ROSEN Canada upgrades MFL-A technology in response to the requirements of the regional market offering Canadian customers an improved and state-of-the-art service.

ROSEN Canada introduces the RoCorr MFL-A+ Service in response to the ever-growing need for improved integrity management of corrosion in pipelines. This upgrade to the existing service for in-line high-resolution metal loss detection and sizing incorporates the latest improvements in data evaluation and anomaly sizing algorithms as well as substantially improved specification parameters for assessing pinhole corrosion and axial slotting, enabling effective management of corrosion threats across the full Pipeline Operator Forum (POF) dimension classifications.

The specific upgrades include a machine-learning-based data evaluation system used to classify and size corrosion defects. Known as AutoDataâ„¢, this artificial intelligence (AI) approach to data evaluation applies artificial neural networks to process large amounts of data, visual recognition algorithms to identify cluster anomalies and installations, all while reducing false call. The system is based on thousands of laser-mapped real pipe defects to identify even the smallest defects. The machine-learning component allows for long-term continuous improvement as each evaluation is performed and reduces human error in defect sizing.

Improved Specification Parameters

Improved specification parameters allows for better assessment of pinhole corrosion and axial slotting.

Improved Specification Parameters

Includes machine-learning-based data evaluation system for classification and sizing of corrosion defects.