With the Quanti.Elan module, users can save and then easily reapply models to new data—the ability to combine outputs from several models improves the accuracy of final results. Models can also be transferred to other projects and curves can optionally be used instead of constants for end-point, curve uncertainties, and other control parameters.
The ELAN solver has been implemented to provide a robust and powerful method for accurate and reproducible results.
- Initialization of temperature, pressure, salinity, and porosity dependent parameters in the Initialization module.
- Resistivity models: Archie, Dual Water, Juhasz, Waxman-Smits, Simandoux, and Indonesia are available.
- Sonic models: Wyllie, Raymer-Hunt-Gardner, Raiga-Clemenceau, field, and velocity equation are available.
- Neutron equations: This provides linear and nonlinear response functions for the wireline and LWD neutron tools of the major service companies.
- Geochemical logs support in a form of dry weights for any service company and relative yields for Schlumberger tools.
- Embedded postprocessing for computation of formation properties such as porosity, Sw, permeability.
Models may be defined as single mineral sets per zone. Multiple mineral sets per zone can be established, with sets switching automatically according to a partitioning curve that changes as the log facies change. Interactive parameter management, such as for wet clay, is also available. Solutions can be constrained against a priori information (e.g., XRD or CEC data); both single- and multicomponent volume constraints are possible.
- Detailed automatic layout (fully customizable by the user).
- Unique array-histograms to clarify data relationships by plotting all components against input log data or log data residuals.
- Juhasz and m* plots.
- Output result curves (e.g., mineral volumes, Sw, and Φ), characterized with calculated uncertainties relating to choice of model components and parameters.
- Sensitivity analysis with Tornado plot to investigate contribution of different parameters in the model.