From initial exploration activities to the end-user, the application of artificial intelligence (AI) in oil and gas is inspiring new ways of approaching exploration, development, production, transportation, refining and sales. In an era of transformation – or oil and gas 4.0 – the ecosystem of technologies and capabilities that make up AI has enormous potential to make the most out of data to unlock efficiencies, reduce downtime and solve problems around human talent.

With AI in oil and gas market valued at US$2 billion in 2019 and expected to reach US$3.81 billion by 2025, more oil and gas organisations are integrating AI applications into their upstream, midstream, and downstream operations along with AI-enabled predictive analytics to achieve the following enhancements:

Intelligent Automation

Companies can audit tasks to identify those that are suitable for automation. For example, rule-based robotic process automation (RPA) to cognitive RPA could handle time consuming and error-prone activities, such as those carried out in the back-office or that support fieldwork like drilling and completion operations.

Predictive Maintenance

As oil and gas demands increase, so does the demand for the machines that run the oil and gas operations. AI could provide a viable solution to help oil and gas companies monitor their machine assets, predict when their machines and equipment require maintenance and make proactive maintenance decisions.

Well-Placement Analytics

Oil and gas producers are increasingly using AI for well-placement analytics and production optimisation. Pipeline companies use AI to analyse pipeline flows and determine the location and size of methane leaks, for example.

Digital Twins

Oil and gas companies use AI software to create “digital twins” of equipment. Digital twins can detect early signs of equipment failure so owner-operators can plan and implement corrective maintenance actions. The software can also use model drilling and extractions to determine whether virtual equipment designs are feasible and gather real-time data feeds from sensors in an operational asset.

Enhancing Health and Safety Standards

Using AI software such as natural-language processing for text analytics, vision analytics, and vital-signs analytics to monitor fatigue and heat exhaustion can result in efficient HSE strategies that can target human interactions.

Across the globe, and particularly in the GCC, national oil companies (NOCs) continue to invest in digitising their operations. Tools such as AI and machine learning are being successfully deployed as part of a comprehensive digital transformation strategy with promising results.

Through their dedicated 4IR Center, Saudi Aramco is deploying technology solutions to meet the world’s energy needs while minimising the environmental impact of their activities. The World Economic Forum has recognised the NOCs ‘Uthmaniyah Gas Plant as a “Lighthouse” manufacturing facility – a leader in 4IR technology applications. It is one of the world’s largest gas processing plants and uses advanced analytics and AI solutions to increase productivity while enhancing the safety, reliability and efficiency of its operating facilities. Meanwhile, in the Khurais oil field, Saudi Aramco reduced total energy consumption by 18%, reduced maintenance costs by 30%, and reduced inspection times by about 40% through AI applications.

Similarly, the Abu Dhabi National Oil Company (ADNOC) has also made strategic investments and launched several initiatives in technology and innovation as part of its digital transformation. These initiatives include the Panorama Digital Command Center, which leverages AI, big data, and smart analytics to aggregate data across ADNOC’s operations and provides operational insights and recommendations. Other initiatives include ADNOC’s Thamama Subsurface Collaboration Center, which uses AI to help identify hydrocarbon resources and unlock value from existing fields. ADNOC is also applying AI-enabled technologies for value chain optimisation, predictive maintenance, and blockchain-based hydrocarbon accounting.