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<p>Accurate prediction of corrosion rates and optimization of inhibitor strategies are vital for enhancing material durability and operational efficiency in oilfield environments. This study pioneers the use of Response Surface Methodology (RSM) to predict corrosion behavior and optimize inhibitor efficiency for L80-1 steel compared to L80-3Cr, L80-13Cr, and 2205 duplex stainless steel in oil reservoirs. Through immersion weight loss tests and electrochemical measurements, L80-1 was found to be the most corrosion-prone, with high salinity and temperature accelerating degradation. RSM innovatively modeled the impacts of inhibitor concentration, temperature, pH, and exposure time, achieving a remarkable R<sup>2</sup> value of 0.972 for corrosion rate predictions and 0.8927 for inhibitor efficiency predictions, demonstrating exceptional model accuracy. These robust predictive models provide new insights into the complex interplay of environmental factors and inhibitor performance. The findings enable optimized material selection and corrosion control strategies, significantly improving the safety and efficiency of oil well operations, particularly in Iraqi oilfields. This novel application of RSM offers a systematic approach to corrosion management, advancing the field of materials engineering in challenging oilfield conditions.</p>

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Corrosion rate prediction and inhibitor optimization for L80-1 steel and alloyed materials using response surface methodology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月25日

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  • ALI HUSSEIN KHALAF,
  • Bing Lin,
  • Zhang Bo,
  • Ying Xiao,
  • Ahmed N Abdalla,
  • Junlei Tang

摘要

Accurate prediction of corrosion rates and optimization of inhibitor strategies are vital for enhancing material durability and operational efficiency in oilfield environments. This study pioneers the use of Response Surface Methodology (RSM) to predict corrosion behavior and optimize inhibitor efficiency for L80-1 steel compared to L80-3Cr, L80-13Cr, and 2205 duplex stainless steel in oil reservoirs. Through immersion weight loss tests and electrochemical measurements, L80-1 was found to be the most corrosion-prone, with high salinity and temperature accelerating degradation. RSM innovatively modeled the impacts of inhibitor concentration, temperature, pH, and exposure time, achieving a remarkable R2 value of 0.972 for corrosion rate predictions and 0.8927 for inhibitor efficiency predictions, demonstrating exceptional model accuracy. These robust predictive models provide new insights into the complex interplay of environmental factors and inhibitor performance. The findings enable optimized material selection and corrosion control strategies, significantly improving the safety and efficiency of oil well operations, particularly in Iraqi oilfields. This novel application of RSM offers a systematic approach to corrosion management, advancing the field of materials engineering in challenging oilfield conditions.