Oil and gas maintenance insights

• Improve throughput of the gas pipeline network by maintaining compressor uptime using data analytics for precise maintenance.
• Shift from traditional calendar-based maintenance to condition-based maintenance or predictive maintenance models.
• Increase operational awareness using real-time data for timely decision-making and optimize maintenance efforts to minimize costs.

A midstream oil and gas company operating a network of gas pipelines across the US initially had a somewhat subjective maintenance approach. Monthly equipment failures were normal, and the pipeline throughput was scarce.

To standardize their processes and improve safety, reliability, and throughput, the company wanted to transition into a more systematic approach. Moreover, they wanted to get more value from their AVEVA PI System. To achieve this, the company needed a robust data infrastructure to leverage data analytics and the interaction of these systems with their physical assets.

Partnering with CSE ICON, the company aimed to implement a system capable of detecting anomalies and issuing early warnings to ensure equipment uptime and prevent costly shutdowns.

Challenge: Over 5,000 unplanned shutdowns per year

Initially, the midstream company tracked occurrences (shutdowns) to identify any issues. The types of issues varied, with occurrences happening every other day, or even seven occurrences during the same day (see figure 1).

Figure 1: Identification of issues on a daily, weekly and monthly basis. Courtesy: CSE ICON, Inc.

The company faced more than 5,000 shutdowns per year. These shutdowns included compressors or compressor engine failures at compression stations, which meant they couldn’t compress enough or any gas.

Consequently, they experienced a loss of time, money, and customer trust. Moreover, despite owning an expensive AVEVA PI System, they didn’t use it to its potential because of non-standard and subjective processes.
Solution: Integrating their system with Maximo for automated maintenance
One of the main goals of this project was to shift from calendar-based and reactive maintenance to a more condition-based (proactive) approach. As condition-based maintenance focuses on monitoring the actual condition of equipment to determine if it needs maintenance or not, the first step was to collect data relevant to equipment operating health.

Besides pressures, temperatures, and voltages, other relevant data was collected from the following devices:

• Ultrasonic meters: Flow rate in pipelines.

• Odorizers: For safety reasons, the odorizers inject a distinctive smell into the gas to detect any leaks.

• Chromatographs: To determine the composition of the gas (hydrocarbons and other compounds).

• Moisture analyzers: Moisture or humidity levels in the gasses.

This data was then put into the system for posterior analysis (see figures 2 and 3).

Figure 2: Ultra sonic meters (left) and odorizers (right) readings. Courtesy: CSE ICON, Inc.

Figure 3: Chromatographs (left) and moisture analyzers (right) readings. Courtesy: CSE ICON, Inc.

For the implementation of condition-based maintenance, the company had to monitor equipment conditions, detect early signs of degradation (or failure), and promote proactive maintenance activities. This involved using the data already collected in step one along with:

• Defining events and alarms: This involved identifying specific changes or conditions that could shut down engines and compressors such as inlet hydrate formation, engine detonation, compressor overload, compressor valve failure, and faulty instrumentation. Alarms and notifications were set to alert operators of these conditions in real-time.

• Developing real-time dashboards: Visualizations of key assets highlighting metrics such as actual vs. theoretical/predicted performance would give operators immediate insight into the condition of the equipment.

• Integration of the PI system and Maximo enterprise asset management (EAM): The integration of these two technologies enabled the automated generation of work orders for maintenance activities (based on the condition of the equipment).

Figure 4 illustrates the maintenance workflow of integrating the PI System and Maximo to leverage data analytics.

Figure 4: Overview of the integration process between the PI System and Maximo. Courtesy: CSE ICON, Inc.

The results included reliable shutdown data saves, which reduced 100 hours of admin work and maintenance costs being cut by 40%.

The system acted as a performance management tool. Operators, compressor technologists, and automation engineers got notifications or alarms to prevent a major catastrophe. The dashboards with visuals made it easy to identify issues. It was clear when a metric was going out of spec and needed monitoring.

Figure 5: Dollars saved annually (actual vs. assumptions). Courtesy: CSE ICON, Inc.

Situational awareness also was improved thanks to standardized processes. Operators could now make informed decisions based on the current situation. The customer’s investment began generating an ROI as it was being used to prevent and improve performance which reduced costs associated with:

• Shutdown of equipment due to maintenance.

• Purchase of replacement parts that didn’t need to be replaced based on their health indicators.

• Manual work order generation for maintenance activities which could present delays.

• Non-optimal settings used on industrial equipment such as running an engine using inefficient configuration.

Overall results:

• Saved over 40% in costs from event prevention and maintenance (See Figure 5).

• The elimination of ~100 hours of administrative time while dealing with unplanned shutdowns.

• A standardized process to identify anomalies and react to potential issues.

• Reliable data regarding the shutdown of the equipment.

• Better understanding of the root cause of shutdowns.

CSE Icon is a certified member of the Control System Integrators Association (CSIA).

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