The report focuses on how different companies are using predictive analytics. It also analyzes to what extent companies are transforming their own technology and data capabilities to predict a better performing and sustainable future between operators, service providers and technology makers.

Key findings of the predictive analytics report include:

  • 57 of the world’s 100 largest oil and gas firms are using, or have plans to use, predictive analytics;
  • 34 of these 57 companies are using or have plans to use predictive analytics;
  • Companies using predictive analytics are benefiting: by $325,000 per rig using machine learning to predict drill-bit locations; by saving costs of $7 million on gas pipelines in eastern US through predicting failures;
  • Research proves predictive analytics are being tested and applied in: machine learning to improve safety improvement capabilities; in unconventional wells to change management attitude; behavioural modelling to reduce the frequency of safety incidents; exploring fully automated drilling platforms; automated analysis of subsurface data; designing the 'rig of the future';
  • Respondents believe Artificial Intelligence (AI) is unlikely to be applied beyond niche applications for another three (3) years.


Contributors to the report emphasized the critical areas for predictive analytics including demand forecasting, oil and gas trading, spare parts inventory management, transport route optimisation, process control and facility management.

Nial McCollam, Chief Technology Officer of LR, mentioned:

The Technology Radar report finds that many of the industry’s largest companies are actively developing predictive capabilities using internal and external resources. At least one-third of the top 100 industry players by size are generating a beneficial impact from using this technology. Sure, there is a long way to go, but adoption and new ways of thinking and working is becoming a reality

Moreover, by using large volumes of historic seismic and production data, current and new sensors, and powerful algorithms, companies are gaining insights that were not available to them. This has enhanced efficiency and productivity, reduced downtime, and earned them demonstrable returns along the way.

What is more, predictive analytics are now coming to bear in asset maintenance, human and equipment. According to Mr. McCollam, preventing events that lead to injury, loss of life or environmental damage is a key part of predictive analytics - and even more so in driving efficiencies and enhancing performance.

Finally, the report highlights how the experience of predictive analytics used in consumer markets can provide better insights on how more accurate predictions are generated when multiple sets of data from different sources are layered up.