The three projects will be a collaborative effort with Robert Gordon University of Computing Science and Digital Media (RGU) to carry out research into the digital transformation of oil fields.

The technical advisor to the global oil and gas industry, DNV GL, is in the process of developing an interactive programme that extracts and processes information from images of piping and instrumentation diagrams and other types of engineering drawings. The purpose of the programme is to speed up the collection of data for use in a number of technical applications.

Ian Phillips, CEO of Oil & Gas Innovation Centre (OGIC), said: “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.

“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.”

The first phase of the project was finished with support from The Data Lab, while the second is being primarily supported by the OGIC. RGU and the OGIC will collaborate to build on the methods and algorithms developed by the first phase of the project.

Utilising Machine Learning to Tackle Industry Problems

ComplyAnts, a data analytics company is working to develop an automated system to manage the compliance process. While RGU will harness the capability of AI to develop an automated system to manage end-to-end compliance process pipeline. The aim of the project is to deliver a fully functional prototype within 12 months.

As part of phase two, software company IDS is working to develop a data-driven tool to predict task durations, associated risk and NTP. During phase one the project developed 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.

Phillips added: “RGU’s School of Computing Science and Digital Media has a wealth of expertise and its involvement in these three projects is testament to this. Two of the three projects have also received support from another of the Scottish innovation centres, The Data Lab, and they are excellent examples of how the innovation centres can work together to support the development of disruptive technology.”

Dr Eyad Elyan, a reader in machine learning and the project academic lead at RGU, added: “This is another great opportunity which enables our team to apply cutting-edge research in machine learning to solve challenging industrial problems by intelligently mining and exploiting large volumes of structured and unstructured data such as images, text documents and others.

“Such projects have the potential to significantly improve existing business practices and can demonstrate the quality of research and teaching taking place at the university.”