Intro
Corrosion detection in the oil and gas industry can be a very challenging and risky activity, time-consuming and also expensive. The global costs of corrosion are estimated to be US$2.5 trillion, which is equivalent to 3.4% of the global GDP (2013). On top of that, it requires the workforce to inspect every edge of an enormous environment full of flammable hydrocarbons. However, the use of artificial intelligence is revolutionizing this procedure.
AI-powered corrosion detection uses drones and sensors to digitally map the asset. And by adopting cutting-edge technology, this new method is proving to be more efficient and reliable than the traditional one. In this blogpost, we’ll take a closer look at how artificial intelligence applied to corrosion treatment works and its urgence to the oil and gas industry.
How corrosion is traditionally managed
To understand how and why artificial intelligence is applied to this context, it is necessary to understand how its demand arose. Before AI, corrosion detection alone took months to be performed, allocating dozens of professionals in FPSOs to look around for anomalies in the whole facility. Not considering the other stages of corrosion treatment, such as painting, maintenance and, above all, decision making, which would still take several months to be complete.
In this sense, an outdated procedure is noted, which in turn must deal with a gigantic structure that is often located offshore. Not being enough, the usual approach exposes numerous people to fatal risks for months, there being affected areas by corrosion that require a rope climbing diagnosis. This results in an unsafe and ineffective operation.
In addition, corrosion inspections may also involve relating data from each of the asset’s components, which is not facilitated by the massive amount of paper documents that the structures have. In the same vein, the inspection results are all handmade, generating a delayed decision making that must be based on paper reports and prone to inaccuracy.
For this reason, corrosion inspections face improvement opportunities with this high-end technology, and AI fits perfectly to execute this task.
The role of AI in corrosion detection
The global costs of corrosion are estimated to be US$2.5 trillion, which is equivalent to 3.4% of the global GDP (2013). And AI is crucial to mitigate those effects. In this sense, the technology functions through algorithms that recognize corrosion signs along oil and gas facilities. To do so, it uses other tools such as 360º photos, sensors and 3D modeling to guarantee reliability in the detection process.
In knowledge of the process context, we can better understand how this technology impacts corrosion detection. Artificial Intelligence acts as a catalyst for identification by using pattern recognition and deep learning algorithms. This way, computers can teach themselves about the context of visual data. If enough data is fed through the system, the computer will “look” at all data and teach itself to tell one image from another. Finally, the images are broken down into pixels and are given tags or labels according to a specific demand.
This method of detecting corrosion allows a real-time monitoring of the asset’s health, which generates a traceable diagnosis. In comparison, the manual inspection reliability lies exclusively in the trust relation built with the operator. Therefore, it becomes clear that AI is playing a significant role in improving safety and effectiveness in the oil and gas industry.
How AI is enhancing corrosion treatment
By adopting AI for corrosion control, around 254% more anomalies are found in far less time than the usual approach. This indicates a much more complete detection than the usual, reducing the downtime of the asset and the number of POB required. As a result, the HSE aspects (Health, Safety and Environment) are significantly improved, showcasing the company’s commitment to the industry’s growth and sustainability.
On other hand, providing information for an algorithm to analyze requires the previous contextualization of millions of structured and unstructured data. If done perfectly, as in Vidya’s platform for example, all relevant information will be integrated in an unique channel, allowing an easier decision making process based on real-time data from any component of the asset.
In the face of mounting challenges in the oil and gas industry, corrosion treatment based on visual observations and paper reports are getting obsolete. Meanwhile, AI emerges as a critical tool to safeguard assets, increase efficiency, and truly commit to sustainable growth.
Conclusion
AI applied to corrosion inspection is no longer a speculative scenario, it’s a reality that’s been saving millions of dollars with a self-improvement tool. Moreover, AI-driven corrosion detection ensures a data-driven approach, leveraging millions of structured and unstructured data points to make informed decisions.
In other words, AI-integrated corrosion detection marks a revolutionary step that addresses the inherent risks, time constraints, and inefficiencies associated with traditional methods. By doing this, oil and gas companies can better protect their professionals and their assets while leveraging their full production capacity.
