This oil industry customer wanted to unlock hidden data from handwritten historical well drilling records. Hidden data includes depth of drilling and the quality of the underground layers. These archived documents were scanned into images. The images contain handwritten notes as well as depth and well drawings done by hand. The company wants to research this data to assess the feasibility and the success probability of future drilling in certain areas.
Moonoia proposed the docBrain AI-powered recognition technology to address these very specific needs. A dataset of around 6000 documents was used for training the neural networks. Handwritten text was examined for predefined keywords to be transformed in WITSML data.
Moonoia showed that the expected results can be produced without the need for data science or business intelligence. Moreover, improved research results from complex interrogations were achieved without with significantly less manual data extraction.
In time, the results of this project / use case will lead to a docBrain evolving solution to facilitate easy research on other kinds of documents / data in completely different fields.