How AI is transforming Oil and Gas Industry

As one of the most valued commodities in the energy sector, the oil and gas industry is no stranger to innovation. At first glance, a deep-sea oil rig may not look like a digitally enriched environment, but the oil and gas industry is making massive strides in integrating AI solutions to solve complex problems. AI can help improve productivity and efficiency, reduce costs, explore new resources, predict maintenance hazards, provide better safety measures, and add value to all supply chain components in the oil and gas industry.

AI is already here

The question of whether AI is needed in the traditional oil and gas industry is moot. Every major company in the field is warming up to the potential impact of AI. A recent survey showed that more than 92% of the current oil and gas companies have already invested or plan to invest in AI. More than 50% of industry executives attest to the fact that they have already started using AI to solve their nagging concerns.

Broadly, AI can refer to a framework involving machine learning, natural language processing, neural networks, autonomics, and various other related systems. The use of machine learning is transforming oil and gas discovery because of the ability to gather and process large amounts of real-time data. AI can substantially reduce unplanned downtime and optimize production to increase revenue. From geological data analyzing upstream to customer engagement downstream, AI is starting to make its' presence felt in the oil and gas industry.

Data Analytics and Exploration

AI-powered data analytics can help companies discover new exploration opportunities. In the upstream sector, AI-augmented solutions can help search for underground or underwater gas fields and crude oil. Recently ExxonMobil announced a partnership with MIT for the design of robots to help improve natural oil detection. These AI-powered robots will help detect natural oil seeps efficiently, without risk, and with minimal environmental impact.

Insights provided by AI algorithms enable better operational visibility and help make better strategic decisions. BP recently invested $5 million in a partnership with Belmont Technology for using their cloud-based ML platform named "Sandy" to mine geological data and compare it with historical data to identify new workflows.

Total S.A., a pioneer in the oil and gas industry from France, recently partnered with Google Cloud to develop AI solutions for subsurface data analysis to explore new avenues. This partnership will help their AI solutions to analyze technical documentation using NLP and examine subsurface images using computer vision technology to make their exploration of oil and gas fields much more efficient.

Ambyint is a market leader in AI-augmented solutions for the oil and gas industry. They provide artificial lift optimization solutions to the Exploration and Production industry. The Ambyint platform uses high-resolution adaptive controllers to lower operating costs up to 30% and increase production up to 7%.

Predictive Maintenance

Offshore oil and gas companies are functioning in unpredictable environments. From corrosions to improper threading in pipelines, complex infrastructure also makes it a maintenance nightmare.

One estimate indicates that an offshore rig experiences about 27 days of unplanned downtime every year. Such maintenance issues can lead to more than $30 million loss per year. This situation provides tremendous potential for production optimization using predictive analytics by AI-augmented systems. AI can anticipate most equipment failures and help save a lot of unplanned downtime and maximize productivity.

All the major industry leaders like Royal Dutch Shell, ExxonMobil, BP, Chevron, Rosneft, and Equinor have started using predictive maintenance systems to boost growth and sustainability. is a leading enterprise AI software provider. They offer a predictive maintenance application with can integrate with client's enterprise databases and mine historical data for patterns, failure data, and so forth. Clients can also upload sensor data, legacy data, and even technician and weather data. The software then predicts a probability of failure in the coming days.

While predictive maintenance can help reduce operating costs in upstream channels, predictive analytics can forecast demand in downstream channels.

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