1887

Abstract

Summary

Formation Evaluation Logs (FEL) and composites integrate together a lot of information gathered together by the well-site geologist while drilling and logging. But, since they are frequently published as unstructured documents, they are not easy to use as a source of information in digital business processes.

We had the opportunity to support our customer Equinor to “read” lithological columns, O&G show symbols, and geological descriptions from FEL and composites using a state-of-the-art computer vision approach called YOLO and our indexing solution named iQC. A process based on YOLO and iQC transforms the graphical information into usable, numeric and text values that can be consumed by business databases. Computer vision and semantic analysis models were trained on composite logs, which were tagged by subject matter experts with expected labels. The developed models automatically detect and draw bounding boxes around target objects in test documents. This paper details this experiment, lessons learnt and provides some perspective to improve the accuracy of the first results obtained.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201901970
2019-06-03
2020-04-03
Loading full text...

Full text loading...

References

  1. Alexey, A.B.
    , Windows and Linux version of Darknet Yolo v3 & v2 Neural Networks for object detection (Tensor Cores are used)https://github.com/AlexeyAB/darknet
    [Google Scholar]
  2. Blinston, K.
    , H. Blondelle, Machine Learning Systems open up access to large volumes of valuable information lying dormant in unstructured documents in The Leading Edge, March 2017
    [Google Scholar]
  3. Redmon, J., Farhadi, A.
    , Yolo v3 an incremental improvement. https://pjreddie.com/media/files/papers/YOLOv3.pdf
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201901970
Loading
/content/papers/10.3997/2214-4609.201901970
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error