The development of prediction tools for production performance and the lifespan of shale gas reservoirs has been a focus for petroleum engineers. Several decline curve models have been developed and compared with data from shale gas production. To accurately forecast the estimated ultimate recovery for shale gas reservoirs, consistent and accurate decline curve modelling is required. In this paper, the current decline curve models are evaluated using the goodness of fit as a measure of accuracy with field data. The evaluation found that there are advantages in using the current DCA models; however, they also have limitations associated with them that have to be addressed. Based on the accuracy assessment conducted on the different models, it appears that the Stretched Exponential Decline Model (SEDM) and Logistic Growth Model (LGM), followed by the Extended Exponential Decline Model (EEDM), the Power Law Exponential Model (PLE), the Doung’s Model, and lastly, the Arps Hyperbolic Decline Model, provide the best fit with production data.

Hybrid models have frequently been used for shale gas production decline prediction by manipulating the unique strength of each of the known decline models. The use of a combination of models provides a more precise predicting model for forecasting time series data as compared to an individual model. In this study, the forecasting performance of decline curve hybrid models and ANN-ARIMA hybrid models are evaluated and compared with Arps’, Duong’s, the Power Law Exponential Decline, Autoregressive Integrated Moving Average (ARIMA) and Artificial Neutral Network (ANN) models, respectively. The variable used to assess the models was the respective flow rate, q(t) monitored over a period of time (T). The results have shown that the single model approach can outperform hybrid models. The average deviation of the two best models indicates a central tendency of the production data around the mean. Subsequently, the spread in the data between the actual and predicted values is found to be less. It can thus be concluded that the ARIMA and ANN models have the best forecasting accuracy for production decline in shale gas compared to the other models.

The accuracy of a predictive tool determines the levels of trust in the model and its attraction for commercial usage. The study examined the single and hybrid model approach for shale gas production. Multilayer perception artificial neural network (ANN), autoregressive integrated moving average (ARIMA), and Arps–power law exponential hybrid decline models were developed to predict shale gas production and compared with the already developed Arps decline and power law exponential (PLE) decline models. By a trial-and-error approach, a multilayer perception (MLP) network with four neurons in the hidden layer was attained in the ANN structure to predict shale gas production. While for the ARIMA model, the number of nodes that showed the best performance indicated (2,1,2) for the two sets of data. Evaluation of the root mean square error (RMSE) values for the models showed that the Arps–power law exponential hybrid decline model had a lower percentage error in conjunction with good accuracy. The study found the Arps–power law exponential hybrid decline model to be a good forecaster of shale gas production and that hybrid models do deliver better accuracy over single models. A future revision of model assumptions may improve its accuracy and make the Arps–power law exponential hybrid decline model an attractive predictive tool.