Mohammed Asibelua: The Impact of AI on the Global Oil and Gas Industry
As the executive chairman of Equinox Group Ltd, Mohammed Asibelua recognises all too well the benefits that AI and automation present not only for his company but for the oil and gas industry as a whole, as well as the Nigerian economy. This article will explore some of the many ways that AI is poised to transform the oil and gas sector.
One aspect where AI poses huge potential is in demand forecasting. Capable of processing and analysing vast tranches of data at lightning speed, AI can predict demand surges by analysing information from past projects and global energy needs, enabling oil and gas companies to allocate resources more efficiently, thereby maximising profits.
Historically, the search for oil and gas reserves has been a challenging and expensive venture. However, thanks to AI, oil and gas exploration is benefiting from vastly improved accuracy and efficiency. Leveraging the power of AI and machine learning technologies to analyse extensive datasets, oil and gas companies can more easily detect anomalies and patterns indicating potential oil reservoirs. By analysing electromagnetic and seismic data, reservoir engineers can discover new hydrocarbon deposits, avoiding the need for risky, expensive and time-consuming traditional exploration practices that are prone to mistakes, relying heavily on human fieldwork.
Due to the hefty costs involved, oil and gas companies seek success in drilling operations on the first attempt. Automated drilling could increase extraction rates by relying on predictive intelligence to convert cross-sourced real-time and historical data into actionable insights for drilling preparation. Leveraging advanced machine learning algorithms, geosteering teams analyse terabytes of historical data to configure optimal parameters. Meanwhile, neural networks predict the likelihood of stuck pipe events, implementing preventative measures by leveraging real-time drilling data.
Another aspect where AI shows significant potential is in overseeing E&P equipment and scheduling maintenance activities. Traditionally, oil and gas producers dedicated huge amounts of resources to the task. However, in reality, manual monitoring is error-prone, lacking the ability to leverage historical data and automated fault detection to identify patterns, predict failures and optimise operations. Fast forward to today, with many companies utilising drones and robots featuring cameras and tiny sensors to scan equipment components with laser precision. Corrosion, cracks and other signs of wear are identified by pre-trained machine learning models, enabling AI to perform meticulous real-time inspections 24/7 without human intervention.
When combined, heat, high pressures and flammable substances culminate in incredibly dangerous working environments. Over the years, oil and gas exploration exercises have led to many tragic safety incidents. However, by relying on virtual field assistants, technicians, drilling rig crews and well operators, companies gain access to critical information more easily and quickly without risking human lives. Conversational AI assistants are increasingly being integrated into field-friendly devices, ensuring round-the-clock availability and enhanced effectiveness in emergencies compared with call centres manned by human staff.
Implementing AI and machine learning algorithms not only improves operational efficiency for oil and gas companies but also helps to improve decision-making processes. By utilising big data analytics, companies can make smarter, better-informed decisions on where to drill and how to manage reservoirs.
Another aspect where AI and automation present game-changing opportunities for the sector is in the field of talent acquisition and retention and company culture. To be successful, a company must create and nurture a corporate culture that is innovative and embraces change. Market leaders not only focus on attracting new talent but also invest in training for their current employees, helping them to develop their skills in AI, machine learning and data science. After all, even the best-laid AI strategy means nothing without the people needed to implement it.
