首页> 外文期刊>Energy & fuels >Accurate Determination of the CO_2-Brine Interfacial Tension Using Graphical Alternating Conditional Expectation
【24h】

Accurate Determination of the CO_2-Brine Interfacial Tension Using Graphical Alternating Conditional Expectation

机译:使用图形交替条件期望值准确确定CO_2-盐水界面张力

获取原文
获取原文并翻译 | 示例
           

摘要

A newly developed CO_2-brine interfacial tension (IFT) correlation based on the alternating condition expectation (ACE) algorithm has been successfully proposed to more accurately estimate the CO_2-brine IFT for a wide range of reservoir pressure, temperature, formation water salinity and injected gas composition. The new CO_2-brine correlation is expressed as a function of reservoir pressure, temperature, monovalent cation molalities (Na~+ and K~+), bivalent cation molalities (Ca~2+ and Mg~2+), N_2 mole fraction and CH_4 mole fraction in injected gas. This prediction model is originated from a CO_2-brine IFT database from the literature that covers 1609 CO_2-brine IFT data for pure and impure CO_2 streams. To test the validity and accuracy of the developed CO_2-brine IFT model, the entire dataset was divided into two groups: a training database consisting of 805 points and a testing dataset consisting of 804 points, which was arbitrarily selected from the total database. To further examine its predicted capacity, the new CO_2-brine IFT correlation is validated with four commonly used pure CO_2-pure water IFT correlations in the literature, it is found that the new CO_2-brine IFT correlation provides the comprehensive and accurate reproduction of the literature pure CO_2-pure water IFT data with an average absolute relative error (% AARE) of 12.45% and standard deviation (% SD) of 18.57%, respectively. In addition, the newly developed CO_2-brine IFT correlation results in the accurate prediction of the CO_2-brine IFT with a % AARE of 10.19% and % SD of 13.16%, respectively, compared to two CO_2-brine IFT correlations. Furthermore, sensitivity analysis was performed based on the Spearman correlation coefficients (rank correlation coefficients). The major factor influenced on the CO_2-brine IFT is reservoir pressure, which has a major negative impact on the CO_2-brine IFT. In contrast, the effects of CO_2 impurities and salt components in the water on the CO_2-brine IFT are in the following order in terms of their positive impact: bivalent cation molalities (Ca~2+ and Mg~2+), CH_4, N_2, and monovalent cation molalities (Na~+ and K~+).
机译:已经成功提出了一种新的基于交替条件期望(ACE)算法的CO_2盐水界面张力(IFT)相关性,可以更准确地估算在广泛的储层压力,温度,地层水盐度和注入范围内的CO_2盐水IFT。气体成分。新的CO_2-盐水相关性表示为储层压力,温度,一价阳离子摩尔浓度(Na〜+和K〜+),二价阳离子摩尔浓度(Ca〜2 +和Mg〜2 +),N_2摩尔分数和CH_4的函数注入气体中的摩尔分数。该预测模型源自文献中的CO_2-盐水IFT数据库,该数据库涵盖了纯净和不纯CO_2流的1609个CO_2-盐水IFT数据。为了测试所开发的CO_2盐水IFT模型的有效性和准确性,将整个数据集分为两组:一个训练数据库(由805个点组成)和一个测试数据集(由804个点组成),可以从总数据库中任意选择。为了进一步检查其预测能力,新的CO_2-盐水IFT相关性通过文献中四种常用的纯CO_2-纯水IFT相关性进行了验证,发现新的CO_2-盐水IFT相关性提供了全面而准确的繁殖。文献中纯CO_2纯水IFT数据的平均绝对相对误差(%AARE)为12.45%,标准偏差(%SD)为18.57%。此外,新开发的CO_2-盐水IFT相关性可以准确预测CO_2-盐水IFT的准确预测,与两种CO_2-盐水IFT相关性相比,%AARE分别为10.19%和%SD。另外,基于Spearman相关系数(秩相关系数)进行灵敏度分析。影响CO_2盐水IFT的主要因素是储层压力,这对CO_2盐水IFT具有重大的负面影响。相比之下,就其正面影响而言,水中的CO_2杂质和盐分成分对CO_2-盐水IFT的影响依次为:二价阳离子摩尔浓度(Ca〜2 +和Mg〜2 +),CH_4,N_2和一价阳离子摩尔数(Na〜+和K〜+)。

著录项

  • 来源
    《Energy & fuels》 |2014年第janaafeba期|624-635|共12页
  • 作者单位

    College of Petroleum Engineering, China University of Petroleum, Qingdao 266580, People's Republic of China;

    College of Petroleum Engineering, China University of Petroleum, Qingdao 266580, People's Republic of China;

    College of Petroleum Engineering, China University of Petroleum, Qingdao 266580, People's Republic of China;

    College of Energy Resources, Chengdu University of Technology, Chengdu 610059, People's Republic of China;

    College of Petroleum Engineering, China University of Petroleum, Qingdao 266580, People's Republic of China;

    College of Petroleum Engineering, China University of Petroleum, Qingdao 266580, People's Republic of China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号