. – DOI: 10.1016/j.ejor.2017.02.046
7. Di Fonzo T., Girolimetto D. Forecast combination based forecast reconciliation: insights and extensions // European J. of Operational Research. – 2021. – DOI: 10.48550/arXiv.2106.05653
8. Данилов Н.Н., Иноземцева Л.П. Математическая модель SWOT-анализа и методика ее применения в экономике // Эконом. анализ: теория и практика. – 2016. – № 9(456). – С. 185–196.