Session: Corrosion Inhibitors in the Oil and Gas Industry (Part II of III)
Optimization of Multi-component Surfactant Mixtures for Corrosion Inhibition in CO2-containing Aqueous Environments (C2026-00110)
Thursday, March 19, 2026
8:00 AM - 8:30 AM Central
Location: 362 DE
Earn .5 PDH
Interested in reading the entire paper? Click on the "Paper" button below to read on the AMPP Knowledge Hub!
*Please note, if your registration came with access to the conference proceedings don't forget to login to your AMPP Knowledge Hub account to access the paper for free. If you login and don't have access to the paper, you can purchase the individual paper or purchase the entire conference proceedings on your Knowledge Hub account.
Mohammadhassan Sarabchi, Joshua Owen, Richard Woollam, Yahya Alhilali, Richard Barker
Geological fluids associated with oil and gas production and geothermal processing can often be characterized by elevated temperatures, high sodium chloride (NaCl) concentrations, and dissolved corrosive gases such as carbon dioxide (CO₂). Such fluids accelerate internal corrosion of carbon steel infrastructure, causing increased operational costs and safety concerns. An effective method of corrosion control in such environments often involves application of corrosion inhibitors. This study presents a methodology aimed at optimizing multi-component surfactant mixtures for enhanced corrosion inhibition performance. The surfactants used are from the homologous series of benzalkonium chlorides (BAC), consisting of 3‑different chain lengths (C12, C14, C16). From a 10‑point mixture design, critical micelle concentrations (CMCs) and corrosion inhibitor performance were determined at 30 °C in 1 wt.% NaCl brine using lipophilic dye and linear polarization resistance measurements, respectively. The maximum coverage, (θMax), adsorption equilibrium constant, (KL), adsorption (kₐ) and desorption (kd) rate constants were determined from the transient region of the corrosion rate response curve, enabling identification of mixtures exhibiting rapid adsorption, slow desorption, and superior overall inhibition efficiency. Mixture-cubic response surface model predictions facilitated the identification of an "optimum" for inhibitor mixture composition for the parameters identified in the kinetics analysis, which was evaluated experimentally.
