IDQI – Improving the quality of numerical data through the integration of artificial intelligence –

Ambitious partnerships between academia and industry have brought about breakthroughs in software technologies that improve the quality, reliability, and ease of use of numerical data from a variety of sensory sources in a wide range of industrial applications. This concept has been tested, validated, and validated using very large O & G industrial production and monitoring datasets.

Leading the project Data Lab – The Scottish Data and AI Innovation Center, Oil & Gas Innovation Center (OGIC), and software R & D firm HyperDAP Ltd. Aberdeen University also support the software R & D process.

The result is IDQI (Intelligent Data Quality Improvementr), an Ai-based algorithmic technology that can interpret and manage vast amounts of numerical data from heterogeneous sources.


Industry faces challenges in processing, processing, and interpreting increasing data. Many of the data-intensive industrial sectors are severely impacted by this “data flood” challenge, which puts them at risk of not being able to extract valuable information from other data sets that are produced or experimentally collected. Being exposed.

Data has become one of the most valuable assets a company can own, but without proper research and interpretation, it has little value.

HyperDap has invested in intelligent data quality improvement technologies to address numerical data quality issues in both the oil and gas industry and many other industrial sectors.

IDQI solution

The venture began in 2019 and has been running at the University of Aberdeen for over a year. This project demonstrated that numerical data can be evaluated, measured and improved in a broad framework of real-world conditions.

The project provided Intelligent Data Quality Improvementr, a highly innovative artificial intelligence algorithm technology for data quality management and analysis. Originally implemented as an algorithmic technology for oil and gas data analysis systems, IDQI uses AI to guide context-dependent quality improvements for large numerical production datasets from a variety of sensory sources.

The HyperDAP team is a unique combination of experienced young professionals who share a wealth of expertise in AI and full-stack development, ideal for taking this project and its underlying software technology to the next level. ..

IDQI has three innovations and uniqueness.

  1. It measures numerical quality and suggests strategies to improve if it falls below a user-defined level of context dependency.
  2. Evaluate the effectiveness of data quality improvement techniques and suggest alternatives if the quality of the improved dataset is still below your goals.
  3. Learn about user decisions related to the proposed data quality improvement strategy and use this knowledge to guide and improve subsequent strategies.

These innovations offer users of IDQI algorithms the possibility to optimize the quality of existing production datasets in context and use the results to assess the impact of various computational workflows, much more than they do today. Allows you to effectively reuse data over a wide area.


IDQI uses artificial intelligence-based software technology to manage large data sets to overcome the limitations of current systems. This example is the lack of automated data quality assessment and improvement capabilities, and the automated suggestion of optimal workflows based on actual data quality.

The platform works on the quality of the data being analyzed and its improvements, focusing on the intelligent specifications of various workflows for meaningful results. It automatically learns the user’s decisions about the workflow proposed by the machine and uses this knowledge to guide the optimal specifications for subsequent workflows.

This innovation uses advanced AI technology to do the following:

  1. Improve the quality of production data and data analysis.
  2. Allows users to perform various workflows in real time.
  3. Leverage machine suggestions to identify the best solution.

This allows users to directly specify, program, and execute their own workflows without relying on third parties to get the most out of machine suggestions. Current systems do not currently include automated mechanisms for assessing and improving data quality.

The oil and gas sector used as a testbed has an indirect positive environmental impact. This technology has the potential to be useful in a variety of state-of-the-art applications, such as analyzing well integrity and performance to improve safety and reduce the likelihood of failure. Operators can also identify anomalies that cause environmental pollution from underground to seawater.

Today, many sectors are creating large numerical datasets ranging from gigabytes to terabytes every day. The oil and gas sector is just one such producer, but it is not the only one. The IDQI project has the potential to revolutionize data interpretation in all areas, providing greater insight than ever before.

What they say

The project was subject to independent peer review as part of the oil and gas innovation center’s approval process, and industry experts were invited to comment on the project’s innovation and applicability. One reviewer wrote:

“Hydrocarbon accounting is an area of ​​business where small changes in data processing effectiveness and system quality can have a disproportionate impact on financial gain / risk mitigation / accounting robustness.

“That’s why it’s an ideal area for investing in pursuing business benefits and applying new technologies. This business area is overwhelmingly bespoke systems with very different vintage and technology, and is a niche in this area. It is also characterized by the lack of competition in the service sector, which provides essential but essential professional services. Therefore, it is beneficial to bring new ideas, new technologies and new players to the service sector. Incorporating the latest computer science technology from academia. That can confuse this market, which is likely to require some innovation. Good luck with your project. We look forward to hearing about its success. “

In the words of The Data Lab

Gillian Docherty, CEO of The Data Lab, said: This isn’t just the oil and gas sector. We encourage industry leaders from all sectors to contact the data lab to discover how data can enhance their R & D work.

“We congratulate HyperDap and everyone at the University of Aberdeen for their perseverance in making this project a reality. We look forward to further development of IDQI.”

IDQI – Improving the quality of numerical data through the integration of artificial intelligence –

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