Discovering the usage envelope for materials in oil & gas applications can be challenging and expensive, especially for valuable materials with a complicated corrosion mechanism.
DNV GL aims to establish a framework for the collection of publicly-viewable (and private) materials data and apply thermodynamic modelling and machine-learning algorithms to enhance the knowledge about the usage envelope for corrosion-resistant alloys (CRAs) such as 13 Cr, 316L, 22 Cr Duplex and Alloy 28/29.
Predictive models for environmentally assisted cracking (EAC)
Database for data-driven decision-making
Identify new factors contributing to EAC
Focused (and therefore lower-cost) testing
Industry partners will obtain value in various scenarios:
Oil companies/material producers
Wider envelope – eg, cost savings from lower alloys or sales revenue from increased material usability
Narrower envelope – eg, cost savings from reduced failures or sales revenue from higher alloys