The purpose of this analysis was to understand the usefulness of decision trees in the oil and gas industry. Decision trees help identify key factors influencing economic viability, making them a valuable tool for decision-making in reservoir management and investment planning
The dataset was generated using Python with random function to simulate reservoir properties such as permeability, porosity, oil production rate, and gas-to-oil ratio.
A decision tree works by identifying the most important factor that best separates the "Yes" and "No" categories.
In this case, Water Cut was the strongest differentiator between economically viable and non-viable wells. Wells with low Water Cut (more oil, less water) were classified as economically viable ("Yes")
Wells with high Water Cut (less oil, more water) were classified as non-viable ("No")
This suggests that, based on the dataset, Water Cut had a greater impact on well profitability than other variables like permeability, porosity, or reservoir pressure
While the model identified a clear pattern, there are key limitations to consider:
🔹 Dataset was created with random values: The dataset was generated using Python’s random functions, meaning it does not represent real-world data patterns
🔹 Revenue & Oil Production Rate Were Not Considered in the Decision Tree: Normally, economic viability is determined by oil production rate and revenue, but the model only used reservoir properties
🔹 Other Key Factors Were Ignored: Operational costs, oil price fluctuations, and well maintenance costs were not considered in this model
✅ Decision trees can identify key trends in well performance
✅ Water Cut is an important factor in determining whether a well is profitable
✅ Machine learning can help optimize decision-making in reservoir management
✅ However, economic factors like production rates and revenue must be included for a more accurate prediction
This analysis highlights the importance of decision trees in the oil and gas industry. While this model focused on reservoir properties, future models should incorporate economic data to improve accuracy.