Artificial intelligence (AI) and machine learning (ML) are becoming more widespread, and it’s no surprise to see the adoption of advanced analytics, predictive maintenance, and predictive monitoring solutions in the oil and gas industry, as well as in other manufacturing verticals. 

According to a recent study, the global market for oil and gas analytics is predicted to exceed $52.46 billion by 2032, growing at a CAGR of 22.45% from its estimated value of $6.92 billion in 2023. In this article, we’ll explore the advantages that predictive analytics can bring to oil and gas companies, and what challenges oil and gas leaders should prepare to overcome. 

What is oil and gas analytics?

Oil and gas analytics refers to using advanced data analytics to extract valuable insights and make informed decisions within the oil and gas industry. It involves applying artificial intelligence (AI) and machine learning (ML) algorithms to crunch enormous big data sets that are generated at various stages of the oil and gas value chain. 

Oil and gas analytics is part of the broader trend of Industry 4.0 and digital transformation within process manufacturing industries. It’s been enabled by the rise of Industry Internet of Things (IIoT) devices that can collect so much data; cloud computing networks that can support massive datasets and the power-hungry ML systems needed to analyze them; and 5G and edge networks that can share data in real- or near-real time. 

Predictive analytics is the most common type of oil and gas analytics. It’s relevant for many use cases, including predictive maintenance, production optimization, supply chain management, worker safety, sustainability, resource allocation, and more. Oil and gas companies are also beginning to adopt autonomous analytics solutions that can make data-driven decisions and carry out actions without human input.

What are the benefits of advanced analytics for oil and gas manufacturing?

  1. Lower costs

Predictive maintenance and predictive monitoring solutions can analyze data from sensors and IIoT devices in refineries to detect anomalies in processes and/or equipment function. These early alerts to potential part failure, fouling, leaks, fatigue, and more enable process engineers to act quickly, while incidents are still minor. 

In this way, engineers can keep maintenance costs down, and proactively repair parts before they fail entirely and require costly replacement, thereby extending equipment lifecycle and helping maximize profits. 

  1. Increased productivity and efficiency 

Timely alerts allow maintenance to be scheduled for optimal times, as well as reducing the incidence of sudden part failure, helping keep refinery downtime to a minimum. By enabling engineers to resolve issues before they significantly slow down production, predictive analytics can also help energy companies to maintain productivity and product quality. 

The insights from advanced analytics can also guide root cause analysis, helping resolve issues much more quickly to maintain production quality and volume. Advanced analytics can go further, improving overall operating efficiency (OOE) in refineries by preventing bottlenecks and fouling from affecting productivity, ensuring that all processes are operating at optimal capacity, and allocating resources more effectively. 

  1. Improved sustainability and safety 

Worker safety, environmental impact, and sustainability are key concerns for energy companies today. Predictive analytics can detect leaks, emissions, flare events, and other hazards in refineries before they occur or worsen. Advanced analytics can monitor environmental impacts to ensure compliance with regulations, while a more efficient production system results in lower emissions and greater worker safety. 

Additionally, oil and gas analytics can detect safety risks like equipment failures, helping prevent accidents. With remote monitoring and analysis, refinery employees don’t need to put themselves into hazardous situations in order to check up on possible issues. More advanced autonomous analytics that can self-repair safeguard worker health even more, by removing the need for manual repairs and incident resolution. 

  1. Better decision-making

Oil and gas analytics can be applied to markets, customer behavior, and wider global patterns that indicate trends in future price fluctuations and demand. With better information about oil and gas markets, executives can make more informed decisions about trading, logistics, resource allocation, and capital investment. 

For example, insights into long-term oil and gas sales can help oil and gas decision-makers calculate the best places to locate a refinery and how large it should be. With information about the main customers over the next several years, it’s possible to identify which locations will be the most convenient for keeping fuel transportation costs and hassle to a minimum. 

  1. Reduced waste

By forecasting potential equipment failures, process inefficiencies, and maintenance needs, predictive analytics can enable refineries to optimize production processes. When oil and gas refineries are operating as efficiently as possible, they can make the best possible use of water, energy, crude oil, and other resources. 

The ability to anticipate and address operational challenges before they escalate also leads to decreased waste through improved maintenance practices, streamlined processes, and better resource management. This not only saves money, but also aligns with the industry’s increasing focus on environmental responsibility and sustainable practices.

What challenges do oil and gas companies face when implementing advanced analytics?

  1. Data accessibility 

The vast amount of data that oil and gas refineries produce is what powers advanced data analytics, but it can also create a challenge. With so much data, it can be difficult to locate and access the datasets necessary. Data flows in from varied sources which may not be easy to integrate, making it tough to ensure consistent data quality. 

The problem is compounded by the fact that different data sources can store data in incompatible formats and standards, creating data silos that result in blind spots and prevent analytics solutions from gaining a holistic understanding of operations. 

  1. Lack of contextual data

Although refineries generate enormous amounts of data, that data might not be as valuable as it appears at first. Most predictive analytics solutions use one of two methods to train ML algorithms: supervised learning, which relies on historical examples or recurring failures; or unsupervised learning, which looks for anomalies within normal process activities. 

However, supervised learning isn’t possible in oil and gas plants, because there’s a lack of historical examples of recurring failures. It’s rare for any problem to recur in exactly the same way. Unsupervised learning is also impractical, because there are too many moving parts in a refinery. In effect, there is no “normal” state, so analytics solutions cannot tell when something is not “normal.” 

Oil and gas companies also tend to be short on historical and contextual data, without which it’s extremely difficult for analytics solutions to understand relationships and connections between data sets and derive meaningful insights. 

  1. Legacy infrastructure 

When it comes to digital transformation, the oil and gas industry has some way to go. Many refineries are still operating with legacy systems and infrastructure that were built over decades. They weren’t designed to support Industry 4.0 technologies, artificial intelligence, or advanced analytics, which often gives rise to compatibility and integration issues. 

Sometimes these gaps can be bridged with system updates and software patches, but not always, while replacing major chunks of production software and operating systems technology is rarely cost effective and often simply impossible. 

It doesn’t help that oil and gas operations can span multiple countries and regions, with each refinery using different systems and tech stacks. Implementing analytics solutions at scale, while ensuring consistent performance and accuracy across various locations, can be a challenge.

  1. Culture shift 

Advanced oil and gas analytics requires not just a change in technologies, but also a change in culture. To get the most out of solutions like predictive maintenance and predictive monitoring, you need stakeholders who understand the value of preventing failures and adopting data-driven approaches. 

Decision makers need to learn to trust the insights delivered by ML analytics, which can often appear to be something of a black box. This could require extensive change management strategies for all levels of the company, so that analytics results are accepted by all employees. 

  1. Skills gap

Advanced analytics requires specialized skills, such as data science, machine learning, and domain expertise, but these are in high demand and short supply. Oil and gas companies suffer from a negative reputation among younger job-seekers, who tend to perceive them as environmentally harmful, unsafe, and unexciting. This makes it even harder for energy companies to recruit the necessary talent.

At the same time, long-term oil and gas employees are reaching retirement age and taking domain knowledge with them, leaving refineries short on the subject matter experts needed to help train and implement new solutions. 

Precognize can help

For example, SAM GUARD®’s predictive monitoring can pick up on the first signs of potential part failure or process inefficiency in refining operations, so that engineers can resolve issues quickly to lower costs, reduce downtime, and boost productivity. SAM GUARD®’s insights help speed up root cause analysis, cut the time on issue resolution and maintenance, cut costs and waste

Our predictive analytics solution can also increase operating efficiencies, slash waste, improve resource allocation, minimize environmental impact, and raise health and safety standards for workers and for communities living in close proximity to refineries. 

SAM GUARD® takes a unique approach of combining human intelligence together with machine learning (HI + ML). It uses human domain knowledge to divide the refinery into smaller, more relevant groups of sensors, guiding ML analytics to understand “normal” patterns of activity and apply unsupervised learning techniques without getting overwhelmed. 

Furthermore, SAM GUARD®’s Analytical Monitoring Service (AMS) harnesses HI to review alerts and filter those that are the most urgent through to process engineers. This assists oil and gas stakeholders to quickly see the value that the solution provides, helping them through the culture shift of advanced oil and gas analytics. 

Oil and gas companies can benefit greatly from advanced analytics

Overall, oil and gas analytics empowers companies to make data-driven decisions that enhance operational efficiency, reduce costs, improve safety, and increase profitability in an industry known for its complexity and challenges. Although there are challenges along the path, the right predictive analytics solution can help you overcome them and start seeing ROI in a shorter time frame.

Learn more about how SAM GUARD® can help oil and gas companies