Chong Dai, Z. Dai, Fangfu Zhang, Yue Zhao, Guannan Deng, K. Harouaka, Xin Wang, Yi-Tsung Lu, Samiridhdi Paudyal, Saebom Ko, Shujun Gao, A. Kan, M. Tomson
{"title":"A Unified Experimental Method and Model for Predicting Scale Inhibition","authors":"Chong Dai, Z. Dai, Fangfu Zhang, Yue Zhao, Guannan Deng, K. Harouaka, Xin Wang, Yi-Tsung Lu, Samiridhdi Paudyal, Saebom Ko, Shujun Gao, A. Kan, M. Tomson","doi":"10.2118/193586-MS","DOIUrl":null,"url":null,"abstract":"\n Scale formation that can hinder continuous oil production is a serious problem in oilfield. Among all common scales, barite and calcite are two of the most important scales. Scale inhibitors have been widely added to prolong the induction time of scales. This study evaluates the methods and previous inhibition models to measure and predict scale formation in the presence of phosphonate and polymer inhibitors under common brine conditions. Turbidity measurement with laser light was used accurately and quickly to measure the induction time, and good reproducibility can be achieved between different sources of inhibitors. By conducting a set of independent inhibition experiments, previous models were evaluated and the demand for model improvement was carefully pointed out. On the basis of these evaluations, new ScaleSoftPizer (SSP) model was proposed by incorporating all available data under various simulated oilfield conditions (4-175°C). The new SSP barite inhibition model was more internally consistent, and the new SSP calcite inhibition model expanded the applicable temperature ranges. The new SSP model was incorporated into SSP 2019. To prove the application of new SSP model, the predicted minimum inhibitor concentrations (MICs) were compared with lab observations and field data, which shows good consistence and improvements. This study improved the prediction of MIC over wide ranges of temperature and inhibitor types, which can significantly reduce the expenses and efforts to solve scale formation problems.","PeriodicalId":10983,"journal":{"name":"Day 1 Mon, April 08, 2019","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Day 1 Mon, April 08, 2019","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2118/193586-MS","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
Scale formation that can hinder continuous oil production is a serious problem in oilfield. Among all common scales, barite and calcite are two of the most important scales. Scale inhibitors have been widely added to prolong the induction time of scales. This study evaluates the methods and previous inhibition models to measure and predict scale formation in the presence of phosphonate and polymer inhibitors under common brine conditions. Turbidity measurement with laser light was used accurately and quickly to measure the induction time, and good reproducibility can be achieved between different sources of inhibitors. By conducting a set of independent inhibition experiments, previous models were evaluated and the demand for model improvement was carefully pointed out. On the basis of these evaluations, new ScaleSoftPizer (SSP) model was proposed by incorporating all available data under various simulated oilfield conditions (4-175°C). The new SSP barite inhibition model was more internally consistent, and the new SSP calcite inhibition model expanded the applicable temperature ranges. The new SSP model was incorporated into SSP 2019. To prove the application of new SSP model, the predicted minimum inhibitor concentrations (MICs) were compared with lab observations and field data, which shows good consistence and improvements. This study improved the prediction of MIC over wide ranges of temperature and inhibitor types, which can significantly reduce the expenses and efforts to solve scale formation problems.