AI and Big Data Drive Oil and Gas Industry Recovery

The oil and gas sector is facing issues such as lack of accessible hydrocarbon deposits, expensive exploration and risky production. Sustainability considerations are also driving demand away from dirty fossil fuel sources and toward cleaner ones.

AI is fueling oil & gas industry optimization

AI can help transform the oil and gas industry by improving quality assurance and in assisting workers, reducing pipeline corrosion, enabling smart procurement, shipping transparency, optimal replenishment digital category management, and discovering new perspectives.

The market for artificial intelligence (AI) in the oil and gas industry was valued at $2,295 million in 2022, and it is projected to increase to $4,444 million by 2028, rising at a compound annual growth rate of 11.64%.

For example, deep water oil and gas output is expected to climb 60% between 2022 and 2030, necessitating a boost in exploratory tasks by autonomous AI-powered robots, according to a Wood Mackenzie report.

Currently, Saudi Aramco, Royal Dutch Shell and ExxonMobil are among the AI leaders in the oil and gas industry.

According to Aramco, the use of AI in its Khurais oil field in Saudi Arabia has helped maximize operational efficiency by cutting down inspection time by 40%, power consumption by 18% and maintenance cost by 30%.

Shell, on the other side, has implemented more than 160 AI initiatives across its entire oil and gas supply chain over the last 10 years.

ExxonMobil too is working with technology companies to use AI to understand and combine data from disparate systems into a single repository. It is also reportedly working towards forecasting equipment breakdowns in the Permian Basin to optimize production.

Growing Influence of Big Data in the Oil and Gas Industry

Big data and data analytics are being used in the oil and gas sector to process and analyze large datasets. The data comes in two categories — structured data such as risk and project management documents, seismic surveys, surface and underground infrastructure, production data, market pricing and weather data, and unstructured data such as daily logs, drilling reports and CAD drawings.

Data analytics has a variety of uses in the oil and gas sector, such as predicting oil and gas demand based on variables like economic growth, geopolitical events, and weather patterns.

Optimization algorithms can be used to choose drilling locations that are most economical, analyze data from sensors of drilling equipment to detect anomalies and diagnose equipment failures, and automat grid data and put in place analysis frameworks.

However, access to massive and high-quality data is essential for successful data science applications, but it is not guaranteed because of poor quality and accuracy of field data and a shortage of labeled data. AI can help with small datasets, but there are few techniques to fix faulty data.

Conclusion

In the recent times, disruptive changes in the oil and gas industry have impacted predictability, productivity and profitability. As the companies navigate the market volatility, AI and data science transformation are likely to influence the direction of the industry.

Author: Yugandhara Kadam