Mid-term forecasting of oil production on oilfield with SARIMAX model

UDK: 519.868:622.276.5
DOI: 10.24887/0028-2448-2020-2-84-88
Key words: forecasting, ARIMA, oilfield, wells, oil production
Authors: A.F. Azbukhanov (RN-BashNIPIneft LLC, RF, Ufa), I.A. Lakman (Ufa State Aviation Technical University, RF, Ufa), A.A. Agapitov (RN-BashNIPIneft LLC, RF, Ufa) , L.F. Sadikova (RN-BashNIPIneft LLC, RF, Ufa; Ufa State Aviation Technical University, RF, Ufa)

The article considers the possibility of applying time series analysis in the oil industry. A model of Seasonal Auto Regression Integrated Moving Average with external variables (SARIMAX) for medium-term forecasting (up to 1 year) of integrated oil production in oil fields is built. As a training sample, monthly data on oil production for 10 years of development of several fields of Rosneft PJSC have been studied; monthly data on the number of producing wells were used as an exogenous variable. As a result of the consistent application of the extended Dickey-Fuller test to the time series, the first order of integration of the random process was substantiated. The correctness of the inclusion of an exogenous variable in the model was confirmed by testing the hypothesis of the presence of co-integration between the variables of oil production and the number of producing wells. The analysis of the autocorrelation and private autocorrelation functions of the studied time series for oil production, as well as the selection of models based on the Akayke and Schwartz information criteria, made it possible to determine the best specification of the SARIMAX model. The obtained forecast values were checked against the actual values of oil production at the field. Based on the predicted and actual values, the model quality metrics have been calculated: the average approximation error (MAPE) was 0.78%. The application of the methodology for predicting oil production proposed in the article and the use of the accumulated volume of data in a structured form led to a qualitative forecast. This, in turn, will allow in the future making more informed business decisions, since a high-quality medium-term forecast allows you to save company resources.

References

1. Md-Khai N.Q.N., Samsudin R., Forecasting crude oil prices using Wavelet ARIMA model approach, Proceedings of International Conference of Reliable Information and Communication Technology, 2017, pp. 535–544.

2. Choi J., Roberts D. C., Lee E.S., Forecasting oil production in North Dakota using the seasonal autoregressive integrated moving average (S-ARIMA), Natural Resources, 2015, V. 6, no. 1, pp. 16–26.

3. Gupta V., Dwivedi S., Production forecasting in the age of Big Data in oil & gas industry, Proceedings of SAS Global Forum, 2017, URL: https://support.sas.com/resources/papers/proceedings17/1469-2017-poster.pdf

4. Yusof N.M., Rashid R.S.A., Mohamed Z., Malaysia crude oil production estimation: an application of ARIMA model, Science and Social Research, IEEE, 2010, pp. 1255–1259.

5. Omekara C.O., Okereke O.E., Ire K.I., Okamgba C.O., ARIMA modeling of Nigeria crude oil production, Journal of Energy Technologies and Policy, 2015, V. 5, pp. 1–5.

6. Albarrak A., Time series analysis of Saudi Arabia oil production data, Ball State University, 2013, 123 r.

7. Guha B., Bandyopadhyay G., Gold price forecasting using ARIMA model, Journal of Advanced Management Science, 2016, V. 4, no. 2, pp. 117–121.

8. Asteriou D., Hall S.G., ARIMA models and the Box–Jenkins methodology, Applied Econometrics, Palgrave MacMillan, 2011, pp. 265–286.

9. Dickey D.A., Fuller W.A., Distribution of the estimators for autoregressive time-series with a unit root, Journal of the American Statistical Association, 1979, V. 74, pp. 427–431.

10. Dickey D.A., Pantula S.G., Determining the order of differencing in autoregressive processes, Journal of Business and Economic Statistics, 1987, V. 5, pp. 455–461.

11. Walter E., Cointegration and error-correction models, Applied Econometrics Time Series, New York: Wiley, 2004, pp. 319–386.

12. Aastveit K.A., Jore A.S., Ravazzolo F., Identification and real-time forecasting of Norwegian business cycles, International Journal of Forecasting, 2016, V. 32, no. 2, pp. 283–292.

The article considers the possibility of applying time series analysis in the oil industry. A model of Seasonal Auto Regression Integrated Moving Average with external variables (SARIMAX) for medium-term forecasting (up to 1 year) of integrated oil production in oil fields is built. As a training sample, monthly data on oil production for 10 years of development of several fields of Rosneft PJSC have been studied; monthly data on the number of producing wells were used as an exogenous variable. As a result of the consistent application of the extended Dickey-Fuller test to the time series, the first order of integration of the random process was substantiated. The correctness of the inclusion of an exogenous variable in the model was confirmed by testing the hypothesis of the presence of co-integration between the variables of oil production and the number of producing wells. The analysis of the autocorrelation and private autocorrelation functions of the studied time series for oil production, as well as the selection of models based on the Akayke and Schwartz information criteria, made it possible to determine the best specification of the SARIMAX model. The obtained forecast values were checked against the actual values of oil production at the field. Based on the predicted and actual values, the model quality metrics have been calculated: the average approximation error (MAPE) was 0.78%. The application of the methodology for predicting oil production proposed in the article and the use of the accumulated volume of data in a structured form led to a qualitative forecast. This, in turn, will allow in the future making more informed business decisions, since a high-quality medium-term forecast allows you to save company resources.

References

1. Md-Khai N.Q.N., Samsudin R., Forecasting crude oil prices using Wavelet ARIMA model approach, Proceedings of International Conference of Reliable Information and Communication Technology, 2017, pp. 535–544.

2. Choi J., Roberts D. C., Lee E.S., Forecasting oil production in North Dakota using the seasonal autoregressive integrated moving average (S-ARIMA), Natural Resources, 2015, V. 6, no. 1, pp. 16–26.

3. Gupta V., Dwivedi S., Production forecasting in the age of Big Data in oil & gas industry, Proceedings of SAS Global Forum, 2017, URL: https://support.sas.com/resources/papers/proceedings17/1469-2017-poster.pdf

4. Yusof N.M., Rashid R.S.A., Mohamed Z., Malaysia crude oil production estimation: an application of ARIMA model, Science and Social Research, IEEE, 2010, pp. 1255–1259.

5. Omekara C.O., Okereke O.E., Ire K.I., Okamgba C.O., ARIMA modeling of Nigeria crude oil production, Journal of Energy Technologies and Policy, 2015, V. 5, pp. 1–5.

6. Albarrak A., Time series analysis of Saudi Arabia oil production data, Ball State University, 2013, 123 r.

7. Guha B., Bandyopadhyay G., Gold price forecasting using ARIMA model, Journal of Advanced Management Science, 2016, V. 4, no. 2, pp. 117–121.

8. Asteriou D., Hall S.G., ARIMA models and the Box–Jenkins methodology, Applied Econometrics, Palgrave MacMillan, 2011, pp. 265–286.

9. Dickey D.A., Fuller W.A., Distribution of the estimators for autoregressive time-series with a unit root, Journal of the American Statistical Association, 1979, V. 74, pp. 427–431.

10. Dickey D.A., Pantula S.G., Determining the order of differencing in autoregressive processes, Journal of Business and Economic Statistics, 1987, V. 5, pp. 455–461.

11. Walter E., Cointegration and error-correction models, Applied Econometrics Time Series, New York: Wiley, 2004, pp. 319–386.

12. Aastveit K.A., Jore A.S., Ravazzolo F., Identification and real-time forecasting of Norwegian business cycles, International Journal of Forecasting, 2016, V. 32, no. 2, pp. 283–292.



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