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What Your Compressor Fleet Data May Be Missing | Detechtion

dfranco@detechtion.com (Darnell Franco) — Fri, 08 May 2026 21:18:00 GMT

Compression teams do not always have a data problem because information is missing. Sometimes, the data is already there, but it is spread across SCADA systems, vendor portals, telemetry platforms, downtime reports, and spreadsheets.

In our OneView Compression™ webinar, we looked at what can happen when compressor fleet data is available but still difficult to use. Below, we break down the common assumptions discussed in the webinar that can keep operators from seeing the full picture across their gas compressor fleet.

Key Takeaways:

Compressor data can exist across multiple systems and still leave visibility gaps.
Mechanical availability alone may not show the full picture of compressor performance.
Standardized fleet data helps compression teams move faster from awareness to action.

Your Data May Be Missing the Full Fleet View

A fleet of gas compressors can generate a lot of data and still leave teams without a clear view of what is happening across their operations.

That is especially true when runtime, downtime, alarms, vendor updates, and SCADA data live in different systems. Operators may have the information they need, but they still have to move between screens to understand which units need attention and why.

During the webinar, we compared this to a control tower trying to make decisions with too many separate screens. The challenge is not just visibility. It is how quickly teams can move from something changed to someone taking action.

That is where time to awareness, time to dispatch, and time to truth become important. When issues are harder to spot, they tend to last longer than they should.

Your Data May Be Missing a Common Language

Having compressor data is not the same as having data that can be compared across the fleet.

Different vendors, SCADA systems, and telemetry platforms may define events, alarms, measurements, and downtime in different ways. One system may not speak the same language as the next, which makes it harder to understand performance consistently from unit to unit.

In the webinar, we discussed the importance of standardizing data across identity, units and scaling, semantics, and time and events. Once that information is normalized, teams can compare apples to apples across runtime, measurement, downtime, utilization, horsepower utilization, and mechanical availability.

That common language gives operators a clearer starting point. Instead of reconciling data first, teams can spend more time understanding what the data is telling them.

Your Data May Be Missing the Gap Between Availability and Runtime

Mechanical availability is important because it tells teams whether a compressor is available to run, but it does not always tell the full performance story.

A compressor fleet can show strong mechanical availability and still lose producing time. In some cases, compressor runtime may be lower than expected because of non-mechanical downtime events, even when the unit itself is available.

That gap was one of the key myths we discussed during the webinar. A compressor may look healthy on paper, but the runtime data may tell a different story.

Examples like high jacket water temperature shutdowns or high discharge temperature shutdowns can affect production without showing up as a mechanical availability problem. When teams can see availability and runtime side by side, they have a better chance of finding the issues quietly eating into performance.

Your Data May Be Missing the Time Lost Before Action

The time lost in data workflows often starts before analysis even begins.

Many compression teams still spend hours logging into vendor portals, exporting CSVs, cleaning spreadsheets, organizing columns, and building a snapshot of fleet performance. By the time the data is ready to review, the issue may have already been sitting in the field longer than it should.

In the webinar, we talked about customers pulling downtime information from 10 to 15 different data sources just to understand what was happening across their compressor fleet.

That work still has to happen, but it does not need to slow the team down every time. When compressor data is brought into one standardized view, teams can start closer to insight and action instead of starting with manual data preparation.

Your Data May Be Missing the Utilization Picture

Utilization visibility can also go under the radar, especially across larger fleets.

As noted during the webinar Q&A, utilization is usually the visibility gap we recommend to fix first. Once operators understand how their compressor units are actually being used, they can make better decisions about whether they need more units, spare units, larger units, smaller units, or a different facility design.

That shifts the conversation from what happened to how the fleet should be operated. It also helps connect compressor data to production, revenue, and cost decisions.

For a closer look at these compressor data gaps, watch the full OneView Compression™ webinar below.

Hidden Compressor Issues: 3 Lessons From the Field

zbennett@Detechtion.com (Zachary Bennett) — Fri, 27 Jun 2025 17:11:16 GMT

When it comes to compression, the most expensive problems are often the ones you never see coming.

In the final episode of our Know Pressure: Your Guide to Compressor Fleet Management webinar series, the Detechtion team breaks down three real-world examples of hidden compressor issues and the practical lessons they reveal for managing production, reliability, and operating cost.

Key Takeaways:

A gas compressor can be running normally while still limiting production, increasing risk, or wasting power.
Loading curves, rod load, recycling, and other operating data can reveal hidden compressor issues before they become more expensive.
Effective compressor fleet management turns data into action by helping compression teams evaluate safe options, make informed changes, and confirm the result.

Lesson 1: Know Where the Compressor Stands on Its Loading Curve

The first lesson from the webinar dealt with understanding where a compressor is operating relative to its loading curve.

In the first case study, the Detechtion team reviewed a pair of compressors at an acquired facility. The units were running in what could best be described as a “set it and forget it” configuration. They were stable, but no one had recently evaluated whether the settings still matched current field conditions.

After the compressor units were added to Detechtion’s analytics platform, our team identified that they were operating off curve. Horsepower utilization was around 50%, and cylinder capacity was roughly 70%. In other words, the units were running, but they were not fully using the capacity already available.

From there, our team modeled potential configuration changes. The goal was to understand whether the units could move more gas at the same suction pressure, move the same amount of gas at a lower suction pressure, or achieve some combination of both.

After the approved configuration changes were made, the units reached nearly 100% cylinder capacity. Suction pressure dropped into the expected range, and production increased. Based on the webinar example, the added production was valued at just under $250,000 per year.

In this case, the lightly loaded compressor was not automatically a problem to fix. It was a signal to evaluate the unit’s position on its loading curve and determine what action, if any, made operational sense.

Lesson 2: Evaluate Reliability Risk Before Pushing for More Production

The second lesson focused on the relationship between production gains and reliability risk.

In this case study, an operator adjusted pocket settings after a third-party outage to move more gas that was behind pipe. The change worked from a production standpoint, but it also increased rod load to about 98%, putting the unit in a higher-risk operating condition.

Because the operator entered a manual data point into the system, Detechtion received an alert and was able to review the situation quickly. Instead of continuing to run in that higher-risk condition, our team worked with the operator to adjust the configuration and raise the suction pressure set point. That helped move more gas while keeping rod load below the previous level.

This example shows why compressor analytics can be valuable during day-to-day operating decisions. The original change was not made carelessly. The operator had a clear production goal. But without visibility into the mechanical impact, a production-focused adjustment could have created unnecessary reliability risk.

For operators, that is where compressor data becomes especially useful. It can help teams understand not only whether a change improves production, but whether it does so safely.

Lesson 3: Reduce Compressor Appetite When Recycling Becomes Routine

The third lesson centered on what happens when compressor capacity no longer matches actual process demand.

In the third case study, the Detechtion team reviewed an oversized compressor that had been recycling for years. In the webinar, recycling was described as a condition where the compressor is compressing more gas than it is selling. Some recycling may happen occasionally as part of normal operation, but continuous recycling can be a sign that the unit is doing more work than the process requires.

In this case, the compressor had too many stages and too much cylinder capacity for the amount of gas being fed to it. The result was unnecessary power consumption.

After evaluating the available options, the operator chose to install crank-end spacers on the first-stage cylinders. Once the change was made, the compressor unit used about 1,100 fewer kilowatt-hours per day, resulting in an estimated $40,000 in annual electricity savings.

Unlike the first example, this case involved a small hardware investment. But because the savings were measurable, the change had a clear business case.

When recycling becomes part of normal operation, operators may be missing a power savings opportunity. Reviewing the compressor’s capacity against actual process demand can help determine whether the unit is doing unnecessary work.

Turning Compressor Data Into Measurable Results

Together, these three lessons show why compressor data needs to lead to action. The process starts with the right data, but it does not stop there.

Operators need a repeatable way to analyze current conditions, evaluate safe options, make the selected change, and confirm the result. That is how compressor insight becomes measurable improvement across production, reliability, and operating cost.

Watch the full webinar below for a closer look at the three case studies and the steps the Detechtion team used to evaluate each opportunity.

Introducing the Action Tracker: Optimization and Workflow Management

Sailin Li — Mon, 28 Apr 2025 21:33:03 GMT

At Detechtion, we’re excited to announce a major advancement in fleet and workflow collaboration with the release of Enalysis Version 5.11! As part of this release, we’re proud to introduce the Action Tracker — a powerful new tool that will fundamentally improve the way optimization opportunities and projects are managed between your team and Detechtion.

SkidIQ Adapt: A Breakthrough in Dynamic Compressor Control

zbennett@Detechtion.com (Zachary Bennett) — Thu, 27 Mar 2025 21:04:36 GMT

Optimize Your Compressor Fleet with SkidIQ Adapt: Watch the Webinar

zbennett@Detechtion.com (Zachary Bennett) — Fri, 14 Feb 2025 16:48:58 GMT

Discover the Future of Compressor Control—Watch the Webinar On-Demand and Sign up to be an Early Adopter!

Recap of 'The Humanity of AI' Webinar

zbennett@Detechtion.com (Zachary Bennett) — Fri, 06 Dec 2024 21:30:04 GMT

Thanks to Hart Energy Webinars for co-hosting with Detechtion as our own Ruchita Rozario, Data Science Supervisor and Brandon Ambrose, Strategic Advisor at Detechtion presented “The Humanity of AI: Bridging the Gap for Smarter Decisions in Mobile Oilfield Operations”.

Where Compressor Optimization Meets Emissions Performance

zbennett@Detechtion.com (Zachary Bennett) — Thu, 05 Dec 2024 17:33:36 GMT

Not every emissions savings opportunity starts with a leak. Sometimes, it starts with a compressor doing more work than the process requires.

In the fifth episode of Detechtion’s Know Pressure: Your Guide to Compressor Fleet Management webinar series, we looked at how compressor optimization connects to emissions performance. The discussion focused on how operators can identify compression inefficiencies that affect fuel use and emissions while keeping production needs at the center.

Key Takeaways:

Compressor emissions often start with fuel gas use.
Unnecessary compressor load can increase emissions impact.
Compressor optimization can reduce emissions impact without reducing production.

Start With Where Compressor Emissions Come From

Emissions can come from several sources across a compression facility. During the webinar, we discussed compressor emissions tied to engine combustion, venting, blowdowns, rod packing, crankcase sources, and flaring.

For compressor optimization, one of the most important sources to understand is stationary combustion from gas-driven engines. When a compressor requires more horsepower to move gas, the driver typically requires more fuel gas. That fuel use can contribute to reported emissions, which means changes in compressor load may also change the emissions picture.

That is why emissions optimization is not only an environmental or regulatory discussion, but also an operational discussion. If a compressor is doing more work than the process requires, that extra work may show up as higher fuel use, higher operating cost, and higher emissions impact.

Look at Fuel Gas Savings as More Than an Operating Cost

The case study we discussed during the webinar started with a two-stage compressor that was bypassing gas nearly 100% of the time to maintain process pressure between stages. The compressor unit was still maintaining production, but it was also doing excess compression work.

In the original case, the compressor was electrically driven, so the opportunity was measured as power savings. Detechtion evaluated several configuration options and identified a change that reduced bypass gas and lowered the power required to move the same gas while maintaining production and process pressure.

Next, we looked at that same opportunity through the lens of a gas-driven compressor. If reducing load lowers the horsepower required by the unit, that can reduce fuel gas consumption. At that point, the value of the optimization opportunity is no longer limited to energy or fuel savings alone. It may also have emissions implications.

Connect Lower Fuel Use to Emissions-Related Savings

The financial value of emissions reduction depends on the facility, the applicable regulation, and where the operation sits relative to its emissions threshold or benchmark.

During the webinar, we discussed two examples: the Waste Emissions Charge in the United States and the TIER framework in Alberta. While the details differ, both examples show why emissions-related savings can become part of the larger business case for compressor optimization.

If a facility is above an applicable threshold or benchmark, reducing fuel gas use may help reduce emissions-related costs. If a facility is below that threshold, understanding fuel use and emissions impact still gives operators a clearer view of how much room they have before additional costs may apply.

The larger takeaway is that compressor optimization can help teams evaluate savings more completely. A project that reduces unnecessary load may lower fuel gas consumption, reduce emissions impact, and support performance goals without requiring production to be sacrificed.

Turn Compressor Optimization Into Measurable Savings

Fuel gas savings and emissions savings are not always obvious from day-to-day operating data. A compressor may appear to be running normally while still doing more work than the process requires.

That is why the process starts with better visibility into the compressor’s current configuration, operating conditions, and performance. For example, in the case study we discussed, Detechtion used ongoing monitoring and analysis to evaluate the current operating state, identify inefficiency, compare configuration options, and determine which change could reduce load while maintaining production.

Longfellow Energy Upgrades to the Detechtion Compressor Control Panel

zbennett@Detechtion.com (Zachary Bennett) — Tue, 05 Nov 2024 19:46:09 GMT

October 2024 marked a significant milestone for Detechtion Technologies as we successfully installed our first full compressor control panel for Longfellow Energy.

What to Track Before Compressor Reliability Issues Escalate

zbennett@Detechtion.com (Zachary Bennett) — Wed, 30 Oct 2024 21:19:45 GMT

A compressor reliability issue does not always begin with an obvious shutdown or catastrophic failure. In many cases, the earliest signs show up first in the data.

A gas compressor unit may still be running, but certain indicators can point to a developing issue long before the full impact is visible in the field. Below are some of the key reliability indicators discussed during the fourth episode of our Know Pressure: Your Guide to Compressor Fleet Management webinar series, and what they can reveal about a developing compressor issue before it escalates.

Key Takeaways:

Developing reliability issues often show up in compressor data before they turn into failures.
High rod load, excessive blowby, and changing pressure relationships can point to a deeper issue elsewhere in the compressor unit.
Frequent, high-resolution data helps teams spot trends earlier and take action before compressor downtime escalates.

Track High Rod Load

High rod load is one of the most important reliability indicators for operators to watch because it reflects the mechanical force being placed on the rod and can be the first sign that something more serious is developing.

In the two case studies we covered in the webinar, rod load was the condition that first brought compressor units into focus. In one case, the issue had already escalated into a severe failure. In the other, it was caught early enough to investigate before the damage became more serious.

That contrast is part of what makes rod load so important to track. A unit may still be running, but rising rod load can be an early warning sign that the compressor is moving toward a larger reliability problem. It is not always the root cause, but it is often the signal that tells operators where to look next.

Track Excessive Blowby

Excessive blowby is another important indicator because it can reveal inefficiency that may not be obvious from raw operating data alone.

Blowby is a KPI that measures the difference between the expected and actual discharge temperatures. When actual discharge temperature runs higher than expected, it can point to recirculating gas, damaged valves, or other conditions that prevent a stage from moving gas as effectively as it should.

For both case studies, blowby helped explain why rod load remained elevated and why the issue was not isolated to a single stage. In simple terms, blowby pointed to performance loss inside the cylinder and helped show that inefficiency in one part of the compressor was creating stress somewhere else.

Track How Pressure and Work Shift Across the Unit

Pressure and loading do not always shift evenly across a compressor, which is why operators should also look beyond the first flagged condition. The case studies we discussed during the webinar showed that when one stage becomes less efficient, the effects can move upstream and create reliability problems somewhere else in the unit.

That is what made pressure relationships and compression ratio changes so important in these examples. Compression ratio, or how much the gas is being compressed across a stage, can help show when more work is being pushed onto one part of the unit. As inefficiency increased on later stages, suction pressure rose, more work shifted upstream, and rod load on stage one increased.

More broadly, when one part of the compressor begins to underperform, operators should look across the full unit to understand whether the real issue may be developing somewhere else.

Track Trends, Not Just Snapshots

Lastly, frequent, high-resolution data can make reliability indicators more useful because it helps operators see whether a condition is stable, worsening, or moving in step with another issue. As we noted during the webinar, the contrast between low-frequency manual entry and higher-frequency automated reporting helped show why that visibility matters.

When operators can only see isolated data points, it becomes much harder to tell whether a flagged condition is stable, getting worse, or connected to another issue. That is what makes higher-frequency data so valuable for prioritizing inspections and acting before a developing reliability issue turns into a failure.

Enbase Mobile: Fleetwide Visibility at your Fingertips

tlozier@detechtion.com (Tim Lozier) — Tue, 29 Oct 2024 17:10:25 GMT

With the release of the new Enbase Mobile application, we sat down with Sarah Whitney, Product Owner of Enbase to tell us a little bit about the app and how it’s going to help users of our Enbase monitoring and control solutions: