Data Cognitive With Machine Learning In Oil & Gas

The Setback

​Most of the Digital Transformation in Oil & Gas requires the data to be structured in meaningful to any analytical and Business Intelligent (BI) tools. Businesses need to conduct data hunting, cleaning and organising the data in making sure it meets a certain requirement or business’ rules. Creating a good NLP and machine learning model to solve a real-world problem can be a challenging task, this process cannot be started without proper planning and having a comprehensive tool that will enable the data management team to handle complex legacy data extraction.

The Solution

Using data cognitive tools with the power of intelligent Optical Character Recognition (OCR) is a good approach to address the challenges. Firstly, is to enable all of the legacy documentation and reports to be cognitive searchable. This step is important in making sure that the data management team can save a lot of time in getting all of the information required to undergo proper data extraction from filtered reports.

 

Each report and technical documentation can be unique depending on who prepares it and from which company. By using cognitive extraction, machine learning can help humans in recognizing multi-type information representation such as a table, graph, key-value pair, and paragraph. We can train a machine learning model to firstly extract said information automatically and send them directly to Subject Matter Experts (SME) to review and verify the data accuracy.

 

Having a good combination of cognitive tools with a proper understanding of data management projects can help oil and gas companies to expedite the first and most important step in digital transformation initiatives.

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