{"title":"Optimizing Seawater Treatment Operations with Condition Monitoring Software","authors":"A. Wilcox, R. Mikkelsen, Pei Ling Esther Lian","doi":"10.4043/29567-MS","DOIUrl":null,"url":null,"abstract":"\n In an effort to maximize the value of the Enhanced Oil Recovery(EOR) process, a condition monitoring software program aims to optimize Seawater Treatment system performance and maintenance efforts. By collecting digital inputs from sensors, instruments and controllers on the platform or vessel, you can monitor system behavior and use application expertise and data science to characterize the operational conditions which enable operators to reduce their operating costs and maximize production.\n Combining unparalleled process expertise with data science and software development team, a system-wide view of the Seawater Treatment (SWT) process is produced. Using current and historical data from SWT operations, ingesting into an IOT platform and, utilizing custom software program, provide full visualization of the system performance and condition monitoring of critical components within the system. One example is monitoring the sulphate removal unit (SRU) and predicting fouling types of the membranes. With this enhanced view of performance and predictive analysis, you can reduce the need for offshore supervision and troubleshooting efforts and can prevent repeat failures and unplanned downtime.\n Using the SRU as an example, the Seawater Treatment software program enables early stage detection of membrane fouling which allows the operator to proactively implement a fouling mitigation program. By detecting the fouling early, the operator can optimize the cleaning in place (CIP) sequences, perform timely CIP and chemical dosing to extend the life of the membranes and prevent unnecessary downtime and prevent permanent membrane damage. It has been observed through historical data that operators can save a significant amount of money per year on membrane life, downtime reduction and production penalty prevention. There are additional potential savings by using an optimized chemical injection program to manage and prevent biogrowth/scale formation in the system.\n To address the operator's need of optimizing Seawater Treatment and other topside process equipment, a suite of process specific software applications can be fully integrated into a digital platform, providing a framework that easily can be tailored to customer's needs to include additional features if required. Combining a comprehensive selection of wellstream processing technologies with deep understanding of fluids behavior and proven track record of digitalization, operational issues can now be uncovered and solved. This is different from typical remote monitoring initiatives in that it applies proven machine learning and predictive analytics frameworks to detect patterns and traces from the captured data to provide the earliest possible detection of future issues and provide proactive recommendations to prevent disruption to operations.","PeriodicalId":10968,"journal":{"name":"Day 3 Wed, May 08, 2019","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Day 3 Wed, May 08, 2019","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4043/29567-MS","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In an effort to maximize the value of the Enhanced Oil Recovery(EOR) process, a condition monitoring software program aims to optimize Seawater Treatment system performance and maintenance efforts. By collecting digital inputs from sensors, instruments and controllers on the platform or vessel, you can monitor system behavior and use application expertise and data science to characterize the operational conditions which enable operators to reduce their operating costs and maximize production.
Combining unparalleled process expertise with data science and software development team, a system-wide view of the Seawater Treatment (SWT) process is produced. Using current and historical data from SWT operations, ingesting into an IOT platform and, utilizing custom software program, provide full visualization of the system performance and condition monitoring of critical components within the system. One example is monitoring the sulphate removal unit (SRU) and predicting fouling types of the membranes. With this enhanced view of performance and predictive analysis, you can reduce the need for offshore supervision and troubleshooting efforts and can prevent repeat failures and unplanned downtime.
Using the SRU as an example, the Seawater Treatment software program enables early stage detection of membrane fouling which allows the operator to proactively implement a fouling mitigation program. By detecting the fouling early, the operator can optimize the cleaning in place (CIP) sequences, perform timely CIP and chemical dosing to extend the life of the membranes and prevent unnecessary downtime and prevent permanent membrane damage. It has been observed through historical data that operators can save a significant amount of money per year on membrane life, downtime reduction and production penalty prevention. There are additional potential savings by using an optimized chemical injection program to manage and prevent biogrowth/scale formation in the system.
To address the operator's need of optimizing Seawater Treatment and other topside process equipment, a suite of process specific software applications can be fully integrated into a digital platform, providing a framework that easily can be tailored to customer's needs to include additional features if required. Combining a comprehensive selection of wellstream processing technologies with deep understanding of fluids behavior and proven track record of digitalization, operational issues can now be uncovered and solved. This is different from typical remote monitoring initiatives in that it applies proven machine learning and predictive analytics frameworks to detect patterns and traces from the captured data to provide the earliest possible detection of future issues and provide proactive recommendations to prevent disruption to operations.