Get More From Digital Oilfield Equipment With Analytics

Optimizing oil and gas equipment can be an impossible task when you cannot answer critical questions about health and performance: Is this well approaching the end of its life? Are we prepared to react to market changes? Why did this piece of equipment fail? When will we get it back up and running? How long until our next equipment failure?

The problem has become especially acute in recent years. The downturn in oil and gas prices has put pressure on producers to cut costs and get the absolute most from their assets. But it’s difficult to improve equipment availability and get ahead of failures when you don’t have real-time insights into performance, or when the data you do collect is confined to silos.

The industry is at a turning point. Hoping to capitalize on the promise of the digital oilfield, oil and gas producers are seeking to connect their equipment, devices and systems, and integrate their data. Most critically though, producers are looking for ways to transform all of that data into operational intelligence.

Rockwell Automation refers to a connected oilfield that operationalizes data and allows for better decision-making as the ConnectedProduction™ environment. In it, stakeholders are connected in real time to better understand equipment performance and downhole conditions, and use those insights to help minimize the unknowns that impact their operations. They can even begin to predict future performance and address issues before they result in unexpected downtime.

All of this business and operational intelligence is fueled by clean, accurate and comprehensive analytics.

Finding Answers in Data
Analytics involves monitoring large amounts of data, collecting and contextualizing that data into information, and then presenting that information to workers in the form of useful, actionable insights. Analytics could include well behavior and performance trends from artificial lift systems. They could also be real-time performance and alarm information from surface processing skids, remote wellhead-monitoring systems or multi-well pad insights into downhole and reservoir conditions.

Regardless of what the analytics are, they have one common purpose: to help stakeholders make better operational decisions that positively impact business results.

Oil and gas producers that want to create a roadmap for deploying analytics should first understand the four analytics categories. Each category is progressively more complex in its use of data but also progressively more valuable in how it can help producers optimize their operations. The four categories are as follows:

1. Descriptive analytics summarize events and describe what happened.
2. Diagnostic analytics associate events with root causes or reasons to explain why something happened.
3. Predictive analytics use modeling and machine learning to predict what will likely happen.
4. Prescriptive analytics use previous instances of similar events to suggest what should be done to correct an issue or optimize performance.

Descriptive and diagnostics analytics are the starting point for oil and gas producers. They inform workers at the most basic level of how things are performing and the conditions in which they’re operating. If a failure occurs, these analytics can help technicians quickly diagnose the problem, and help make sure they send the right person to the right place, with the right tools and spares to resolve the problem. This can dramatically speed up troubleshooting and resolution times during downtime events.

Predictive analytics are the target goal for many producers today. These analytics look for known conditions and relationships within the data that indicate a device or piece of equipment is approaching failure. Producers can use this to identify and resolve equipment failures before they happen, providing a tremendous opportunity to reduce costly downtime.

It’s important to note that predictive analytics won’t always be absolute. Sometimes, they are configured around the probability of various failures. For example, a production team could set a threshold for a failure probability of 80 percent. If the software detects a failure probability at this threshold, it will trigger an alarm condition and can notify users that a failure is likely impending and requires service.

Deployment Considerations
Oil and gas producers looking to deploy analytics should consider the analytics approach when determining the information solution that is right for them. In many cases, upgrades to existing infrastructure will be needed before the operator can fully utilize data in an analytics approach.

For instance, most producers use a mix of equipment, devices and data stores from different vendors. But some analytics software is not vendor-agnostic. This can create integration challenges, like connectivity problems or data limitations, when the software is used with third-party hardware. More importantly, it can impede a producer’s ability to access the valuable data needed to meet their goals.

An open-architecture analytics approach will support the producer’s current technology investments. An open-architecture can also allow simultaneous connection with multiple analytics software engines, including third-party analytics such as those from oil and gas industry consultants. This allows producers to create an end-to-end production advisory system that leverages both internal production intelligence and external domain expertise. For example, Schlumberger offers expert tools for sub-surface diagnostics or flow assurance simulation. An open-architecture allows for end-to-end collaboration between operations and engineering environments that results in faster problem identification and resolution.

Rockwell Automation has collaborated with Schlumberger to create this production advisory system. Combining ConnectedProduction technology with oil and gas software, services and domain expertise from Schlumberger, the digital solution helps optimize production by connecting upstream operators with critical, real-time analytics and domain insights to reduce deployment risks and costs.

Many producers will also benefit from scalable analytics solutions that process data as close as possible to where the data originates. Device-level analytics, for instance, can provide health and diagnostic information for critical devices, and even send alerts to stakeholders’ mobile devices if a device needs attention. System-level analytics can create smarter equipment that asks for help before a failure, or improve collaboration between internal and external stakeholders. And business-level analytics can be used to analyze production and operational performance.

One aspect of data that is often overlooked but critical to achieve the accuracy and usefulness of analytics results is data integrity. The information solution should be able to validate data coming from the field, as well as minimize data gaps that could impact the results of analytic software and hence the decisions made by operators.

Finally, oil and gas producers should consider how operators interact with the results of the analytics software and other systems as they take actions to improve performance of operations. Intuitive, easy-to-use dashboards can help workers not only identify and understand critical production information, but also respond to it as quickly as possible.

For example, upon receiving a notification that a device has failed in the field, an operator can issue a maintenance work order right from the integrated environment. When the maintenance technician is reviewing the issue in the field, they can access their parts inventory system within the integrated environment, and confirm that the required spare parts are in stock and request them through the software. Or, if the parts are out of stock, they can be ordered right from the software.

Analytics in the ConnectedProduction Environment
Analytics in a ConnectedProduction environment can help oil and gas producers identify performance trends, quickly identify and react to events affecting production and equipment uptime, and even proactively prevent lost production and downtime. When these analytics are combined with other ConnectedProduction capabilities, like remote monitoring, producers can monitor production across multiple fields from a single, central location. And when they’re combined with business systems, producers can quickly adjust production in response to market changes and business needs.

Given the opportunity that analytics present and the pressures facing oil and gas producers, the question isn’t: Can analytics help improve our operations? But rather: How long can we compete without them?