Even the most expert geologist needs good data to do good work. We’ve all heard the aphorism “garbage in, garbage out,” but how are you supposed to know what data is a mistake and what data is significant?Those unfamiliar with the logging process ask: “Why in this day and age, are there mistakes in log readings? How can a logging company keep customers when their data is inconsistent and delivered with errors?”
One reason is that there’s just not time to address the problems. Geologists are busy and can’t follow up minor mistakes on each log. It’s too late to have the log redone, and the show must go on.
What are some common errors in logs?
- A non-standard value was used for null data – The LAS standard is to represent nulls as -9999, -999.25, or -9999.25. If the vendor uses another value and fails to list it in the ‘NULL.’ section of the LAS header, it will be read as a real data point.
- Reported with non-standard units – It’s easy to miss a single well reported with unexpected units while importing a batch of logs. Gamma ray logs are reported in American Petroleum Institute units, but this is calibrated based on a specific response to a setup at the calibration facility at the University of Houston. Logs from other countries may be reported by a different standard and a reliable conversion factor from one standard to another may be nonexistent.
- Digitization error – Portions of a log may be repeated twice, excluded entirely, or have miskeyed values.
- Borehole size not taken into account – A reading with higher counts can be expected in a smaller borehole. Tools must be calibrated correctly for the size of borehole they will be reading.
- Environmental disturbance not taken into account – Certain chemicals in the drilling mud, as well as the casing itself, can affect log readings and must be accounted for.
So what’s a geologist to do?
Fortunately, DI Transform has a strong suite of visual tools for well log QC and correction. All of these adjustments can be applied to all wells, to a well list, or to a single well. These modifications can be saved for future use should more logs be imported.
Figure 1: In DI Transform Essential, you can create a histogram of log data for an entire project.
Figure 2: Filter by data range to compare log values in a zone of interest.
Figure 3: Zoom in to the lower regions of the bin probability axis to view outliers. Ctrl + click allows you to select the outlier bins and create a list of well logs to investigate further.
Figure 4: You can bulk replace bad values and normalize or smooth a log via the DI Transform Well Log Calculator
Figure 5: Clip or redraw data graphically in the well log editor.
There’s no such thing as a perfect log, so reducing the tedium surrounding log QC is the best we geologists have for now. DI Transform’s powerfully visual tools make it easy to build a solid project you can have confidence in.
Try it now.
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