In refinery environments—dense, high-risk, and structurally complex—the margin for error is slim. Assets operate under extreme temperatures and pressures. Facilities are labyrinthine, built across decades of upgrades, brownfield modifications, and often incomplete documentation. In this context, the traditional approach to maintenance, where teams rely on periodic inspections or failure-based interventions, even old 2D drawings, can no longer meet the scale, safety, and operational demands of modern energy markets. This is where predictive maintenance software for refineries becomes a critical enabler of safety, continuity, and performance.
Scan Data Management in Refineries
The importance of predictive maintenance in refineries stems from the high cost of unplanned downtime and the risks posed by asset failure. A single day offline in a major refinery can result in losses ranging from hundreds of thousands to several million dollars, depending on output capacity and product mix. Failures in heat exchangers, compressors, or piping can trigger safety shutdowns, HSE incidents, or catastrophic damage. Predictive maintenance leverages a combination of sensor data, historical performance, and physical condition indicators—often derived from scan data—to forecast failure points before they occur. This allows teams to replace, repair, or reinforce assets at the optimal time.
The value of these workflows lies in their ability to drive a more intelligent digital twinstrategy. A digital twin is only as useful as the data it contains.
