Development of New Corrosion Inhibitors Using Robotics with High Throughput Experimentation Methods

2021 ◽  
Author(s):  
Nihal U Obeyesekere ◽  
Jonathan J Wylde

Abstract Critical micelle concentration (CMC) is a known indicator for surfactants such as corrosion inhibitors ability to partition from two phase systems such as oil and water. Most corrosion inhibitors are surface active and at critical micelle concentration, the chemical is partitioned to water, physadsorb on metallic surfaces and form a physical barrier between steel and water. This protective barrier thus prevents corrosion from taking place on the metal surface When the applied chemical concentration is equal or higher than the CMC, the chemical is available in aqueous phase, thus preventing corrosion. Therefore, it was suggested that CMC can be used as an indicator of optimal chemical dose for corrosion control1. The lower the CMC of a corrosion inhibitor product, the better is this chemical for corrosion control as the availability of the chemical in the aqueous phase increase and therefore, can achieve corrosion control with less amount of chemical. In this work, this physical property (CMC) was used as an indicator to differentiate corrosion inhibitor performance. The corrosion inhibitor formulations were built out by using combinatorial chemical methods and the arrays of chemical formulations were screened by utilizing high throughput robotics 2-4, using CMC as the selection guide. To validate the concept, several known corrosion inhibitor formulas were selected to optimize their efficacy. Each formula contained several active ingredients and a solvent package. These raw materials were blended in random but in a control, manner using combinatorial methodologies. Instead of rapidly blending a large number of formulations using robotics, the design of control (DOE) methods were utilized to constrain the number of blends. Once the formulations were generated by DOE method, using Design Expert software that can effectively explore a desired space. The development of an equally robust prescreening analysis was also developed. This was done by using the measurements of CMC with a high-throughput screening methodology. After formulation of a vast array of formulation by using Design Expert software, the products were screened for by CMC using automated surface tension workstation. Several formulations with lower CMC than the reference products were selected. The selected corrosion inhibitor formulations were identified and blended in larger scales. The efficacy of these products was tested by classical laboratory testing methods such as rotating cylinder electrode (RCE) and rotating cage autoclave (RCA) to determine their performance as anti-corrosion agents. These tests were performed against the original reference corrosion inhibitor. The testing indicated that several corrosion inhibitor formulations outperform the original blend thus validating the proof of concept.

2021 ◽  
Author(s):  
Nihal U. Obeyesekere ◽  
Jonathan J. Wylde ◽  
Thusitha Wickramarachchi ◽  
Lucious Kemp

Abstract Critical micelle concentration (CMC) is a known indicator for surfactants such as corrosion inhibitors’ ability to partition to water from two phase systems such as oil and water. Most corrosion inhibitors are surface active. At critical micelle concentration, the chemical is partitioned to water from the interface, physisorption on metallic surfaces and forms a physical barrier between steel and corrosive water. This protective barrier thus prevents corrosion initiating on the metal surface. When the applied chemical concentration is equal or higher than the CMC, the surfactant is partitioned to aqueous phase from the oil-water interface. This would lead to higher chemical availability of the inhibitor in water, preventing corrosion. Therefore, it was suggested that CMC can be used as an indicator to optimal chemical dose for corrosion control1-5. The lower the CMC of a corrosion inhibitor product, the better is this chemical for corrosion control as the availability of the chemical in the aqueous phase increases. This can achieve corrosion control with lesser amount of corrosion inhibitor product. Thus, increasing the performance of corrosion inhibitor product. In this work, the physical property, CMC, was used as an indicator to differentiate corrosion inhibitor performance. A vast array of corrosion inhibitor formulations was achieved by combinatorial chemical methods using Design of Experiment (DoE) methodologies and these arrays of chemical formulations were screened by utilizing high throughput screening (HTE)6-8, using CMC as the selection guide. To validate the concept, a known corrosion inhibitor formulation (Inhibitor Abz) was selected to optimize its efficacy. This formula contains several active ingredients and a solvent package. Three raw materials of this formulation were selected and varied in combinatorial fashion, keeping the solvents and other raw materials constant9. These three raw materials were blended in a random but in a controled manner utizing DoE and using combinatorial techniques. Instead of rapidly blending a large amount of formulations using robotics, the design of experiment (DoE) methods were utilized to constrain the number of blends. When attempting to discover the important factors, DoE gives a powerful suite of statistical methodologies10. In this work, Design Expert software utilizes DoE methods and this prediction model was used to explore a desired design space. The more relevant (not entirely random) formulations were generated by DoE methods, using Design Expert software that can effectively explore a desired design space. The Design of Experiment software mathematically analyzes the space in which fundamental properties are being measured. The development of an equally robust prescreening analysis was also developed. After blending a vast array of formulations by using automated workstation, these products were screened for CMC by utilizing an automated surface tension workstation. Several formulations with lower CMCs than the reference product (Inhibitor Abz) were discovered and identified for further study. The selected corrosion inhibitor formulations were blended in larger scales. The efficacy of these products was tested by classical laboratory testing methods such as rotating cylinder electrode (RCE) and rotating cage autoclave (RCA) to determine their performance as anti-corrosion agents. As the focus of this project was to optimize the corrosion Inhibitor Abz, this chemical was used as the reference product throughout of this work. The testing indicated that several new corrosion inhibitor formulations discovered from this work outperformed the original blend, thus validating the proof of concept.


CORROSION ◽  
1959 ◽  
Vol 15 (2) ◽  
pp. 37-39

Abstract A study was made of completion practices and corrosion control measures for gas wells with tubing pressures in excess of 5000 psi. A survey of 12 producing companies was made to determine what had been done to combat the problem. This paper reflects the results of this survey and includes data collected from 166 wells. Topics discussed include design problems in well installation, treatment with corrosion inhibitors, corrosion inhibitor efficacy evaluation, well characteristics, casing and tubing programs, and inspection methods. Data are given by company on the number of P-grade casing and tubing failures in service in well, failures occurring during hydrostatic tests, percent rejects of inspected pipe, and cost of failures of casing and tubing. 8.4.2


2020 ◽  
Vol 44 (19) ◽  
pp. 7647-7658
Author(s):  
Paul A. White ◽  
Gavin E. Collis ◽  
Melissa Skidmore ◽  
Michael Breedon ◽  
Wayne D. Ganther ◽  
...  

Using chemical design, computational modelling, structure–activity and structure–property relationships, with high-throughput solution and coating assays we can rapidly identify prime corrosion inhibitor candidates for further detailed evaluation.


2021 ◽  
Author(s):  
Sarvesh Mehta ◽  
Siddhartha Laghuvarapu ◽  
Yashaswi Pathak ◽  
Aaftaab Sethi ◽  
Mallika Alvala ◽  
...  

<div>In drug discovery applications, high throughput virtual screening exercises are routinely performed to determine an initial set of candidate molecules referred to as "hits". In such an experiment, each molecule from large small-molecule drug library is evaluated for physical property such as the binding affinity (docking score) against a target receptor. In real-life drug discovery experiments, the drug libraries are extremely large but still a minor representation of the essentially infinite chemical space , and evaluation of physical property for each molecule in the library is not computationally feasible. </div><div>In the current study, a novel machine learning framework "MEMES" based on Bayesian optimization is proposed for efficient sampling of chemical space. The proposed framework is demonstrated to identify 90% of top-1000 molecules from a molecular library of size about 100 million, while calculating the docking score only for about 6% of the complete library. We believe that such a framework would tremendously help to reduce the computational hour and resources in not only drug-discovery but also areas that require such high-throughput experiments.</div>


2012 ◽  
Vol 35 (22) ◽  
pp. 3197-3207 ◽  
Author(s):  
Matthias Wiendahl ◽  
Stefan A. Oelmeier ◽  
Florian Dismer ◽  
Jürgen Hubbuch

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