With the Techlog Quanti module, you can perform log quality control and precomputations of fluid properties followed by a full petrophysical analysis. This workflow can be saved and reused for future work, incorporating Monte Carlo for uncertainty analysis.
Using the module for interactive log interpretation, it is possible to:
- Design your own petrophysical workflow.
- Save and quickly reapply workflows to new data.
- Easily transfer workflows to other projects.
- Resample on-the-fly, allowing variables from different datasets to be input to a workflow.
- Add your own scripts to further extend your workflows.
Log quality control
The Quanti module allows for the:
- Detection of pure minerals (e.g., coal and halite).
- Detection of borehole geometry effects (e.g., bad hole, oval hole, and rugosity).
- Flagging and treatment of environmental effects (e.g., tension pulls, barite, KCl, and washouts).
- Precomputations of fluid properties.
Graphical parameter selection
Multiwell control of petrophysical parameters can be achieved by setting defaults for well, dataset, and zone combinations. With graphical and tabular, multiwell and multizone parameter management, users have control at all times. Plots are dynamically linked to parameter tables, allowing graphical selection of equation parameter values. Using the cascade function, parameter values can be edited and effects monitored on subsequent results.
A comprehensive list of petrophysical computations (lithology, porosity, saturation, productivity, etc.) is available in the Quanti module. Parameter defaults can be defined at project, well, and zone levels and hierarchical parameter management facilitates scenario comparison. Users may insert scripts into the Quanti workflows for instant multiwell, multizone applications. Monte Carlo uncertainty modeling can also be performed.
Quanti workflows enable users to:
- Apply cutoffs to determine reservoir and pay quality rock.
- Calculate sums and averages over discriminated intervals.
- Create report quality tables by zone or by layer.
- Investigate sensitivities to the choice of cutoffs and uncertainties in results.
The module may be launched in Monte Carlo mode, to give repeated sampling from theoretical distributional forms. This method investigates uncertainty in the results and sensitivity to inputs.