Prediction and analysis of daily oil production in block T of A oil production plant based on ARIMA model

Mingyuan Hu; Yi Zhang; Jun Guo; Yazhou Chen; Feng Wei; Mengjiao Gou

doi:10.1117/12.2660959

2 February 2023 Prediction and analysis of daily oil production in block T of A oil production plant based on ARIMA model

Mingyuan Hu, Yi Zhang, Jun Guo, Yazhou Chen, Feng Wei, Mengjiao Gou

Author Affiliations +

Proceedings Volume 12462, Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022); 124621I (2023) https://doi.org/10.1117/12.2660959
Event: International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022), 2022, Xi'an, China

Abstract

Oil production plant A has entered the stage of high water cut in the middle and late stages of development, and its benefits are getting worse and worse. In order to formulate a reasonable production plan for the enterprise, avoid blind investment and improve production efficiency, based on the current situation, this paper introduces the modeling process of the ARIMA model in detail. Based on the oil production data of block T of the A oil production plant from January 2007 to September 2021, the ARIMA model is used to analyze the oil production. Four different ARIMA models were obtained through judgment, and after comparison, the study found that the prediction error metrics of the ARIMA (3,1,1) (2,0,0) [12] model were all small, and the model The effect of the short-term prediction of the daily oil production sequence in block T of the A oil production plant is relatively accurate, and it is of great significance for the short-term prediction of oil production.

Citation Download Citation

Mingyuan Hu, Yi Zhang, Jun Guo, Yazhou Chen, Feng Wei, and Mengjiao Gou "Prediction and analysis of daily oil production in block T of A oil production plant based on ARIMA model", Proc. SPIE 12462, Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022), 124621I (2 February 2023); https://doi.org/10.1117/12.2660959

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