Accurate Determination of the CO2–Brine Interfacial Tension Using Graphical Alternating Conditional Expectation
datasetposted on 17.12.2015 by Zhaomin Li, Shuhua Wang, Songyan Li, Wei Liu, Binfei Li, Qi-Chao Lv
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
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+).