Quanti.Elan
the multi-component inversion model, empowered by the ELAN experience
Workflow
Incorporating the ELAN solver, the Quanti.Elan multicomponent inversion module allows users to benefit from tried-and-tested algorithms within a modern and easy-to-use interface. Precomputation of salinity- and temperature-dependent properties is now built in to assist with parameter initialization. Multicomponent models with both linear and nonlinear tools response functions can also be built.
Users can save and then easily reapply models to new data, while 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.
Solutions
The ELAN solver has been implemented to provide a robust and powerful method for accurate and reproducible results.
- Sequential Quadratic Programming: This powerful family of robust, nonlinear optimization methods from the previous version remains.
- Resistivity models: Archie, Dual Water, Juhasz, Waxman-Smits, Simandoux, and Indonesia are available.
- Sonic models: Wyllie, Raymer-Hunt-Gardner, Raiga-Clemenceau, and Field equation are available.
- Neutron equations: This provides linear and nonlinear response functions for the wireline and LWD neutron tools of the major service companies.
Model design
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. “Special fluids” can be incorporated to account for the effect of barite in the drilling mud.
Outputs
- 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
Request More Information