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 MKSVM-GA, is developed using a hybrid technique of support vector machine (SVM), mixed kernels (MK), 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 (273–473.15 K), pressures (0.1–49.3 MPa), and viscosities (0.139–1.950 mPa·s). Our results show that the proposed model is more applicable than the artificial neural network (ANN) model at this sample size, which is superior to 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.
Skip Nav Destination
Article navigation
April 2019
Research-Article
Estimation of CO2 Diffusivity in Brine by Use of the Genetic Algorithm and Mixed Kernels-Based Support Vector Machine Model
Qihong Feng,
Qihong Feng
School of Petroleum Engineering,
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
Search for other works by this author on:
Ronghao Cui,
Ronghao Cui
School of Petroleum Engineering,
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
e-mail: ronghao.cui1993@gmail.com
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
e-mail: ronghao.cui1993@gmail.com
Search for other works by this author on:
Sen Wang,
Sen Wang
School of Petroleum Engineering,
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
e-mail: fwforest@gmail.com
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
e-mail: fwforest@gmail.com
Search for other works by this author on:
Jin Zhang,
Jin Zhang
School of Petroleum Engineering,
China University of Petroleum (East China),
Qingdao 266580, Shandong,
China University of Petroleum (East China),
Qingdao 266580, Shandong,
China
Search for other works by this author on:
Zhe Jiang
Zhe Jiang
School of Petroleum Engineering,
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
Search for other works by this author on:
Qihong Feng
School of Petroleum Engineering,
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
Ronghao Cui
School of Petroleum Engineering,
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
e-mail: ronghao.cui1993@gmail.com
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
e-mail: ronghao.cui1993@gmail.com
Sen Wang
School of Petroleum Engineering,
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
e-mail: fwforest@gmail.com
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
e-mail: fwforest@gmail.com
Jin Zhang
School of Petroleum Engineering,
China University of Petroleum (East China),
Qingdao 266580, Shandong,
China University of Petroleum (East China),
Qingdao 266580, Shandong,
China
Zhe Jiang
School of Petroleum Engineering,
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
1Corresponding authors.
Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received July 13, 2018; final manuscript received October 6, 2018; published online November 19, 2018. Assoc. Editor: Daoyong (Tony) Yang.
J. Energy Resour. Technol. Apr 2019, 141(4): 041001 (11 pages)
Published Online: November 19, 2018
Article history
Received:
July 13, 2018
Revised:
October 6, 2018
Citation
Feng, Q., Cui, R., Wang, S., Zhang, J., and Jiang, Z. (November 19, 2018). "Estimation of CO2 Diffusivity in Brine by Use of the Genetic Algorithm and Mixed Kernels-Based Support Vector Machine Model." ASME. J. Energy Resour. Technol. April 2019; 141(4): 041001. https://doi.org/10.1115/1.4041724
Download citation file:
Get Email Alerts
Related Articles
Application of Artificial Intelligence Techniques in Prediction of Energetic Performance of a Hybrid System Consisting of an Earth-Air Heat Exchanger and a Building-Integrated Photovoltaic/Thermal System
J. Sol. Energy Eng (October,2021)
A Genetic Algorithm Based Multi-Objective Optimization of Squealer Tip Geometry in Axial Flow Turbines: A Constant Tip Gap Approach
J. Fluids Eng (February,2020)
Predicting Vertical Daylight Illuminance Data From Measured Solar Irradiance: A Machine Learning-Based Luminous Efficacy Approach
J. Sol. Energy Eng (June,2023)
Evaluating the Effectiveness of Machine Learning Technologies in Improving Real-Time Drilling Data Quality
J. Energy Resour. Technol (September,2022)
Related Proceedings Papers
Related Chapters
Performance Validation Using Several Statistical Learning Theory Paradigms for Mammogram Screen Film and Clinical Data Features
Intelligent Engineering Systems Through Artificial Neural Networks, Volume 17
Cutting Tool Wear Monitoring Applying Support Vector Machines and Genetic Algorithms
International Conference on Advanced Computer Theory and Engineering (ICACTE 2009)
A Study on Temperature Prediction for Forced Ventilated Greenhouses Based on LE-SVM
International Conference on Green Buildings and Optimization Design (GBOD 2012)