Why WellAware’s Closed-Loop Control Wins in the Real World
The problem
Injection variance measurement isn’t a lab problem — it’s a field problem. Most injection sites contend with changing back-pressure, inconsistent power quality, temperature-driven viscosity changes, entrained gas, fouling and coating, installation variability, and pump wear and drift, all at the same time.
The result: systems that look accurate on paper often drift over time, require frequent troubleshooting, or quietly report misleading injection numbers — and that assumes they don’t fail in the field outright.
WellAware has spent over a decade refining a different measurement method, one that uses the hardware you already have and proven analytics to give you confidence in every reading.
Two approaches — one is structurally more reliable
Approach A: Measure frequently using extra hardware
Many competing systems try to improve accuracy by inserting additional components into the field system: solenoid diversion cycles, sight glass measurement columns, external hydrostatic sensors reading pressure through the sight glass and tank, and automatic pump calibrations. The architecture depends on high-frequency mechanical actuation and added hardware.
It can work, but the trade-offs add up. It costs more, ties you to specific hardware, and introduces new failure points and leak risk. It requires tight tolerances and high-cycle wear components, which means more failure modes, more troubleshooting, and more ongoing maintenance. It puts additional stress on the pump motor and power system. And it doesn’t address the root cause of the problems driving variance in the first place.
Sight glasses are an excellent tool — we use them for our initial pump calibration — but keeping them accurate over time requires a knowledgeable tech onsite and specific site hardware. WellAware’s system instead relies on the hardware you already have, and grows more accurate over time.
Approach B: Use what’s already there, and control precisely (the WellAware method)
WellAware takes a simpler, more scalable, data-based approach. We start with a manual calibration at time zero, then treat the tank as the truth reference using edge analytics and temperature compensation. Measurement improves over time through many small readings and statistical calibration informed by WellAware’s decade-plus of operations. WellAware Optimization recalibrates the pump automatically as needed after assessing system variables like pump behavior, power, and tank metrics, and AI/ML-based anomaly detection and recommendations flag the root-cause system problems that affect variance.
The result is consistent performance with little to no component failure.
Why WellAware Wins
The tank and analytics are the best variance-minimizing tools you’ll ever have.
Small sight glass measurement windows amplify noise from bubbles, switching dynamics, sensor drift, and fouling. A tank-based method uses a much larger reference volume over longer periods, which improves signal-to-noise, reduces relative error, provides a better truth source for injection verification, and helps detect leaks or injection-status problems at the source.
Fewer failure points mean higher uptime
Competitor architectures introduce several additional failure modes: solenoids sticking or losing timing coordination; sight glasses fouling, plugging, or leaking; leaks at fittings and added hardware; mounting, orientation, and timing errors; hydrostatic sensor drift; and added strain on the power system from constant power draw.
WellAware removes the fragile measurement hardware and replaces it with software-driven confidence and a longer-horizon truth source.
Closed-loop speed control stabilizes injection variance
Measurement alone doesn’t prevent drift. Injection accuracy degrades when back-pressure changes, voltage fluctuates, viscosity shifts, or the pump motor wears. WellAware’s closed-loop motor control maintains stable pump delivery through that real-world variation.
What this means for operators
With WellAware you get higher confidence in injected volume, reduced system maintenance, and fewer truck rolls. You get finer pump control, less downtime from measurement-hardware failures, and greater insight across all your connected hardware — from a measurement system that improves over time instead of degrading.
Bottom line
Solenoid and sight-glass systems try to improve accuracy by adding fragile, expensive measurement hardware to harsh field environments. WellAware solves accuracy differently: we use the most stable reference on site — the tank — coupled with edge analytics, build confidence through long-horizon averaging and calibration, and stabilize injection with closed-loop control backed by AI/ML recommendations that address the root-cause problems affecting variance.
The result is a more scalable, lower-maintenance, higher-uptime injection measurement solution that only gets better as you use it. And because the approach is data-based, it identifies system issues before they cause downtime — notifying users of problems and prioritizing which sites to visit and why.
Let’s review your current injection setup and identify where variance is costing you.
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