pyResToolbox MCP Server

Convert Dykstra-Parsons beta to Lorenz coefficient.

HETEROGENEITY CONVERSION - Converts beta parameter to Lorenz coefficient. Essential for converting literature data and comparing reservoirs using different heterogeneity metrics.

Parameters:

• value (float, required): Dykstra-Parsons beta coefficient (0-1). Must be 0 ≤ β ≤ 1. Typical: 0.3-0.8. Example: 0.6 for moderate heterogeneity.

Dykstra-Parsons Beta (β):

• Permeability variation coefficient (dimensionless, 0-1)

• β = (k50 - k84.1) / k50

• Based on log-normal permeability distribution

• Requires permeability data (core, logs)

• Common in literature and older studies

Lorenz Coefficient (L):

• Ranges from 0 (homogeneous) to 1 (completely heterogeneous)

• Based on cumulative flow capacity vs cumulative storage capacity

• Directly measurable from production data

• More intuitive for production analysis

Typical Ranges:

• β < 0.5: Low heterogeneity (L ~ 0.2-0.3)

• β = 0.5-0.7: Moderate (L ~ 0.3-0.5)

• β > 0.7: High heterogeneity (L > 0.5)

Use Cases:

• Literature Conversion: Convert published beta values to Lorenz

• Reservoir Comparison: Compare reservoirs using different metrics

• Simulation Input: Convert beta to Lorenz for simulation models

• Reservoir Analog Studies: Use analog beta values with Lorenz-based tools

• Historical Data: Convert old Dykstra-Parsons studies to modern metrics

Returns: Dictionary with:

• lorenz_coefficient (float): Lorenz coefficient (0-1)

• beta (float): Input beta coefficient

• method (str): "Dykstra-Parsons to Lorenz conversion"

• inputs (dict): Echo of input parameters

Common Mistakes:

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

• Confusing beta with other variation coefficients

• Using beta from wrong distribution (must be log-normal)

• Not understanding that conversion is approximate (depends on distribution)

Example Usage:

Result: L ≈ 0.4-0.5 (moderate heterogeneity).

Note: Conversion assumes log-normal permeability distribution. For non-log-normal distributions, conversion may be less accurate. Always validate against actual production data when possible.