Beyond the buzzwords: AI and data analytics in the pipeline industry
AI and data analytics seem to be on everyone’s mind. In nearly every industry and occupation, people are investigating ways they can make their lives easier by using these tools.
At Klarian, we are big believers in the transformative power of data. We weave AI and data analytics into the fabric of Digipipe, to help pipeline operators optimise their operations for greater efficiency. We’ve covered the role they can play in optimising operational efficiency in previous articles (check out the links below for more). In this article, we’re taking things back to the start and getting under the skin of these buzzwords.
It’s not just a simple matter of deploying AI and data analytics indiscriminately. How do AI and data analytics differ? What can each of the methods do for your operations? These are the questions we’re going to unravel.
Understanding the buzzwords: AI and data analytics
So, what’s the difference?
AI is the simulation of human intelligence in machines that can perform tasks that typically require human intelligence, such as understanding natural language, recognising patterns, and making decisions. Data analytics, on the other hand, is the process of examining vast amounts of data to uncover hidden patterns, correlations, and insights that can inform better decision-making.
In summary, while data analytics focuses on uncovering meaningful insights from data, AI encompasses the capability to learn, adapt, and make predictions, thus adding a layer of intelligence and automation to the analytical process.
So, how do you best deploy these two technologies to optimise your assets most effectively?
Asset optimisation with data analytics
Pipelines generate vast swathes of data including flow rates, pressure, and temperature to name a few. Traditional methods of manual data interpretation are simply not equipped to handle the sheer volume and complexity of data generated by modern pipeline operations.
Data analytics is at the core of Klarian’s asset optimisation offering. For example, by analysing SCADA data from your pumping operations, we can produce a wealth of decision-ready information. Our collaboration with you enables us to intelligently interpret our software generated recommendations and use all available information. For example, showing you which pumps you might want to think about refurbishing or which sections of pipe would benefit from drag-reducing-agent.
Elevating efficiency with AI
AI leverages advanced algorithms and machine learning to enable systems to learn from data, adapt to new information, and improve their performance over time. For pipeline operators, this means AI can handle complex tasks that were previously resource-intensive or prone to human error.
For example, AI-powered predictive maintenance algorithms can analyse historical data to forecast potential equipment failures. By identifying trends and patterns, these algorithms can alert operators to deviations from normal operations, allowing for proactive intervention before minor problems spiral. You can read more about the advantages of predictive maintenance in a previous blog article: preventive vs predictive maintenance.
Synergy: where AI and data analytics meet
When taking a data-driven approach to asset optimisation, pipeline operators have a choice: AI, data analytics, or a combination thereof.
Data analytics is reliable, especially for older assets that lack historical data for AI to train on, but AI does a better job at understanding the nuance of real-life situations. Both approaches have their pros and cons. At Klarian, we don’t like constraining ourselves to one thing or another. We will develop a data solution tailored to address your specific challenges, combining AI and data analytics in the way that best suits your operations to get you the best results.
Hit the button below to read more about how we incorporate AI and data analytics into Digipipe.