Production Decline Prediction of Shale Gas using Hybrid Models

Abstract

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.

Accuracy Assessment of Single and Hybrid Models for Predicting Shale Gas Production

“…However, based on the findings from the study on production decline prediction using hybrid models, the results showed that the artificial neural network (ANN), autoregressive integrated moving average (ARIMA), and Arps−power law exponential (Arps−PLE) hybrid rate decline models showed better accuracy. 5 The finding concurred with work conducted by Taskaya-Temizel et al 6 hybrid models, Arps−PLE hybrid decline and ANN−ARIMA hybrid, for shale gas production. The main ideas are to (a) use the goodness-of-fit statistical assessment to evaluate the accuracy of the models, (b) validate the models using the mean absolute percentage error (MAPE) values, and (c) summarizes findings from the research study.…”

“…A proposed hybrid model approach was examined since studies conducted have proved its higher accuracy. However, based on the findings from the study on production decline prediction using hybrid models, the results showed that the artificial neural network (ANN), autoregressive integrated moving average (ARIMA), and Arps–power law exponential (Arps–PLE) hybrid rate decline models showed better accuracy . The finding concurred with work conducted by Taskaya-Temizel et al whereby in certain conditions the single-model methodology can outperform the hybrid models.…”

“…The model characterizes the decline rate by infinite time, D ∞ , which is defined as a "loss ratio" (which is assumed to be constant from Arp). 11 The production rate is derived as follows (5) where dq/dt is the slope, D i is the decline rate over a long-term period, and n̂is the time exponent. By substituting the above equations, the production rate is obtained…”