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Analytics Vidhya

Machine Learning for Prediction in Hydraulic Fracturing

Decision Tree to Predict Number of Perf Clusters per Stage

Photo by WORKSITE Ltd. on Unsplash
Illustration of stage and clusters in a perforated well — © Yohanes Nuwara

Overview of dataset

The first 10 observations of data
Top 10 leases with the largest number of clusters per stage

Feature Selection

Correlation heatmap of 25 features — © Yohanes Nuwara

Rcrit = +/- 0.27

Selected features for predictive model

Machine Learning Strategy for Small Datasets

Animation of LOOCV (Source: Wikimedia)
Validation curves for “max_depth” and “min_samples_leaf” hyperparameters

Prediction

An example case — © Yohanes Nuwara

Model Interpretability

Decision Tree model for the number of clusters per stage prediction — © Yohanes Nuwara
Decision space plot — © Yohanes Nuwara
  • 3.867 (or 4) clusters per stage — dark blue
  • 5.83 (or 6) clusters per stage — orange
  • 9 clusters per stage — purple
  • 15 clusters per stage — light blue
  • Gas Specific Gravity ≤ 0.631 → Gas Specific Gravity ≤ 0.575 → Net Pay ≤ 142.5 ft → 3.867 clusters per stage
  • Gas Specific Gravity ≤ 0.631 → Gas Specific Gravity ≤ 0.575 → Gas Saturation ≤ 0.8 → 5.83 clusters per stage
  • Gas Specific Gravity ≤ 0.631 → Bottom Perforation ≤ 16,212.5 ft → 9 clusters per stage
  • Gas Specific Gravity ≤ 0.631 → Bottom Perforation >16,212.5 ft → 15 clusters per stage

Conclusion

Data Reference

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Yohanes Nuwara

Writes about Data & AI :: Focus on the implication of Sci-Tech to Sociopolitics, Economics, and Environment