# Dynamic Prediction of Shale Gas Drilling Costs Based on Machine Learning
## 4 Experiments and Results
### Drilling Cost Indicator Construction  
With today's rapid advances in big data and artificial intelligence technologies, large-scale data processing and mining is playing an increasingly important role in the analysis of drilling cost drivers and the development of optimisation strategies. Traditional cost analysis methods often show limitations in considering indirect factors and fail to fully exploit the potential of big data analysis, thereby limiting the effectiveness of cost optimization initiatives. As a result, this research is based on a comprehensive big data analytics framework that examines multiple factors that influence shale gas drilling costs. The primary objective is to develop a holistic cost analysis methodology that is capable of thoroughly incorporating various influencing factors, which is of significant value in optimising shale gas drilling costs.  
As shown in Table 6, this study incorporates indicators across 13 different dimensions to provide a comprehensive analysis of the factors influencing shale gas drilling costs. In addition, through an extensive literature review and consideration of data accessibility, 36 specific variables were systematically selected for investigation during the data preparation phase.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification** & **Variant** & **Classification** & **Variant** \\ \hline Basic information & Well number, type of well and time of commissioning & & \multirow{2}{*}{Road conditions} & Number of bridges, real urban road area \\  & Well depth, horizontal section length, actual fracturing length, number of fracturing sections, sand addition intensity, fracturing displacement, fracturing pump pressure, fluid intensity used, total sand addition, number of platform wells, shale & & \\ Policies & Level of policy support & Labour Force & \multirow{2}{*}{Toral wage bill of workers in employment in municipal area, number of employed persons Per capita consumption expenditure of urban residents, per capita consumption expenditure of rural consumption expenditure of rural residents and total retail sales of consumer goods Public Tram Passenger Volume of Municipal Districts and Number of Taxis in Municipal Districts Transport, storage and postal employment, number of post offices and cargo turnover Daily average temperature value \\ \hline \hline \end{tabular}
\end{table}
Table 6: Drilling cost indicator construction system.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variant** & **Mean** & **Median** & **Variance** & \
\begin{tabular}{c} **Minimum** \\ **Value** \\ \end{tabular} &
\begin{tabular}{c} **Maximum** \\ **Value** \\ \end{tabular} \\ \hline Well depth (metres) & 4875.57 & 4900 & 222,555.88 & 3330 & 6325 \\ Horizontal section length (metres) & 1662.14 & 1500 & 127,824.33 & 850 & 3100 \\ Actual fracturing length (metres) & 1579.62 & 1470 & 147,103.84 & 236 & 3166 \\ Number of fracturing sections (sections) & 25.00 & 24 & 37.73 & 4 & 51 \\ Strength of sand addition (Mg/m) & 2.30 & 2.32 & 0.36 & 0.81 & 4.70 \\ Fracturing displacement (m\({}^{3}\)/min) & 0.427 & 0.447 & 0.005 & 0.241 & 1.618 \\ Fracturing pump pressure (MPa) & 75.81 & 75.72 & 73.53 & 50.5 & 96.68 \\ Intensity of fluid used (m\({}^{2}\)/m) & 29.16 & 28.77 & 14.37 & 16.09 & 44.58 \\ Total sand addition (Mg) & 3688.65 & 3369.04 & 2,431,753.95 & 319.48 & 11,813.79 \\ Number of platform wells (ports) & 4.82 & 5 & 2.12 & 1 & 8 \\ Verification of single well EUR by shale year (\(10^{9}\) m\({}^{3}\)) & 1.14 & 1.12 & 0.15 & 0.19 & 2.42 \\ Investment in drilling and completing wells (\(10^{6}\) yuan) & 64.3429 & 62.9134 & 10,597.0741 & 37.4399 & 120.3020 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Descriptive statistics of drilling engineering parameters.