## 8 Conclusions  
In this study, production data analyses performed on 1216 multi-stage hydraulically fractured horizontal wells from five shale formations (Niobrara, Barnett, Haynesville, Eagle Ford, and Bakken) that were recently abandoned. Furthermore, production history matching with two DCA models was performed using least-square fitting on all the wells from this study. Following are the main highlights of this study.  
Using two years of production data to match showed a mean absolute percentage error (MAPE) of less than 10% in EUR estimations across all wells from all five formations. The DCA model that provided the best fit varied between formations. For the Niobrara, Barnett, Eagle Ford, and Bakken formations, the Arps hyperbolic decline model showed higher accuracy of matching and forecasting production using limited early production data. While for the Haynesville formation, the SEPD model showed a higher accuracy in forecasting production using only a limited early production data. Across all wells in this study, four different initial flow rate values were investigated. In all 1216 wells from the five formations, assigning the initial flow rate as the maximum flow rate in the SEPD model resulted in the best estimates of EUR in production history matching. The maximum mean absolute percentage error (MAPE) when using the initial flow rate as the maximum flow rate was less than 5% in the production history matching performed in this study. Using other initial flow rate definitions increased the error greatly in all wells in the five formations. For the five studied reservoirs, the maximum flow rate was reached within the first three months of production for most of the wells. The main outcomes of the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**SEPD**} & \multicolumn{2}{c}{**Arps Hyperbolic Decline**} \\ \cline{2-5}  & **Average Exponent** & **Average Characteristic Time** & **Average Hyperbolic** & **Average Nominal Decline** \\
**Formation** & **Parameter** & **Parameter** & **Exponent** & **Rate** \\
**(n)** & **(\(\star\))** & **(\(\mathbf{b}\))** & **(\(\mathbf{D}\))** & **(\(\mathbf{P}\))** \\ \hline Niobrara & 0.62 & 258 & 1.33 & 0.032 \\ Barnett & 0.63 & 364 & 1.56 & 0.033 \\ Haynesville & 0.86 & 369 & 1.06 & 0.011 \\ Eagle Ford & 0.72 & 277 & 1.09 & 0.041 \\ Bakken & 0.39 & 161 & 1.56 & 0.055 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average decline parameters for all wells in each formation for the SEPD and Arps hyperbolic decline models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Formation**} & **SEPD** & **Arps Hyperbolic Decline** \\ \cline{2-3}  & \multicolumn{2}{c}{**Correlation Coefficient (\(\mathbf{r}\))**} \\ \hline Niobrara & 0.808 & 0.853 \\ Barnett & 0.816 & 0.799 \\ Haynesville & 0.605 & 0.777 \\ Eagle Ford & 0.832 & 0.853 \\ Bakken & 0.720 & 0.782 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Using average decline parameters error analysis in terms of correlation coefficient for all wells in each formation for the SEPD and Arps hyperbolic decline models.  
production history matching for all the wells using their entire production history were the DCA average parameters n and \(\tau\) for the SEPD model and b and D\({}_{i}\) for the Arps hyperbolic decline model--for each formation. The accuracy of using average decline parameters of the Arps hyperbolic decline model provided a correlation coefficient between 0.77 and 0.85 for all the formations. For the SEPD model, a 0.60 to 0.83 correlation coefficient resulted when using the average decline parameters for all the formations.  
This research received no external funding.  
Not applicable.  
The author is grateful to the College of Petroleum Engineering and Geoscience, King Fahd University of Petroleum and Minerals. For its support in publishing this work.  
The author declares no conflict of interest.