Accurate Determination of the CO2–Brine Interfacial Tension Using Graphical Alternating Conditional Expectation

A newly
developed CO2–brine interfacial tension
(IFT) correlation based on the alternating condition expectation (ACE)
algorithm has been successfully proposed to more accurately estimate
the CO2–brine IFT for a wide range of reservoir
pressure, temperature, formation water salinity and injected gas composition.
The new CO2–brine correlation is expressed as a
function of reservoir pressure, temperature, monovalent cation molalities
(Na+ and K+), bivalent cation molalities (Ca2+ and Mg2+), N2 mole fraction and CH4 mole fraction in injected gas. This prediction model is originated
from a CO2–brine IFT database from the literature
that covers 1609 CO2–brine IFT data for pure and
impure CO2 streams. To test the validity and accuracy of
the developed CO2–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 CO2–brine IFT correlation is validated
with four commonly used pure CO2–pure water IFT
correlations in the literature, it is found that the new CO2–brine IFT correlation provides the comprehensive and accurate
reproduction of the literature pure CO2–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 CO2–brine IFT correlation results
in the accurate prediction of the CO2–brine IFT
with a % AARE of 10.19% and % SD of 13.16%, respectively, compared
to two CO2–brine IFT correlations. Furthermore,
sensitivity analysis was performed based on the Spearman correlation
coefficients (rank correlation coefficients). The major factor influenced
on the CO2–brine IFT is reservoir pressure, which
has a major negative impact on the CO2–brine IFT.
In contrast, the effects of CO2 impurities and salt components
in the water on the CO2–brine IFT are in the following
order in terms of their positive impact: bivalent cation molalities
(Ca2+ and Mg2+), CH4, N2, and monovalent cation molalities (Na+ and K+).

基于交替条件期望（ACE）算法所开发的新型二氧化碳-盐水界面张力（IFT）相关性，已成功提出，以更精确地估算广泛压力范围、温度、地层水盐度和注入气体成分下的二氧化碳-盐水IFT。该新型二氧化碳-盐水相关性以储层压力、温度、单价阳离子摩尔浓度（Na+和K+）、二价阳离子摩尔浓度（Ca2+和Mg2+）、氮气摩尔分数和注入气体中的甲烷摩尔分数作为函数表达。此预测模型源自文献中的二氧化碳-盐水IFT数据库，涵盖了1609个纯二氧化碳和非纯二氧化碳流体的二氧化碳-盐水IFT数据。为验证所开发的二氧化碳-盐水IFT模型的有效性和准确性，整个数据集被分为两组：一个包含805个点的训练数据库和一个包含804个点的测试数据集，后者从总体数据库中任意选取。为进一步检验其预测能力，新的二氧化碳-盐水IFT相关性通过与文献中四种常用的纯二氧化碳-纯水IFT相关性进行验证，发现新的二氧化碳-盐水IFT相关性能够综合准确地再现文献中的纯二氧化碳-纯水IFT数据，平均绝对相对误差（% AARE）为12.45%，标准差（% SD）为18.57%。此外，与两种二氧化碳-盐水IFT相关性相比，新开发的二氧化碳-盐水IFT相关性在预测二氧化碳-盐水IFT时，平均绝对相对误差（% AARE）为10.19%，标准差（% SD）为13.16%。此外，基于Spearman相关系数（秩相关系数）进行了敏感性分析。影响二氧化碳-盐水IFT的主要因素是储层压力，它对二氧化碳-盐水IFT有显著的负影响。相比之下，二氧化碳杂质和水中盐分成分对二氧化碳-盐水IFT的正影响程度依次为：二价阳离子摩尔浓度（Ca2+和Mg2+）、甲烷、氮气和单价阳离子摩尔浓度（Na+和K+）。