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Predictive Modeling of Gas Production, Utilization and Flaring in Nigeria using TSRM and TSNN: A Comparative Approach


Affiliations
1 Department of Petroleum Engineering, University of Ibadan, Ibadan, Nigeria
2 Department of Statistics, University of Ibadan, Ibadan, Nigeria
 

Since the discovery of oil and gas in Nigeria in 1956, much gas has been flared because the operators pay little or no concern to its utilization, and as such, trillions of dollars have been lost. In this paper, a model is proposed using Time Series Regression Model (TSRM) and Time Series Neural Network (TSNN) to model the production, utilization and flaring of natural gas in Nigeria with the ultimate aim of observing the trend of each activity. The results show that TSNN has better predictive and forecasting capabilities compared to TSRN. It is also observed that the higher the hidden neurons, the lower the error generated by the TSNN.

Keywords

Natural Gas, Production, Utilization, Flaring, TSRM, TSNN, Model Selection.
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  • Predictive Modeling of Gas Production, Utilization and Flaring in Nigeria using TSRM and TSNN: A Comparative Approach

Abstract Views: 76  |  PDF Views: 1

Authors

Olugbenga Falode
Department of Petroleum Engineering, University of Ibadan, Ibadan, Nigeria
Christopher Udomboso
Department of Statistics, University of Ibadan, Ibadan, Nigeria

Abstract


Since the discovery of oil and gas in Nigeria in 1956, much gas has been flared because the operators pay little or no concern to its utilization, and as such, trillions of dollars have been lost. In this paper, a model is proposed using Time Series Regression Model (TSRM) and Time Series Neural Network (TSNN) to model the production, utilization and flaring of natural gas in Nigeria with the ultimate aim of observing the trend of each activity. The results show that TSNN has better predictive and forecasting capabilities compared to TSRN. It is also observed that the higher the hidden neurons, the lower the error generated by the TSNN.

Keywords


Natural Gas, Production, Utilization, Flaring, TSRM, TSNN, Model Selection.