Thermodynamic Prediction of Scale Formation in Oil Fields During Water Injection: Application of SPsim Program Through Utilizing Advanced Visual Basic Excel Tool

被引:0
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作者
Hashemi, Seyed Hossein [1 ]
Besharati, Zahra [2 ]
Torabi, Farshid [1 ]
Pimentel, Nuno [3 ]
机构
[1] Petroleum Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina,SK,S4S 0A2, Canada
[2] Department of Chemistry, School of Science, Alzahra University, Tehran,1993893973, Iran
[3] Instituto Dom Luiz (IDL), Universidade de Lisboa, Lisbon,1749-016, Portugal
关键词
Barium sulfate - Offshore oil fields - Offshore oil well production - Offshore oil wells - Oil well flooding - Petroleum reservoir evaluation - Secondary recovery - Strontium deposits;
D O I
10.3390/pr12122722
中图分类号
学科分类号
摘要
This study focuses on the design and validation of a computer program named SPsim, developed using Visual Basic coding and advanced Excel tools to predict the formation of sulfate mineral deposits during water injection in oil fields. Water injection for secondary oil recovery is an effective economic strategy, but it can be negatively impacted by the formation of sulfate minerals such as calcium sulfate, gypsum, barium sulfate, and strontium sulfate. The results of this study demonstrate that SPsim can accurately predict the formation of these mineral deposits based on the composition of the formation water and injection water under various temperature and pressure conditions. Specifically, the formation of barium sulfate and calcium sulfate is observed under certain conditions, which is a significant concern in oil fields. The study also highlights that calcium sulfate, barium sulfate, and strontium sulfate are among the most challenging mineral deposits in the studied fields, while the formation of gypsum deposits is less significant. The program was compared with results from other software tools, such as ScaleChem 3.2 and StimCad 2, as well as field observations. The findings indicate that SPsim provides a reliable and effective tool for predicting and managing sulfate scaling in water injection operations, making it a valuable resource for both industrial and academic applications. © 2024 by the authors.
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