Machine-Learning-Based Approach to Optimize CO 2 -WAG Flooding in Low Permeability Oil Reservoirs

Ming Gao (), Zhaoxia Liu, Shihao Qian, Wanlu Liu, Weirong Li, Hengfei Yin and Jinhong Cao
Additional contact information
Ming Gao: PetroChina Research Institute of Petroleum Exploration & Development, Beijing 100083, China
Zhaoxia Liu: PetroChina Research Institute of Petroleum Exploration & Development, Beijing 100083, China
Shihao Qian: Department of Petroleum Engineering, Xi’an Shiyou University, Xi’an 710065, China
Wanlu Liu: PetroChina Research Institute of Petroleum Exploration & Development, Beijing 100083, China
Weirong Li: Department of Petroleum Engineering, Xi’an Shiyou University, Xi’an 710065, China
Hengfei Yin: PetroChina Research Institute of Petroleum Exploration & Development, Beijing 100083, China
Jinhong Cao: PetroChina Research Institute of Petroleum Exploration & Development, Beijing 100083, China

Energies, 2023, vol. 16, issue 17, 1-21

Abstract: One of the main applications of carbon capture, utilization, and storage (CCUS) technology in the industry is carbon-dioxide-enhanced oil recovery (CO 2 -EOR). However, accurately and rapidly assessing their application potential remains a major challenge. In this study, a numerical model of the CO 2 -WAG technique was developed using the reservoir numerical simulation software CMG (Version 2021), which is widely used in the field of reservoir engineering. Then, 10,000 different reservoir models were randomly generated using the Monte Carlo method for numerical simulations, with each having different formation physical parameters, fluid parameters, initial conditions, and injection and production parameters. Among them, 70% were used as the training set and 30% as the test set. A comprehensive analysis was conducted using eight different machine learning regression methods to train and evaluate the dataset. After evaluation, the XGBoost algorithm emerged as the top-performing method and was selected as the optimal approach for the prediction and optimization. By integrating the production prediction model with a particle swarm optimizer (PSO), a workflow for optimizing the CO 2 -EOR parameters was developed. This process enables the rapid optimization of the CO 2 -EOR parameters and the prediction of the production for each period based on cumulative production under different geological conditions. The proposed XGBoost-PSO proxy model accurately, reliably, and efficiently predicts production, thereby making it an important tool for optimizing CO 2 -EOR design.

Keywords: CO 2 -EOR; XGBoost regression; PSO; parameter optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/17/6149/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/17/6149/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:17:p:6149-:d:1223989

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().