Recently, a major oil leak occurred at the coast of Southern California, Orange County (OC) coast, causing the spill of almost 3,000 barrels into the Pacific Ocean. The colossal spill is reported to cover 13 square miles, resulting in the closure of the major coastlines and posing considerable risk to wildlife and human health.
The devastating spill resulted in 126,000 gallons of crude oil being released into the environment. But this is not the worst; the world has witnessed the deadly incidences of the Valdez oil spill in Alaska (11 million gallons) and the 2010 Deepwater Horizon spill in the Gulf of Mexico (134 million gallons). However, with the advances in safety technology over the last decade and oil companies’ investments into reliability and digital transformation, one wouldn’t expect such an instance in 2021.
But perhaps this is exactly as expected – after all, when compared with other oil and gas operations, the pipeline industry has been slow to adapt to Industry 4.0 based digital technologies due to the conservative nature of the industry, and comfort with the status quo.
Time to augment Pigging with Explainable AI
The lack of technological solutions that offer meaningful understanding and user assistance is often cited as a major reason for the slow adaption of digital technologies by the pipeline industry.
In the case of the OC oil spill, the pipeline staff claimed to regularly perform preventive pipeline maintenance processes, also known as “pigging”, where a machine called a “pig” is passed through the pipeline to check its condition. Smart pigs with a few analytical features are also available. However, most smart pigging systems lack the advanced technological features that can mitigate the breakdown risks.
With the increasing prevalence of remote operations, there is increased scope of SaaS applications like Eugenie, that can assist in the areas of pipeline inspection and crack detection. Eugenie’s advanced products can detect operational issues like corrosion and other hazards through timely alarms to guide the operational teams for taking appropriate actions.
A seamless journey from data to decision-making
Eugenie’s products Ray-Finn and Papillon have been developed after extensive research conducted with thousands of maintenance and reliability professionals. The Explainable AI framework of our products is created to guide operational professionals.
Eugenie’s products can generate actionable insights even in the absence of asset failure data points through powerful point-anomaly detection and deep learning methods. With Eugenie, any asset health can be monitored without the need to manually analyze each sensor-level data. Instead, Eugenie’s products can diagnose machine health with asset-level monitoring without user intervention. The results are delivered in the form of insights that lead to speedy action.
Eugenie’s solutions contain a centralized learning platform that is intuitive and easy to refer, when in need. This not only helps users to find similar anomalies that occurred in the past but also learn about the resolutions which their teammates had implemented in the past. The patented AI-based products of Eugenie comprising of the auto-ML platform, combined with its rich database of machines and a human-centered framework make it a preferred predictive maintenance vendor for many industrial companies. Despite the limitations and discrepancies in data, Eugenie predicts asset and equipment failures with more than 90% accuracy.
Real-time machine diagnostics with an altered approach
Eugenie’s products combine sensor-generated data with machine learning models to show new ways of monitoring the production process and meet the KPI’s. Based on the continuous monitoring of assets and processes, operational behaviours indicate distinct signs in case of deviations.
Eugenie’s plug-and-play solution is easy to use and starts adding value immediately after deployment. It analyses high-volume, time-series industrial data – but it is designed to be used by non-technical operational staff. Captured data through different sources such as SCADA are automatically visualized in the form of user-friendly, graphical representations. Users get detailed root cause recommendations and can monitor performance in real-time to make better faster and better decisions.
Turning the voluminous industrial data into actionable information is a daunting task – but Eugenie makes it easy for the industrial companies with diagnostic insights. These insights are delivered directly to those who can interpret the information, thereby improving day-to-day decision-making. Explainable AI is the key factor because Eugenie helps in identifying anomalies, analysing performance in real-time to optimize operations. Thus reducing machine failures and increasing machine availability.
A real risk to human health and wildlife
Most pipeline failures lead to huge economic as well as ecological losses, resulting in adverse effects on the environment, humans, and wildlife. Therefore, reactive or preventive maintenance can be the worst choice if maintenance can be applied in the case of pipelines.
The pipeline maintenance processes need an immediate revival of the current technological advancement to achieve operational efficiency. Better asset reliability is the vital step towards sustainability, thus avoiding risks to the environment.
Eugenie’s Explainable AI, with root-cause analysis and diagnostic insights, enables effective operational decision-making with improved reliability of assets like oil pipelines.
This decision-intelligence framework also paves the way for companies to introduce increased sustainability in their operations while reducing operational costs.
Today, sustainability is an obligatory requirement from most stakeholders. A safer and greener future is a priority for most oil and gas companies. With the current technological advancements and availability of robust solutions like Eugenie, it is possible to make a viable transition to sustainability.
To learn more about how Eugenie has helped industrial companies ensure a profitable transition to operational sustainability while improving asset reliability, talk to us today or feel free to reach out to us at firstname.lastname@example.org.