Built by engineers who spent years inside midstream operations watching preventable failures happen.
Beatriz Herrera spent 11 weeks embedded as a reliability consultant at a Texas-based midstream operator in the Permian Basin in early 2023. During those weeks, she sat alongside field technicians as three separate compressor stations experienced unplanned shutdowns within a six-week window. Each one was detectable in hindsight from AVEVA PI historian data that nobody had time to review between maintenance calls.
The operator had 18 months of high-resolution historian data sitting in AVEVA PI that could have predicted each of those failures 10 to 14 days out. The data was structured for post-incident forensics, not real-time fault detection. There was no layer between the raw sensor archive and the SCADA alarm that tripped only when the unit was already failing.
The first prototype was a Python scoring model run nightly against PI System exports for a single compressor station, shared with the operator's reliability engineer as a weekly Excel report. Within three weeks it surfaced an impeller imbalance signature that led to a planned outage instead of an emergency shutdown, avoiding an estimated $65,000 in emergency maintenance costs.
Midstreamly is that prototype, rebuilt as a production system. It integrates directly with AVEVA PI, Bently Nevada, and Emerson Ovation. Fault-mode alerts appear inside the operator's existing SCADA and IIoT environment. No separate monitoring portal required.
The mission: give midstream operators a predictive maintenance layer their existing historian data always contained but could never surface.
Midstreamly exists to close the gap between the structured sensor archive that most midstream operators already own and the actionable fault alerts they have never been able to generate from it. The mission is not to install new instrumentation. It is not to replace existing historian systems. It is to add the analytical layer between the raw data and the SCADA operator that surfaces degradation trajectories before protection thresholds are reached.
Every midstream operator running AVEVA PI, Bently Nevada, or Emerson Ovation has data that could have predicted their last three compressor failures. The data was there. The layer to read it in real time was not. Midstreamly is that layer.
The specific mission statement Beatriz Herrera carried out of the Permian Basin engagement in 2023: give midstream operators a predictive maintenance layer their existing historian data always contained but could never surface. That sentence is unchanged. It is the filter for every product decision we make.
Midstreamly is not a horizontal industrial AI platform that handles every asset type across every industry. It is a vertical predictive maintenance product purpose-built for pumps and compressors in midstream oil and gas operations. That scope is deliberate.
The failure modes for midstream reciprocating and centrifugal compressors are well-characterized. The instrumentation standard is established: Bently Nevada 3500 Series for high-criticality rotating equipment, AVEVA PI as the historian backbone, Emerson Ovation or Honeywell Experion as the control system. The economics of an unplanned compressor shutdown in a gathering or transmission system are consistent enough to build financial models around. These conditions allow Midstreamly to deliver fault-mode detection that is specific, calibrated, and actionable rather than generic.
We are currently deployed at gathering system operators in the Permian Basin, Eagle Ford, and Gulf Coast regions. We support compressor fleets ranging from 12 to 140 monitored units. Our stage focus is operators who have AVEVA PI historian data going back at least 12 months, Bently Nevada 3500 Series installations at their high-criticality compressor stations, and maintenance events that cost more than $15,000 each when unplanned. If that describes your operation, Midstreamly is built for you.
Every product decision at Midstreamly runs through five operating principles that came directly from field experience in midstream operations. These are not aspirational values written after the fact. They are the constraints we discovered by watching what fails in industrial AI deployments and deliberately designing against those failure modes.
The Midstreamly team spent years inside midstream operations before building this platform. We understand what field technicians need from an alert, what reliability engineers need from a dashboard, and why another monitoring portal nobody logs into is not the answer.