AI is enabling oil and gas projects with numerous uses. These include production optimisation with computer vision to analyse seismic and subsurface data faster, equipment downtime reduction for predictive maintenance, reservoir knowledge, and oil corrosion risk modelling to decrease maintenance costs. Here are a few more notable use cases and applications:

 

• Geological/Surface Analysis

• Downtime reduction

• Optimizing Production Scheduling

• Digital Twin Asset Maintenance

• Defect Detection

• AI-led Cybersecurity

• Worker Safety

• Analytics-Driven Decisions

• Tracking Emissions

• Logistics Optimizations

• AI-Driven Inventory

• Backoffice Optimization

• Optimized Procurement

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#1 Surface Analysis/Geological Assessment

O&G exploration leaders are literally mining AI. The deep-sea AI robot will help ExxonMobil discover natural seeps. ExxonMobil's AI-powered robots can identify oil seeps, reducing exploration risk and marine life harm.

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The Wadia Institute of Himalayan Geology (WIHG) discovered a new AI-based technique in September 2020 that analyses seismic waves (natural or induced by explosive material) to determine geological features beneath the surface, helping discover hydrocarbons like oil and natural gas faster and more efficiently. AI platforms are mapping underground oil deposits using subsurface geophysical data. This approach determines the reservoir's value and improves drilling.

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#2 Reduce Well/Equipment Downtime
Catastrophic asset failures cost offshore oil and gas installations millions of dollars every day in unplanned downtimes!
One big O&G firm used AI to predict well collapses, decrease maintenance, efficiently operate wells, and extend their lifespan. They did it how? The plant's engineers created a traffic light system to warn of well collapses. They operationalized the mechanism to reduce downtime. AI-enabled assistants helped the facility cut well revival time by 83% and alternative fuel expenses by $20,000 per well per day.

#3 Optimizing Production Scheduling
Offshore oil projects frequently have cost and schedule overruns, according to OnePetro. Due to weather, resource, and scheduling issues. The buildup phase of oilfield development involves many interdependent activities, including as drilling and platform installation, which complicates the situation. Thus, offshore oil project planning and scheduling models must account for these interacting components and hazards. For instance, an AI-based solution enables operators to preempt electrical submersible pumps (ESPs) breakdowns while maximising production, notes JPT report. Cloud-based solutions offer offshore operators powerful analytics tools with AI algorithms that detect anomalies in incoming data, indicating equipment failure.

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• Asset Tracking/Digital Twins

Digital twin (DT) technology can help with asset management, project planning, and lifecycle management. 63% of oil field assets are past halfway through their anticipated lifespan, making equipment status difficult to determine.  Digital Twins help oil and gas firms overcome production imbalances, quick economic changes like the COVID-19 epidemic, and equipment dependability difficulties. Oil and gas firms need digital twins for real-time visibility and flexibility in these busy and chaotic times.

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• Defect Detection

Oil and gas firms struggle to identify improper pipeline threading and fault-prone operations. Upstream issues that cause end-of-line defects cost the manufacturer and budget. AI may verify production quality and reveal analytics problems. AI-powered Defect Detection solutions are cheaper than traditional approaches. Deep learning pattern recognition can detect ill-dressed workers in camera footage. Moreover, predictive analytics alarm the operators on the equipment's health state, enabling pro-active actions to prevent a catastrophe with the repercussions to health, safety, and environment.

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• AI-based Cybersecurity

Siemens said that 70% of oil and gas companies suffered security breaches in the Ponemon Institute assessment. 42% of energy companies reported phishing assaults in PwC's Global State of Information Security Survey. The rise in the number of physical and cyberattacks and its security expenses have prompted the necessity for artificial intelligence tools to encrypt the operating system into their enterprise's security. Video cameras as sensors monitor utility security concerns 24/7. Software secures utilities on every endpoint.

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• Workplace Safety

Heavy equipment, non-covered rotary equipment, high pressure, high temperature, and aggressive chemicals make oilfield work dangerous. According to a Science Direct report, "Artificial intelligence in oil and gas industry upstream: Trends, challenges, and scenarios for the future," numerous deep learning-based IT systems enable safety inspectors recognise safety protocol violence. Heavy equipment, non-covered rotary equipment, high pressure, high temperature, and aggressive chemicals make oilfield work dangerous. According to a Science Direct report, "Artificial intelligence in oil and gas industry upstream: Trends, challenges, and scenarios for the future," numerous deep learning-based IT systems enable safety inspectors recognise safety protocol violence.

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#8 Analytics-Driven Decisions
The popular phrase "DATA is the new OIL" fits O&G perfectly. Manufacturing data floods oil and gas companies. They cannot use the huge data in data silos because they lack analytics tools. AI algorithms analyse real-time data streams from sensors and machinery of different plants or geoscience data to develop business-oriented ideas.

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• Tracking Emissions

Several oil and gas businesses have net-zero emissions ambitions, according to McKinsey. Many companies are decarbonizing their operations and value chains despite the economy. Occidental Petroleum, a US O&G business, and Carbon Engineering, a Canadian startup, will create a plant that gathers and buries 500,000 metric tonnes of CO2/year.

Oil producers utilise AI tools to track and regulate fugitive greenhouse gas emissions from pipelines and oilfield equipment. AI optimises CO2 storage for better oil recovery in upstream oil businesses.

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• Logistics Network Optimizations

The oil and gas supply chain is complicated, including decision nodes like crude acquisition, buy price, transportation to the refinery, refining activities, gantry operations, and retail sale of final products. AI coordinates the operations team and warehouse to ensure critical parts are available in upstream.
AI can aid midstream planning, execution, and route selection. Instead, it helps refiners plan optimal blending, forecast demand, estimate prices, and improve downstream customer connections. In summary, AI helps oil and gas companies predict the market price of crude oil and finished products, plan and schedule, optimise the crude basket, create a smart warehouse, maintain inventories, handle shipping operations, risk hedge, improve delivery times, and reduce costs.