Machine Learning and Experimental Validation of S32750 Superduplex Stainless Steel for Oil, Gas, and Geothermal Wells

In this research, we developed and validated a predictive model to evaluate the performance limits of S32750 superduplex stainless steel in extreme and aggressive environments, such as oil, gas, and geothermal wells. The goal was to create a reliable framework that acceler-ates material selection by combining experimental evidence and artificial intelligence (AI). A machine learning (ML) technique was used to perform a meta-analysis of previously conducted stress corrosion cracking (SCC) tests, extracting deeper insights into the interaction between harsh conditions and steel behavior. The OLI Flux Analyzer module was used to generate a consistent matrix of physicochem-ical parameters for each test environment, ensuring data uniformity and traceability. A support vector machine (SVM) classifier was then trained to delineate the operational boundaries of the material under both oilwell and gas well conditions and an anoxic geothermal environment. To validate the model’s predictive reliability, a new experimental test was designed using Ripple’s cyclic slow strain rate testing (CSSRT), a more aggressive and realistic method than conventional slow strain rate test (SSRT). In this test, S32750 specimens were subjected to cyclic tensile loading within the elastic range to induce passive film breakdown while immersed in a corrosive solution. Post-test characterization was performed using scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDX). The laboratory results showed strong agreement with the AI-generated predictions, confirming the model’s robustness. Results confirmed the predictive capability of the model and the critical influence of parameters, such as pH, chloride activity (Cl^–), and hydrogen sulfide (H₂ S)/ carbon dioxide (CO₂) content. This combined experimental-computational approach offers a valuable tool for accelerating the qualifica-tion and selection of corrosion-resistant alloys (CRAs) in challenging operational scenarios.