A Review of Mineral and Rock Wettability Changes Induced by Reaction: Implications for CO2 Storage in Saline Reservoirs

Chen, Ting; Song, Laiming; Zhang, Xueying; Yang, Y.B.; Fan, Huifang; Pan, Bin

doi:10.3390/en16083484

Cited by 5 publications

(1 citation statement)

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“…But extracting residual oil from tight reservoirs in complex geological formations remains a significant challenge [1]. Numerous studies have demonstrated the significant impact of CO 2 flooding on enhancing oil recovery (EOR) in low-permeability reservoirs [2][3][4][5]. CO 2 flooding holds the potential for achieving high efficiency in extracting oil from reservoirs.…”

Section: Introductionmentioning

confidence: 99%

Prediction of ORF for Optimized CO2 Flooding in Fractured Tight Oil Reservoirs via Machine Learning

Yue,

Dai,

Liao

et al. 2024

Energies

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Tight reservoirs characterized by complex physical properties pose significant challenges for extraction. CO2 flooding, as an EOR technique, offers both economic and environmental advantages. Accurate prediction of recovery rate plays a crucial role in the development of tight oil and gas reservoirs. But the recovery rate is influenced by a complex array of factors. Traditional methods are time-consuming and costly and cannot predict the recovery rate quickly and accurately, necessitating advanced multi-factor analysis-based prediction models. This study uses machine learning models to rapidly predict the recovery of CO2 flooding for tight oil reservoir development, establishes a numerical model for CO2 flooding for low-permeability tight reservoir development based on actual blocks, studies the effects of reservoir parameters, horizontal well parameters, and injection-production parameters on CO2 flooding recovery rate, and constructs a prediction model based on machine learning for the recovery. Using simulated datasets, three models, random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM), were trained and tested for accuracy evaluation. Different levels of noise were added to the dataset and denoised, and the effects of data noise and denoising techniques on oil recovery factor prediction were studied. The results showed that the LightGBM model was superior to other models, with R2 values of 0.995, 0.961, 0.921, and 0.877 for predicting EOR for the original dataset, 5% noise dataset, 10% noise dataset, and 15% noise dataset, respectively. Finally, based on the optimized model, the key control factors for CO2 flooding for tight oil reservoirs to enhance oil recovery were analyzed. The novelty of this study is the development of a machine-learning-based method that can provide accurate and cost-effective ORF predictions for CO2 flooding for tight oil reservoir development, optimize the development process in a timely manner, significantly reduce the required costs, and make it a more feasible carbon utilization and EOR strategy.

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