pyResToolbox MCP Server

Generate layered permeability distribution from Lorenz coefficient.

LAYER PROPERTY GENERATION - Creates detailed layer-by-layer permeability and thickness distribution matching specified heterogeneity. Essential for building reservoir simulation models and predicting waterflood performance.

Parameters:

• lorenz (float, required): Lorenz coefficient (0-1). Must be 0 ≤ L ≤ 1. Typical: 0.2-0.7. Example: 0.6 for moderate heterogeneity.

• nlay (int, required): Number of layers to generate. Must be > 0. Typical: 5-50. Example: 10 for 10-layer model.

• k_avg (float, required): Average permeability in mD. Must be > 0. Typical: 10-1000 mD. Example: 100.0 mD.

• h (float, optional, default=100.0): Total thickness in feet. Must be > 0. Typical: 50-500 ft. Example: 100.0 ft.

Method: Uses Dykstra-Parsons log-normal permeability distribution with correlation to Lorenz coefficient to generate realistic layer properties:

1. Convert Lorenz to beta parameter

2. Generate log-normal permeability distribution

3. Sort layers by permeability (ascending)

4. Assign equal thickness to each layer

5. Calculate layer statistics

Output Properties: For each layer:

• Thickness (ft): Layer thickness (equal for all layers)

• Permeability (mD): Layer permeability (log-normal distribution)

• Thickness Fraction: Fraction of total thickness

• kh Fraction: Fraction of total flow capacity (k × h)

Statistics Calculated:

• k_min, k_max: Minimum and maximum permeability

• k_avg, k_median: Average and median permeability

• k_std: Standard deviation

• Heterogeneity ratio: k_max / k_min

Critical for:

• Reservoir Simulation: Generate layer properties for simulation models

• Waterflood Prediction: Predict sweep efficiency and recovery

• Vertical Sweep Efficiency: Analyze vertical conformance

• Conformance Studies: Evaluate production allocation

• Upscaling: Create coarse-scale models from fine-scale data

• Sensitivity Analysis: Test impact of heterogeneity on performance

Usage Example: For 10-layer simulation model with Lorenz=0.6:

Result: 10 layers with permeabilities ranging from ~20 mD (low-k) to ~500 mD (high-k), each with 10 ft thickness. High-k layers have higher kh fractions.

Returns: Dictionary with:

• layers (list): List of dicts with layer properties (thickness, permeability, fractions)

• statistics (dict): Permeability statistics (min, max, avg, median, std, ratio)

• total_thickness_ft (float): Total thickness

• average_permeability_md (float): Average permeability

• lorenz_coefficient (float): Input Lorenz coefficient

• number_of_layers (int): Number of layers

• method (str): "Dykstra-Parsons log-normal distribution"

• note (str): Usage guidance

• inputs (dict): Echo of input parameters

Common Mistakes:

• Lorenz coefficient outside valid range (must be 0-1)

• Too few layers (<5) causing poor resolution

• Too many layers (>50) causing unnecessary complexity

• Wrong average permeability (must match reservoir average)

• Not understanding that layers are sorted by permeability

• Confusing thickness fraction with absolute thickness

Example Usage:

Result: 10 layers with log-normal permeability distribution, each 10 ft thick. Permeability ranges from ~20 mD to ~500 mD, matching Lorenz=0.6 heterogeneity.

Note: This generates idealized layer properties assuming log-normal permeability distribution and equal layer thickness. For actual reservoirs, use measured core or log data when available. Layer properties are ready for direct use in reservoir simulation models.