Excellent Ai Solutions In The Oil And Gas Industry
Artificial intelligence is proven to be a significant enabler for oil & gas projects with various applications. These include production optimization with computer vision to evaluate seismic and subsurface data more quickly, minimising equipment downtime for predictive maintenance, comprehending reservoirs, and modelling for forecasting oil corrosion hazards to decrease maintenance costs.
Here are a few other significant use cases and application areas:
1. Surface Analysis/Geological Evaluation
AI is truly proving to be a treasure for O&G exploration leaders. For instance, ExxonMobil intends to deploy the deep-sea AI robot to improve its ability to locate natural seeps. ExxonMobil's AI-enabled robots can detect these oil leaks, which will eventually minimise exploration risk and marine life damage. The Wadia Institute of Himalayan Geology (WIHG) found a novel AI-based technology in September 2020 that aids in interpreting data from seismic waves (natural or created by explosive material) to assess the geological characteristics under the surface, hence facilitating the discovery of hydrocarbons such as oil and natural gas in less time and with more efficiency. Briefly, AI systems are being used to analyse subsurface geophysical data and map subterranean oil resources precisely. This approach finally provides the precise value of the reservoir and increases the efficiency of drilling operations.
2. Reduce Well and Equipment Downtime
Unplanned downtimes cost offshore oil and gas installations millions of dollars each day in the case of catastrophic asset breakdowns!
According to a whitepaper by the World Economic Forum titled 'Digital Transformation Initiative' by Oil and Gas Industry, 92% of refinery shutdowns were due to unscheduled maintenance, costing oil and gas businesses between $42 million and $88 million annually.
This is where Artificial Intelligence, Data Science, and IoT-based predictive analysis aid in generating significant cost reductions.
Using AI, one of the largest O&G corporations improved its capacity to detect well collapses before they occur, decrease maintenance, operate the wells effectively, and extend their remaining useful life. What was their strategy? The engineers of the factory created a traffic signal system that alerted them to the imminent risk of a well collapse. This allowed them to operationalize and implement the strategy for reducing downtime. The deployment of AI-powered assistants enabled the facility to lower the time required to revive a well by up to 83% and the cost of alternative fuel per well per day by $20,000 per well.