Search

This article reports on poly (acrylamide-co-malonic acid) and poly(AM-co-MAc) copolymers that inhibit the iron sulfide scale. The iron sulfide scale is considered as one of the most challenging oilfield scales to dissolve or inhibit once formed due to the fast deposition kinetics and low solubility criteria. We present the synthesis, characterization, and testing of poly(AM-co-MAc) that inhibits iron sulfide formation by hindering nucleation growth and preventing particles from settling. A strictly anoxic apparatus was designed and used to evaluate the performance of the synthesized inhibitor. The influence of temperature, pH, and brine composition was also quantified. The results indicated that poly(AM-co-MAc) inhibits iron sulfide formation and nucleation growth under various conditions. The inhibitor effectively maintained iron sulfide particle separation at specific dosages, lowering its ability to cluster. Maximum efficiency of 92% was recorded at 60 °C and 1% volume inhibitor concentration. DFT calculations confirmed the inhibitive properties of poly(AM-co-MAc) via binding energy calculations. Compared to recently reported inhibitors, poly(AM-co-MAc) proves to be safer concerning toxicity properties. This study provides theoretical and technical insights for a novel chemistry solution for the metal-sulfide scale. The authors would like to acknowledge the City of Grand Forks for funding this study through Project Number: UND0023658 and Qatar University National Capacity Building Program (NCBP), grant #QUCP-CENG-2021-03. The Research Computing group from Texas A&M University at Qatar provided the HPC resources and services used in this work. The Qatar Foundation funds research computing for education, science, and community development ( http://www.qf.org.qa ). The outcomes achieved are exclusively the responsibility of the authors. Scopus

A reliable and practical determination of a chemical species’ solubility in water continues to be examined using empirical observations and exhaustive experimental studies alone. Predictions of chemical solubility in water using data-driven algorithms can allow us to create a rationally designed, efficient, and cost-effective tool for next-generation materials and chemical formulations. We present results from two machine learning (ML) modeling studies to adequately predict various species’ solubility using data for over 8,400 compounds. Molecular-descriptors, the most used method in previous studies, and Morgan fingerprint, a topological, circular-based hash of the molecules' structures, were applied to produce water solubility estimates. We trained all models on 80% of the total datasets using the Random Forest (RFs) technique as the regressor and tested the prediction performance using the remaining 20%, resulting in R2 test values of 0.88 and 0.82 for the descriptors and circular fingerprint methods, respectively. We interpreted the produced ML models and reported the most effective features for aqueous solubility measures using Shapley Additive exPlanations (SHAP) and thermodynamic analysis. Low error, ability to investigate the molecular-level interactions and compatible with thermodynamic quantities made fingerprint a distinct model compared to other available computational tools.

The calcium carbonate (CaCO3 ) scale is one of the most common oilfield scales and oil and gas production bane. CaCO3 scale can lead to a sudden halt in production or, worst-case scenario, accidents; therefore, CaCO3 scale formation prevention is essential for the oil and gas industry. Scale inhibitors are chemicals that can mitigate this problem. We used two popular theoretical techniques in this study: Density Functional Theory (DFT) and Ab Initio Molecular Dynamics (AIMD). The objective was to investigate the inhibitory abilities of mixed oligomers, specifically acrylamide functionalized silica (AM-Silica). DFT studies indicate that Ca2+ does not bind readily to acryl acid and acrylamide; however, it has a good binding affinity with PAM and Silica functionalized PAM. The highest binding affinity occurs in the silica region and not the -CONH functional groups. AIMD calculations corroborate the DFT studies, as observed from the MD trajectory that Ca2+ binds to PAM-Silica by forming bonds with silicon; however, Ca2+ initially forms a bond with silicon in the presence of water molecules. This bonding does not last long, and it subsequently bonds with the oxygen atoms present in the water molecule. PAM-Silica is a suitable calcium scale inhibitor because of its high binding affinity with Ca2+ . Theoretical studies (DFT and AIMD) have provided atomic insights on how AM-Silica could be used as an efficient scale inhibitor. 2022 by the authors. Licensee MDPI, Basel, Switzerland. Acknowledgments: This work was supported by Qatar University National Capacity Building Program (NCBP), grant #QUCP-CENG-2021-03, and Qatar National Research Fund grant # NPRP12S-0130-190023. The Research Computing group at Texas A&M University in Qatar provided the HPC resources and services used in this work. Qatar Foundation funds research computing for Education, Science, and Community Development (http://www.qf.org.qa, accessed on 1 January 2020). Giuliano Carchini and Elkhansa Elbashier are appreciated for their fruitful discussion. Qatar University and the Gas Processing Center are acknowledged for their support. The outcomes achieved herein are exclusively the responsibility of the authors. Scopus

Calcium carbonate scale forms in production and injection wells, precipitating upon pressure and temperature changes, incompatible water mixing, and dehydration. Production shutdown is one of the most dramatic consequences caused by calcium carbonate scale formation. We synthesized, characterized, and tested a polyacrylamide functionalized nano-silica (PAM-SiO2NPs) inhibitor that prevents calcium carbonate formation at high temperatures and high brine. Positive compatibility results were confirmed before the inhibition efficiency test, whereby the efficiency was calculated by quantifying the calcium ions left in solution using ICP-OES. The inhibitor was effective at temperatures of 40, 60, and 80 C, with pH values of 7, and 9, and varying brine compositions. The inhibition efficiency increased with an increase in temperature and pH, reaching 95% after 16 h of reaction time at a temperature of 80 C, and a pH of 7 using an inhibitor concentration of 20 ppm (mg/l). A higher amount of scale formed due to an abundance of calcium ions with a cationic to anionic ratio of 80/20; however, the inhibition efficiency was higher with a ratio of 20/80, especially at lower inhibitor dosages. The collected scale in the blank and the samples with low inhibitor concentrations were characterized using SEM/EDS and TEM. The Characterization indicated that the inhibitor chelates calcium, preventing scale formation. SEM results indicated a change in scale morphology as the inhibitor was absorbed to the active crystal sites, interrupting the microcrystal growth. These findings indicate promising inhibition results at high temperatures, varying pH values, and different brine concentrations with relatively low inhibitor concentrations. Results were compared to commercial inhibitors and found to be more effective (>80%) at very low concentrations (0.2 ppm). We are grateful for the City of Grand Forks for funding this study through Project number: UND0023658 . Scopus

Reverse osmosis (RO) is poised to be the primary membrane-based water desalination technology as the need for potable water increases. Current and prior research focuses on the implications of the RO membrane system performance as it pertains to scale; however, very few reviews of scale and its detrimental effects within the concentrate line that affect both system performance and reliability at pilot-scale are available. We analyzed peer-reviewed literature covering the most recent studies on scale characterization and mitigation strategies. Data gathered from a pilot-scale plant experiment conducted with an industrial collaborator were used to emphasize the importance of quality information on this topic. Published data, and data collected from the pilot plant were investigated and analyzed to determine the most significant scaling problems and methods of mitigation. Potential antiscalant alternatives to mitigate the complications caused by scale formation and fouling within RO systems, particularly in concentrate lines, were investigated.