The Rock Physics license has received several improvements for 2024.3
A few highlights:
With this software release, we're focused on making it easier to start RokDoc projects and execute multi-well workflows with fewer clicks. This is done through multi-well operations and by providing automated QCs so that users don't need to manually build plots to assess results. Overall, the time spent in the software is more effective and users can reach and incorporate deeper understanding of their data faster than ever.Project Startup Efficiency
Rapidly import existing time-depth relationships for all wells with TWT logs or use checkshot sets. This will allows users to more easily transfer data into RokDoc without re-calculating and re-verifying already-established time-depth relationships
Updated Rock Physics Model Workflows
With the recent refresh to the RPM interfaces, it's easier than ever to navigate rock physics workflows. Calibration parameters can be quickly documented with screed capture or by highlighting the text in the summary window and the RPM description can be positioned to the side while you work, for quick reference. Now, the rock physics models have certain parameters collapsed by default, so that the most important inputs are adjusted first. This should make working with complex models more straightforward. This functionality is available for external interface users as well, so custom RPMs can be similarly structured for simplicity and accessibility.
To save time on multi-well applications with faster modeling turnaround and testing, users can now apply rock physics models in forward (predict elastic properties) and reverse (predict petrophysical properties) across multiple wells simultaneously. In addition, reverse modeling can be run on up to 3 properties simultaneously (e.g. porosity, saturation, and mineralogy).
The RPML library now covers a larger family of theoretical models (Inclusion/DEM) and provides more QC tools for assessing the predicted classification and fundamental effectiveness of the model fits.
A new training function algorithm has been added to produce faster training for machine learning applications. Users can track the progress and accuracy of the training with automated QCs for the following algorithms:
We look forward to continuing to hear your feedback for future RokDoc releases!