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New digital tech projects get support from OGIC

The Oil & Gas Innovation Centre (OGIC) is supporting three new research projects centred on how digitalisation can improve efficiency and provide cost savings to the oil and gas industry.

‘Digitalisation is key to a sustainable oil and gas industry. Successful automation and integration of a huge range of tasks across many aspects of the exploration and production cycle are now possible through the ability to rapidly process vast quantities of data in very short periods of time,’ says Ian Phillips, Chief Executive Officer of OGIC. ‘OGIC is supporting three projects researching new approaches to exploration tasks which will reduce costs and increase efficiency and, ultimately, production in a less labour-intensive way.’

Three companies have teamed up with Robert Gordon University’s (RGU) School of Computing Science and Digital Media to carry out research into the digital transformation of the oil field.

DNV GL is developing an interactive programme extracting and processing information from images of piping and instrumentation diagrams and other types of engineering drawings. This will speed up the collection of data for use in a number of technical applications. Phase one of the project was completed with support from The Data Lab, with phase two being primarily supported by OGIC. Working with RGU, phase two will build on the methods and algorithms developed by phase one of the project.

Meanwhile, multidisciplinary data analytics company ComplyAnts is working to develop an automated system to manage the compliance process. Artificial intelligence will be used to develop an automated system to manage the end-to-end compliance process pipeline. The project aims to deliver a fully functional prototype within 12 months.

Software company IDS is working to develop a data-driven tool to predict task durations, associated risk and non-productive time (NPT). This is phase two of the project. Phase one, which was supported by The Data Lab, saw the development of a natural language processing (NLP) library which classifies engineering terms within a daily report. These are then mapped to allow benchmarking and data analysis. This will reduce the amount of time it takes engineers to work with offset data.

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