## 3 Data Preparation and Description
### Workflow for Developed Model  
The developed model consisted of 129 horizontal wells (Webb--72 wells, LaSalle--57 wells) in the dry gas Eagle Ford Shale. The Drilling Info Webb site has been used to acquire the field data. Hydraulic fracturing, well completion, and reservoir property data related to productivity were determined as input data and the cumulative production of three years after production as an output data. The introduction and statistical analysis for inputs and targets parameters are presented in Tables 1 and 2.  
Figure 6: Pearson correlation matrix and distribution of data used in the datasets.  
The workflow in Figure 7 show the three steps for productivity forecasting workflow. The first step is data preprocessing and preparation. To develop a supervised learning model, input variables and target variables need to be selected, and machine learning modeling need to be transformed into datasets that enable learning. In addition, outlier or null data that degrade the predictive performance of the model are removed. In the second step, application of machine learning modeling to improve predictive performance. First, The VIM based on RF prevents overfitting by eliminating low-correlation input features in the predictive model. Second, Clustering validity determines the number of clusters. It then re-generates the predictive model of clusters across the entire datasets for training. The third step is to use test data to compare the results between supervised learning models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters Type** & **Parameters** & **Unit** & **Abbreviation** \\ \hline \multirow{8}{*}{Input variables} & Prop. & MMIb & Prop. \\  & Slick water volume & Mgal & Slick water \\  & Cluster & - & Cluster \\  & Lateral length & ft & Laterallength \\  & Net pay & ft & Netpay \\  & Tubing head pressure & psia & THP \\  & Wellhead shut in pressure & psia & WHSP \\  & Gas gravity & - & Gasgravity \\  & Well test gas rate & Mscf & Testq \\  & Initial production rate (3 month) & MMscf & Qi\_3M \\  & Easting & - & East. \\  & Northing & - & North. \\  & Measured depth & ft & MD \\  & True vertical depth & ft & TVD \\  & Peak for barrel of oil equivalent & mbo & Peak BOE \\  & Gas oil ratio & - & GOR \\  & Gas liquid ratio & - & GLR \\  & Initial water rate (3 month) & Mbbl & W\({}_{\rm i}\) \\ \hline Target variables & Cumulative gas production (36 month) & Bcf & Q\({}_{\rm p}\)\_36M \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters and abbreviation for input and target variables used in this study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters Type** & **Parameters** & **Range (Min-Max)** & **1 st Quartile** & **Mean** & **3 rd Quartile** \\ \hline \multirow{8}{*}{Input variables} & Prop. & 2.02–7.45 & 3.02 & 4.16 & 4.99 \\  & Slick water & 25.2–230.2 & 69.9 & 96.5 & 117.9 \\  & Cluster & 36–81 & 48 & 52 & 57 \\  & Laterallength & 3579–8066 & 4718 & 5189 & 5641 \\  & Netpay & 7.0–562.0 & 58.0 & 140.1 & 201.0 \\  & THP & 1380–7330 & 2980 & 3838 & 4666 \\  & WHSP & 200–6448 & 700 & 2004 & 3064 \\  & Gasgravity & 0.592–0.852 & 0.683 & 0.710 & 0.737 \\  & Testq & 0.4–30.8 & 8.0 & 12.6 & 16.6 \\  & Qi\_3M & 0.026–0.612 & 0.189 & 0.275 & 0.352 \\  & East. & 1,558,855–2,061,324 & 1,601,894 & 1,756,328 & 1,951,825 \\  & North. & 119,813–919,229 & 154,413 & 556,278 & 888,760 \\  & MD & 9724–18326 & 14,328 & 15,192 & 16,257 \\  & TVD & 5404–12944 & 8461 & 9516 & 10,926 \\  & Peak BOE & 8.6–61.2 & 19.4 & 24.4 & 30.9 \\  & GOR & 0–5,196,333 & 14,870 & 1,714,477 & 86,480 \\  & GLR & 0–662,275 & 10,339 & 76,090 & 73,995 \\  & W\({}_{\rm i}\) & 0.7–214.9 & 17.2 & 45.0 & 63.5 \\ \hline Target variables & Q\({}_{\rm p}\)\_36M & 0.28–4.48 & 1.00 & 1.46 & 1.60 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical analysis of input and target variables datasets used in this study.