Data is the foundation of a successful modeling project. We all have had an experience where high quality, mistake-free data was not available for a project and lesser quality, problematic data was the only thing available. These problematic datasets can contain a variety of issues not limited to missing data values, missing labels, incorrect data values, and outliers; all of which can cause inaccurate projects. With the release of a the latest version of Voxler 3D Visualization mapping software this past fall, the new worksheet window allows real-time editing of your imported data; Now you can leverage Voxler as a valuable data quality control tool for correcting problematic 3D point cloud data and well data. This blog will detail how to effectively QC (quality control) the problematic data issues directly within Voxler's mapping software using the new worksheet window.
QC missing data values:
Datasets can come with missing values caused by a variety of reasons including creator error or equipment malfunction. This situation is typically seen in well data where entire intervals of sampled data are missing. For example, in the image below there are a few sample intervals missing for well MW-1; shown by the missing “gaps” along the well trace.
Missing interval data can be seen on well trace MW-1.
The “gaps” in the interval data can also be seen in Voxler’s worksheet by clicking the Edit Worksheet button in the Property Manager. In the image below the missing interval data is highlighted.
Missing interval data can easily be found in Voxler’s worksheet.
This missing interval data issue can be easily resolved in Voxler by using the following steps:
The corrected or quality controlled data can be exported from the Worksheet window by using the File | Save As Copy command. The image below displays well MW-1 with the corrected interval samples data.
The missing interval data has been added to the worksheet and now displays along the well trace.
QC missing labels:
Occasionally, your acquired data does not contain a complete column of labels. This can be easily resolved by using similar steps as used to resolve the missing data values in the previous section. Here are the steps that can be used:
QC incorrect Well IDs:
Receiving data that uses a different method to uniquely identify well data is a common issue when combining well data that has been created from different data providers. Commonly some data providers use the well name while others use API number to identify wells. When importing well data that uses two different methods to identify wells, the well traces will not render because there is no way to link the collars table, the directional survey table, and the samples table. In the image below you can see the two well data tables are using two different identifiers for the wells; one is using the well name, the other is using the API number.
Different unique well identifiers in a collars table and directional survey table.
This issue can be easily fixed by editing one or more of the well data tables so the well IDs match. I recommend adding an additional column to the data so that both unique identifiers will be referencing the data. This can be accomplished by using the following steps:
A new column of well names had been added to a collars table so both tables have matching unique identifiers.
In conclusion, when facing missing or incorrect data issues within your 3D point cloud dataset or well dataset, Voxler’s new hot editing worksheet can help you quickly solve these issues. New copies of Voxler and upgrades from previous versions are available for purchase from our shopping page. Contact firstname.lastname@example.org with any suggestions or questions you may have!
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