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Jupyter notebook · 4 code cells · 1 figures

• 1. Title & Introduction
• Fluid Substitution Model Using Gassmann's Equation
• 2. Import Required Libraries
• 3. Define the Gassmann Fluid Substitution Function
• 4. Implement Gassmann’s Fluid Substitution Function
• 5. Generate Example Input Data
• 6. Define Input Parameters & Run Fluid Substitution
• 7. Visualize Results
• 8. Plot the Changes in Rock Properties
• 9. Summary & Conclusions

Free snippet (1 of 4 code cells):

# Define rock & fluid properties
vp1 = 3500  # Initial P-wave velocity (m/s)
vs1 = 2000  # Initial S-wave velocity (m/s)
ro1 = 2.35  # Initial bulk density (g/cc)
phi = 0.2   # Porosity (20%)
k0 = 36     # Mineral bulk modulus (GPa)

# Initial fluid: Brine
rofl1 = 1.1  # Brine density (g/cc)
kfl1 = 2.5   # Brine bulk modulus (GPa)

# New fluid: Gas
rofl2 = 0.2  # Gas density (g/cc)
kfl2 = 0.1   # Gas bulk modulus (GPa)

# Apply Gassmann fluid substitution
vp2, vs2, ro2 = gassmnv(vp1, vs1, ro1, rofl1, kfl1, rofl2, kfl2, k0, phi)

# Display results
print(f"Old Vp: {vp1:.2f} m/s |", f"New Vp: {vp2:.2f} m/s")
# ...

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Jupyter notebook · 11 code cells · 9 figures

• 1. Introduction
• Vp Template of Overpressured Shale: Calculation Based on Different Pore Pressure Gradients
• 2. Generate example seismic vp data
• 3. Define Depth and Environmental Properties
• 4. Density Trend Calculation
• 5. Overburden Stress Calculation
• 6. Temperature Profile
• 7. Geologic Age Estimation
• 8. Beta Function Calculation
• 9. Pore Pressure and Effective Stress Template Estimation
• 10. Converting Effective Stress to Porosity
• 11. Estimating Vp from Porosity
• 12. Compare Computed Vp Template with Seismic Vp
• Summary

Free snippet (1 of 11 code cells):

# Traugott equation parameters
rosfl = 1.8
a = 0.35
b = 0.25
c = 3125

# Compute density trend
ro = rosfl + a * (zbml * 3.2808 / c) ** b

# Plot Density Trend
plt.figure()
plt.plot(ro, zbml)
plt.xlabel('Density (g/cc)')
plt.ylabel('Depth BML (m)')
plt.axis([1, 3, -300, 8300])
plt.gca().invert_yaxis()
plt.title('Density Trend vs Depth')
plt.show()

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Jupyter notebook · 5 code cells · 2 figures

• 1. Import required libraries
• 2. Define Example Input Data
• 3. Data Standardization
• 4. Build and Train the DNN Model
• 5. Make Predictions and Convert to Original Scale
• 6. Key Takeaways

Free snippet (1 of 5 code cells):

# Select features and target variable
X = df[['Depth', 'SeisVp', 'Temperature']].values  # Feature matrix
y = df['PorePressure'].values  # Target variable

# Standardizing features
X_mean, X_std = X.mean(axis=0), X.std(axis=0)
X_standardized = (X - X_mean) / X_std

# Standardizing target
y_mean, y_std = y.mean(), y.std()
y_standardized = (y - y_mean) / y_std

# Check standardized data
print("Feature mean after standardization:", X_standardized.mean(axis=0))
print("Feature std after standardization:", X_standardized.std(axis=0))

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Jupyter notebook · 10 code cells · 4 figures

• Introduction
• 1. Import Required Libraries
• 2. Define Rock Physics Parameters
• 3. Compute Porosity Using Athy’s Equation
• 4. Define Vclay Range
• 5.Compute Bulk & Shear Modulus Using Mixing Laws
• 6. Compute Solid Phase Density and Vp
• 7. Visualizing Bulk and Shear Moduli Trends
• 8. Compute Vp Using RHG Equation
• 9. Plot Porosity vs Depth
• 10. Plot Vp vs Depth
• Summary

Free snippet (1 of 10 code cells):

# Bulk and shear moduli (GPa) and density (g/cc) of minerals
k1, g1, d1 = 36.6, 45, 2.65  # Quartz
k2, g2, d2 = 21, 7, 2.58  # Clay
kf, rhof = 2.6891, 1.05  # Water properties

# Define depth range (Below Mud Line, BML)
zbml = np.arange(0, 8000, 1)

# Water depth and density
wd = 100  # Sea water depth (m)
row = 1.03  # Sea water density (g/cc)

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