A New Approach in Predicting the Higher Heating Value of Natural Gas from Ghana’s Oil Fields

Abstract

The Heating value of natural gas is used to determine the quality of the gas sample, hence accurate prediction of heating value helps in controlling the issue of underbilling and overbilling between a gas aggregator and an off-taker. Moreover, the heating value of natural gas is not a fixed value and the accuracy of it in real-time is essential. This study was focused on the prediction of the Higher Heating Value (HHV) of natural gas based on percentage gas compositions obtained from Ghana’s offshore oil fields using Artificial Neural Networks (ANN), Adaptive Boost (AdaBoost), Extreme Gradient Boost (XGBoost), Linear Regression (LR). These algorithms were modelled to determine the best predictive model using 2021 sample data on gas specifications. Eighty percent (80%) of the data was used for training and the remaining 20% was used for testing. The performance of each algorithm was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), R2 and Adjusted R2. XGBoost performed better than all the other predictive models with an R2 and adjusted R2 of 91.18% and 90.93% respectively and RMSE, MAE, and MAPE of 1.7302, 0.5393 and 0.57% respectively. The incorporation of this method provides a diverse approach to the analysis of the pipeline dynamic results of the heating value of natural gas.

Keywords

• adaptive boost
• artificial neural networks
• extreme gradient boost
• higher heating value
• linear regression