The role of AI and machine learning in transforming the oil and gas industry

In the evolving landscape of the oil and gas industry, the integration of artificial intelligence (AI) and machine learning (ML) technologies is increasingly shaping how businesses optimise their operations. The potential for digitalisation to create efficiency, consistency, and performance improvements has dominated discussions within the sector, particularly in upstream production.

The conversations around these advanced technologies arise amidst a critical transition phase where the adoption of AI and ML solutions is uneven across the industry. While some companies are still in the nascent stages of experimenting with these innovations, others have made significant strides in embedding them into their daily operations. The journey towards fully digitalised operations necessitates a fundamental shift in mindset, moving from mechanical to digital perspectives—a change that can be challenging.

Historically, the industry witnessed similar transitions in the 1990s when the focus shifted to gathering sensor data for performance analysis. This evolution from reactive to predictive maintenance demonstrated the value of leveraging performance data for informed decision-making, leading to improvements in operational efficiencies.

Understanding terms associated with digitalisation is crucial. A significant number of industry stakeholders still grapple with the definition of digitalisation, making it challenging to engage in meaningful discussions on its application and value. AI, a central pillar of digital technology, is designed to mimic human intelligence in tasks such as natural language comprehension, pattern recognition, and decision-making. Within oil and gas, AI applications enhance well planning, detect hazards, and optimise drilling processes.

Machine learning, a subset of AI, utilises statistical algorithms to recognise patterns and perform tasks autonomously. Such technology is pivotal in identifying equipment wear and potential failure conditions, proving invaluable in optimising operational processes.

Another game-changing aspect of AI and ML is their ability to support the creation of digital twins—digital replicas of physical systems—that merge real-time data with predictive analytics, providing actionable insights throughout the lifecycle of projects.

According to Baker Hughes, these technologies fuel automation, minimising human intervention and significantly enhancing operational efficiency. The extraction of value from automating processes is paramount, especially as the industry grapples with a dwindling workforce; more reliance on technology is seen as a solution to bridge gaps left by retiring skilled workers.

The operational benefits of automation are substantial. Deploying smaller onsite crews not only reduces the carbon emissions associated with transport but also ensures worker safety by minimising on-site personnel, thereby limiting the chances of injuries or incidents.

However, while automation has realised considerable advantages in downstream operations, upstream sectors have yet to fully embrace these technologies. Concerning safety and the complex nature of operational environments are primary sources of hesitation among stakeholders.

Despite these hesitations, instances of successful automation deployment continue to grow. As more discrete processes become automated, confidence among stakeholders is being built. Baker Hughes has been at the forefront, showcasing the potential of intelligent tools for data-driven insights in well planning, and emphasising the need for a cohesive platform that integrates numerous functions along the well construction process.

A specific case in point is the implementation of the i-Trak drilling automation service by Baker Hughes, which enhances wellbore placement through real-time data analysis. This service utilises both a paired hardware-software system and edge computing to facilitate comprehensive automation in the drilling process. Its successful application in Norway’s Johan Sverdrup field exemplifies how automation can drive cost-effective operations, achieving an impressive reduction in expenditures while maintaining drilling accuracy.

In parallel, companies like Baker Hughes are leveraging AI to enhance electric submersible pumps (ESPs) through predictive analytics aimed at identifying potential operational failures. Their method includes an ensemble model that integrates various failure conditions into a comprehensive predictive framework, which has yielded thousands of critical condition alerts.

Looking ahead, the drive towards automation in oil and gas is expected to gather momentum. The advent of generative AI, coupled with advances in artificial reasoning, promises to broaden the scope of digitalisation, integrating a vast array of informational sources and enhancing operational reliability. As processes become increasingly sophisticated, construction of a standardised approach to these technologies is anticipated to promote wider adoption, paving the way for the future of highly autonomous oil and gas operations.

In this landscape of continual evolution, the industry is on the horizon of significant transformation, driven by technological advancements that seek to improve both productivity and environmental stewardship.

Source: Noah Wire Services