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Smarter, safer drilling: innovation in the oil and gas industry
personRich McEachran eventJul 10, 2019

Smarter, safer drilling: innovation in the oil and gas industry

Rigs and vessels can be harsh and hazardous environments, but technology is helping to improve safety and maintenance.

On April 20, 2010, the Deepwater Horizon drilling rig in the Gulf of Mexico exploded, killing 11 people and leading to what is considered the largest oil spill and cleanup operation in US waters. 

The cause of the drilling disaster was a broken subsea blowout preventer, a piece of machinery used to seal, control and monitor the uncontrolled release of oil and gas.

The White House oil spill commission, published in 2011, concluded that the event could have been prevented. Poor management and communication meant workers had failed to flag up unexpected results from a pressure test with an engineer.

As plumes of oil persisted to rise from the depths of the Gulf, Igor Mezic, a professor in mechanical engineering at the University of California, Santa Barbara, and colleagues at his software development company Aimdyn, set about predicting the movement of the oil. In a paper published in the journal Science, Mezic said their predictions were accurate to within two miles. 

Nine years on from that fateful day in 2010 and there are robust accountability standards and environmental regulations in place. What’s more, innovation has come on leaps and bounds and is being used to improve safety and reduce inefficiencies in the oil and gas industry. 

Thanks to the convergence of new technologies, the Internet of Things, big data and predictive analytics, it’s now possible for rig and vessel operators to prevent accidents from happening altogether, rather than minimising the impact and consequences after they have occurred. 

Here are three examples. 

Eyes in the sky

Drilling for oil is a dangerous business. Workers put in long shifts, often late at night, that can be both mentally and physically exhausting and typically involve a combination of heavy machinery and combustible materials. The gruelling nature of the work means tasks such as visually inspecting equipment and detecting gas leaks can be prone to error.

Source: Sky-Futures

Drones are increasingly being used for surveying and monitoring pipelines, inspecting well sites and for managing other key oil and gas assets. The aim isn’t to replace workers, but to assist them. For example, a big advantage drones offer is that they can access hard-to-reach parts of rigs, such as under the bridge, and can be useful for spotting corrosion on the outside of a structure, which otherwise might only be seen from a helicopter. 

Their high-definition cameras and optical zoom mean drones can also identify loose nuts and bolts that might be missed by the human eyes and which pose a danger to production and workers if they go unnoticed. 

Companies like Sky Futures can offer oil and gas clients an end-to-end drone service, providing them with tools that capture data and turn it into actionable insight using artificial intelligence. Engineers can use this information to see how quickly a part is degrading or when it’ll need to be repaired or replaced, thus reducing the likelihood of downtime and improving spare parts management. 

Working like a dog

At the end of last year, a four-legged robotic platform, named ANYmal, performed various inspection tasks on an unmanned offshore converter platform in the North Sea. 

Source: ANYbotics 

Not every oil and gas structure is manned all year round. Some are primarily operated remotely, without the need for personnel to be present – production at the first fully automated platform was recently started up in the North Sea by Norwegian energy giant, Equinor. Those structures that are unmanned need to be monitored rigorously to prevent any disruptions and downtime in operations and that there’s a constant electricity supply. 

In its week-long trial, the dog-like ANYmal checked the status of sensory equipment, detected any thermal hotspots and was also on the lookout for any oil or water leakages. The information was fed back in peak-time, using visual and thermal cameras, microphones and sensors, to an onshore control centre. 

A spokesperson for ANYbotics, the company behind the legged robot, said in a statement: "A crucial task for energy providers is the reliable and safe operation of their plants, especially when producing energy offshore. Autonomous mobile robots are able to offer comprehensive support."

Twins at work

According to a 2017 Baker Hughes report, The Impact of Digital on Unplanned Downtime, 42 per cent of offshore equipment is more than 15 years old. The report also found that the cost of unplanned downtime was 36 per cent lower for those that used predictive data-based monitoring when compared to those that relied on a more reactive approach. 

A leading offshore drilling contractor, Noble Corporation, has partnered with GE (General Electric) to turn its Globetrotter I vessel into the first digital drilling vessel. 

Source: Noble Corporation

Noble has leveraged GE’s digital twin technology so it can compare data collected through sensors and control systems to the historical data of the vessel’s various asset. Advanced analytics mean Noble can map the real-time status and health of the vessel and be made aware of any anomalies. 

Early warning signs mean that offshore personnel can address potential problems before they occur. 

GE and Noble believe that this approach will help to achieve a 20 per cent reduction in operational expenditure on equipment. And by using predictive analytics, engineers can ensure they’re focusing their efforts on the more critical maintenance events.


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Outro

Science and technology are the principal drivers of human progress. The creation of technology is hindered by many problems including cost, access to expertise, counter productive attitudes to risk, and lack of iterative multi-disciplinary collaboration. We believe that the failure of technology to properly empower organisations is due to a misunderstanding of the nature of the software creation process, and a mismatch between that process and the organisational structures that often surround it.