0
research-article

An Improved Intelligent Model for Fast Estimation of CO2 Diffusivity in Brine

[+] Author and Article Information
Qihong Feng

School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, Shandong 266580, People's Republic of China
fengqihong.upc@gmail.com

Ronghao Cui

School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, Shandong 266580, People's Republic of China
ronghao.cui1993@gmail.com

Sen Wang

School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, Shandong 266580, People's Republic of China
fwforest@gmail.com

Jin Zhang

School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, Shandong 266580, People's Republic of China
Jinzhang405@126.com

Zhe Jiang

School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, Shandong 266580, People's Republic of China
jiangzheupc@163.com

1Corresponding author.

ASME doi:10.1115/1.4041724 History: Received July 13, 2018; Revised October 06, 2018

Abstract

Diffusion coefficient of carbon dioxide (CO2), a significant parameter describing the mass transfer process, exerts a profound influence on the safety of CO2 storage in depleted reservoirs, saline aquifers, and marine ecosystems. However, experimental determination of diffusion coefficient in CO2-brine system is time-consuming and complex because the procedure requires sophisticated laboratory equipment and reasonable interpretation methods. To facilitate the acquisition of more accurate values, an intelligent model, termed SVM-GA, is developed using a hybrid technique of support vector machine (SVM) and genetic algorithm (GA). Confirmed by the statistical evaluation indicators, our proposed model exhibits excellent performance with high accuracy and strong robustness in a wide range of temperatures (273-473.15K), pressures (0.1-49.3MPa), and viscosities (0.1388-1.95 mPa┬Ěs). Our results show that the proposed model is superior to the artificial neural network (ANN) model and four commonly-used traditional empirical correlations. The technique presented in this study can provide a fast and precise prediction of CO2 diffusivity in brine at reservoir conditions for the engineering design and the technical risk assessment during the process of CO2 injection.

Copyright (c) 2018 by ASME
Your Session has timed out. Please sign back in to continue.

References

Figures

Tables

Errata

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In