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)

adomfosugit/BHFP5S57

Bottom Hole Pressure (BHP) Prediction Model

Features Used

Derived Features

Author

Model Usage

adomfosugit/
BHFP5S57