Optimizing oil and gas equipment can be an impossible task when critical questions about health and performance cannot be answered: 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 it be back up and running? How long until the 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 is difficult to improve equipment availability and get ahead of failures when you don’t have real-time insights into performance or when the data that are collected are confined to silos.
The industry is at a turning point. Hoping to capitalize on the promise of the digital oil field, 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 those data into operational intelligence.
Rockwell Automation refers to a connected oil field that operationalizes data and allows better decision-making as the ConnectedProduction environment. In it, stakeholders are connected in real time to better understand equipment performance and downhole conditions, and they 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 those 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. It also could be real-time performance and alarm information from surface processing skids, remote wellhead-monitoring systems or multiwell pad insights into downhole and reservoir conditions.
Regardless of the analytics, the one common purpose is to help stakeholders make better operational decisions that positively impact business results.
Oil and gas producers that want to create a road map 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
- Descriptive analytics, which summarizes events and describes what happened;
- Diagnostic analytics, which associates events with root causes or reasons to explain why something happened;
- Predictive analytics, which uses modeling and machine learning to predict what will likely happen; and
- Prescriptive analytics, which uses 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 are 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 is the target goal for many producers today. The analytics looks 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 is important to note that predictive analytics will not always be absolute. Sometimes it is configured around the probability of various failures. For example, a production team could set a threshold for a failure probability of 80%. 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.
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 also can 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 fl ow assurance simulation. An open-architecture allows end-toend 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 also will benefit from scalable analytics solutions that process data as close as possible to where the data originate. For instance, device-level analytics can provide health and diagnostic information for critical devices as well as 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 often is 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 analytics 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 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 the analytics is combined with other system capabilities, like remote monitoring, producers can monitor production across multiple fields from a single, central location. And when they are combined with business systems, producers can quickly adjust production in response to market changes and business needs.
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