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首页> 外文期刊>Journal of Energy Resources Technology >Estimation of CO_2 Diffusivity in Brine by Use of the Genetic Algorithm and Mixed Kernels-Based Support Vector Machine Model
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Estimation of CO_2 Diffusivity in Brine by Use of the Genetic Algorithm and Mixed Kernels-Based Support Vector Machine Model

机译：遗传算法和基于混合核的支持向量机模型估算卤水中CO_2的扩散率

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摘要

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.
机译：二氧化碳（CO2）的扩散系数是描述传质过程的重要参数，对枯竭的水库，盐水层和海洋生态系统中CO2储存的安全性产生深远的影响。然而，由于该过程需要复杂的实验室设备和合理的解释方法，因此实验确定CO2-盐水系统中扩散系数的过程既费时又复杂。为了促进获取更准确的值，使用支持向量机（SVM），混合内核（MK）和遗传算法（GA）的混合技术开发了称为MKSVM-GA的智能模型。经统计评估指标证实，我们提出的模型在广泛的温度（273-473.15 K），压力（0.1-49.3 MPa）和粘度（0.139-1.950 mPa.s）范围内均具有出色的性能，高精度和强大的鲁棒性。 。我们的结果表明，在此样本量下，提出的模型比人工神经网络（ANN）模型更适用，它优于四种常用的传统经验相关性。这项研究中提出的技术可以为储层条件下盐水中CO2扩散率提供快速而精确的预测，以便在CO2注入过程中进行工程设计和技术风险评估。

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