Project details:
This project evaluates three potential regions for drilling new oil wells. A regression model predicts reserves for future wells, while a bootstrapped risk analysis estimates expected profit and loss probability. The final recommendation balances predicted revenue with financial risk, helping the company choose the safest and most profitable region for development.
Description
Business Context & Problem
An oil company needs to decide where to drill its next set of wells. Each region contains many potential well locations, and drilling is expensive. A data-driven approach helps determine which area offers the best return while keeping financial risk low. This project focuses on forecasting reserves in new wells and selecting the region with the strongest profit outlook.
Data & Analytical Approach
The dataset included characteristics of oil samples and estimated reserves from wells across three regions. After cleaning and preparing the data, exploratory analysis helped highlight differences in well productivity between regions. A regression model was trained to predict the expected volume of reserves for new wells based on their features. The same modeling approach was applied uniformly across all regions to allow fair comparison.
Statistical / ML Analysis
Using Scikit-learn, a linear regression model was selected for its stability and interpretability. Predictions were then combined with business constraints such as the number of wells to be drilled and the minimum profit threshold. To evaluate financial outcomes, a bootstrapping technique simulated multiple scenarios for each region, providing estimates of expected profit, confidence intervals and the probability of loss — a key metric for decision-making.
Key Insights & Final Recommendations
The analysis revealed clear differences between regions in terms of average predicted reserves and variability. One region showed consistently higher expected profit with a low probability of financial loss, making it the safest and most attractive option for development.
By combining machine learning predictions with a robust risk assessment, the project provides a well-supported recommendation for where the company should focus its next drilling efforts.