Q-SCAL
Q-SCAL is a new product for loading, storing and analyzing all types of conventional and special core analysis data.
NEWS
March 2011: LogicomEP complete Q-SCAL geomechanical integration with Petris Recall ™.
Core data and in particular special core analysis laboratory measurements are central to both static and dynamic reservoir evaluation. But the data are often sparse, poorly understood and incorrectly deployed. Q-SCAL is a database and set of analysis tools to allow you to maximise the integrity and value of your SCAL data.
Q-SCAL’s internal database is a standard relational type, so most compliant 3rd party software should be able to access the data. There is also a seamless link to the Petris Recall™ system, so users of this industry standard database have instant access to all of Q-SCAL’s data and analysis tools.
In today’s hectic industry, by no means the least important aspect of Q-SCAL is the speed at which data loading and analysis can be performed. Traditional two or three day jobs can now be done in hours, a huge saving in cost and time.
PROGRAM FEATURES
Data Import
Most core analysis data are presented in XL spreadsheets, and Q-SCAL reads directly from these. A set of templates distributed with the program describe many common formats; but you can also define your own. In this way, large quantities of often complicated data can be loaded in minutes.
Conventional data
Conventional core data can be plotted and analysed in common and unconventional ways. Flow unit techniques (FZI, RQI etc.) allow powerful modern poro-perm relationship analysis.
Capillary Pressure
The capillary pressure workflow is a particular feature of Q-SCAL:
- Data corrections include closure, stress, clay, equilibrium state and other adjustments, depending on the type of cap.pressure experiment. Quality control is an essential element of data preparation.
- Conversion to reservoir conditions (saturation-height) is an interactive process where you can visually understand the influence of surface tension, contact angle and other reservoir variables, validating your decisions against supporting data.
- Estimation of residual saturations based on reservoir closure or other criteria.
- Typing of sample data by Lambda, Thomeer , Skelt-Harrison, J-Function and other common algorithms.
- Group fitting of field-, well- or lithology-based datasets on porosity or permeability criteria.
- Alternatively, you can search for relationships between saturation-height fitting parameters and other sample characteristics – porosity and permeability of course, but also grain density, lithology and so on.
- Generation of type curves by statistical or visual analysis of the data set allows you to optimize your saturation height functions, vital for
