Update README.md

README.md

@@ -13,4 +13,52 @@ tags:
 - Gas
 - Bottomhole_Pressure
 - BHP
----
+---
+# Bottom Hole Pressure (BHP) Prediction Model
+
+This project implements a machine learning model to predict Bottom Hole Pressure (BHP) in oil wells based on various well parameters.
+
+## Features Used
+- **Qo**: Oil production rate (STB/day)
+- **GOR**: Gas-Oil Ratio (scf/STB)
+- **THT**: Tubing Head Temperature (°F)
+- **Pwh**: Wellhead Pressure (psi)
+- **WCT**: Water Cut (%)
+- **Depth**: Well depth (ft)
+
+## Derived Features
+The model uses these engineered features:
+- **Fluid gradient**: `(WCT/100)*0.433 + (1-(WCT/100))*0.273`
+- **Ph (Hydrostatic Pressure)**: `Fluid gradient * Depth`
+
+
+## Author
+Kwadwo Fosu Adom
+## Model Usage
+
+
+
+```python
+import pickle
+import pandas as pd
+
+# Load your test data
+test_df = pd.read_csv('your_test_data.csv')  # or other source
+
+# Calculate derived features
+test_df['Fluid gradient'] = (test_df['WCT']/100)*0.433 + (1-(test_df['WCT']/100))*0.273
+test_df['Ph'] = test_df['Fluid gradient'] * test_df['Depth']
+
+# Features to scale (must match training)
+scaled_features = ['Qo', 'GOR', 'THT', 'Pwh(psi)', 'Ph', 'Depth']
+
+# Load model and scaler
+with open('modelBIGDATA5US1P57.pkl', 'rb') as file:
+    saved_data = pickle.load(file)
+    model = saved_data['model']
+    scaler = saved_data['scaler']
+
+# Make predictions
+X_test_scaled = scaler.transform(test_df[scaled_features])
+test_df['Predicted_BHP'] = model.predict(X_test_scaled)
+