Physics-Based Proxy Modeling of CO2 Sequestration in Deep Saline Aquifers
نویسندگان
چکیده
The geological sequestration of CO2 in deep saline aquifers is one the most effective strategies to reduce greenhouse emissions from stationary point sources CO2. However, it a complex task quantify storage capacity an aquifer as function various characteristics and operational decisions. This study applies physics-based proxy modeling by using multiple machine learning (ML) models predict trapping scenarios aquifer. A compositional reservoir simulator was used develop base case model simulate mechanisms (i.e., residual, solubility, mineral trapping) for 275 years following 25-year injection period An expansive dataset comprising 19,800 data points generated varying several key decision parameters iterations model. develop, train, validate four robust ML models—multilayer perceptron (MLP), random forest (RF), support vector regression (SVR), extreme gradient boosting (XGB). We analyzed sequestered mechanisms. Based on statistical accuracy results, with coefficient determination (R2) value over 0.999, both RF XGB had excellent predictive ability cross-validated dataset. proposed has best performance prediction R2 values 0.99988, 0.99968, 0.99985 residual trapping, mineralized dissolution mechanisms, respectively. Furthermore, feature importance analysis algorithm identified monitoring time critical dictating changes performance, while relative permeability hysteresis, permeability, porosity were some parameters. For XGB, however, uncertain geologic varied based different findings this show that smart can be tool estimate similar characteristics.
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ژورنال
عنوان ژورنال: Energies
سال: 2022
ISSN: ['1996-1073']
DOI: https://doi.org/10.3390/en15124350