Meana Petro
DF-07FundacionalOla 1TIIA

Control Automatizado de Calidad de Datos

En una frase

Continuously checks incoming data for stuck sensors, impossible values, missing periods and broken units, and reports the field’s data health as a number.

El problema

A pressure transmitter that failed six months ago and now reads a constant value will be used by every model downstream as if it were real. Data quality problems do not announce themselves; they quietly degrade every decision built on the data.

Cómo funciona

Rule-based checks — range, rate of change, stuck value, stale timestamp, unit consistency — run alongside statistical detection of distribution shift and cross-sensor consistency checks based on physical relationships. Results are aggregated into a data health score per tag, per well and per field, trended over time so degradation is visible before it is discovered by a failed model.

Datos necesarios

  • Data streams and stores to be monitored with their metadata
  • Engineering ranges and expected behaviour per tag
  • Physical relationships between tags for consistency checking
  • Instrument calibration records where they exist

Qué obtiene

  • Data health score by tag, well, field and system, trended
  • Ranked issue list with the suspected cause and the affected downstream capabilities
  • Quality flags attached to the data so models can exclude bad periods automatically

Qué no puede hacer

Detection is not repair. This finds the failed transmitter and tells you which analyses it has been corrupting; fixing it is an instrument technician’s job, and the value only materialises if the operator commits to acting on the findings. Where a check depends on a physical relationship, it cannot distinguish which of the two sensors is wrong — only that they disagree.

Cada página de capacidad declara una limitación real. Si un proveedor no puede decirle dónde termina su producto, no le están diciendo la verdad sobre dónde empieza.

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