  Raster well-log images are digital representations of well-logs data
generated over the years. Raster digital well logs represent bitmaps of the log
image in a rectangular array of black (zeros) and white dots (ones) called
pixels. Experts study the raster logs manually or with software applications
that still require a tremendous amount of manual input. Besides the loss of
thousands of person-hours, this process is erroneous and tedious. To digitize
these raster logs, one must buy a costly digitizer that is not only manual and
time-consuming but also a hidden technical debt since enterprises stand to lose
more money in additional servicing and consulting charges. We propose a deep
neural network architecture called VeerNet to semantically segment the raster
images from the background grid and classify and digitize the well-log curves.
Raster logs have a substantially greater resolution than images traditionally
consumed by image segmentation pipelines. Since the input has a low
signal-to-resolution ratio, we require rapid downsampling to alleviate
unnecessary computation. We thus employ a modified UNet-inspired architecture
that balances retaining key signals and reducing result dimensionality. We use
attention augmented read-process-write architecture. This architecture
efficiently classifies and digitizes the curves with an overall F1 score of 35%
and IoU of 30%. When compared to the actual las values for Gamma-ray and
derived value of Gamma-ray from VeerNet, a high Pearson coefficient score of
0.62 was achieved.
