In today’s reality, with low oil prices and the COVID-19 demand slump, oil and gas companies have been forced to embrace and integrate technology in their processes more urgently than ever before. While most companies already have adopted some digital technologies, some others still have a long way to go.

“After COVID-19, the pandemic has significantly accelerated the digital transformations underway in the oil and gas industry,” said Noor Alnahhas, CEO of technology startup Nybl. “Oil companies are looking for technology to enable them to become more efficient, do more with less, and be more profitable. O&G companies are typically late adopters of technology, but we’re seeing more interest and appetite to try new technologies.”

nybl is an “AI-ecosystem” entity based out of Khobar, KSA, has developed an artificial intelligence technology that aims to transform business efficiencies and expenditure by combining science-based machine learning, data-behaviour models, digital process simulations to provide solutions in an array of sectors including oil & gas, manufacturing, education, financial services, government services and healthcare, among others.

 “Essentially, we wanted to build a company that innovates world-changing technology. We emphasize the word technology because for us – technology is evolutionary. While today that technology is artificial intelligence, in five years it may be something else. As such, we’re focused on delivering solutions that are intuitive and science-led. To date we’ve created unprecedented technology that doesn’t require historical data to function, predict and diagnose,” he said.

Starting with only a team of 7 people in 2019, the company now employs over 25 people in UAE, KSA, and Kuwait. Nybl’s proprietary, science-based machine learning, data-behaviour models, and digital process simulations use technology to predict behaviours and risks.

The machine learning platform turns under-utilised data into actionable intelligence to drive efficiencies and help businesses optimise or automate workflows while providing real-time analytics.

nybl’s oil & gas application, lift.ai, is a real-time failure prediction, efficiency optimisation and production maximisation solution for artificial lift pumps. The app focuses on four different elements of optimisation: failure prevention – using their Failure Prediction Index (FPI), downtime reduction, extending machinery lifecycles, and increasing oil production – all of which they achieve using real time artificial intelligence.

“Artificial Intelligence is still a new player in the oil and gas industry,” Alnahhas added. “The main applications are around production, and health and safety. But, with the acceleration of digital transformations we are witnessing today, many interesting AI applications are emerging for O&G, from safety and operator training, to reservoir engineering and drilling.

According to Alnahhas, the biggest challenge in working with AI in the O&G sector is the lack of historical training data to enable proper machine learning (ML). Almost all data is either confidential, incomplete or not enough to enable proper training of ML models.

This challenge drove Nybl to find an alternative way to deploy AI and ML models, and they’ve now proven, successfully, that we can eliminate the need for historical training data by using science-based artificial intelligence. Not only does this eliminate the need for historical training data, but it also eliminates the bias that comes with the historical training data.

“These two achievements are on the cutting edge, and we’re the first company in the world to have multiple case studies proving it works,” he said.

CN-Shield is the company’s AI security solution, which Alnahhas said provides “oversight of any given physical location and offer pre-emptive security measures to protect an organisation’s critical assets.”

By collecting data from linked security systems, including CCTV feeds, access controls, and sensors, it can process that information to develop “complete situational awareness.”

“It then runs threat-tracking and mitigation measures and, when necessary, initiates automated countermeasures to nullify the threat, all while alerting security personnel,” he said. “In addition, the system provides intelligence on operational enhancement, efficiencies, and failure prediction related to all linked security systems.”

According to Alnahhas, machine learning and data science could be key to steering the industry towards a lower-carbon future by making carbon-emitting process more efficient to eliminate waste.