德国岩土工程研究所Taherdangkoo, Reza团队报道了甲烷、乙烷和丙烷在纯水和电解质溶液中溶解度的机器学习预测，及对杂散气体迁移建模的影响。这一研究成果于2024年3月22日发表在《地球化学学报》上。

该研究团队编制了一个数据库，其中包含了2129个实验数据，涉及甲烷、乙烷和丙烷在纯水和各种在工作温度和压力范围内的电解质溶液中的溶解度。研究采用两种机器学习算法，即回归树(RT)和贝叶斯优化算法(BO)调整的增强回归树(BRT)，被用于确定气体的溶解度。

研究人员将预测结果与实验数据以及四个成熟的热力学模型进行了比较。分析表明，BRT-BO具有足够的精度，预测值与热力学模型的预测值吻合良好。实验值与预测值的决定系数(R²)为0.99，均方误差(MSE)为9.97×10^-8。杠杆统计方法进一步证实了研究所开发的模型的有效性。

据了解，水力压裂是一种有效的非常规页岩气藏和致密气藏油气开采技术。水力压裂的一个潜在风险是杂散气体从地下深处向上运移到浅层含水层。杂散气体会溶解在地下水中，诱发化学和生物反应，对地下水质量产生负面影响，并增加大气排放。了解轻烃在水环境中的溶解度对地下流动和输运的数值模拟至关重要。

附：英文原文

Title: Machine learning prediction of methane, ethane, and propane solubility in pure water and electrolyte solutions: Implications for stray gas migration modeling

Author: Kooti, Ghazal, Taherdangkoo, Reza, Chen, Chaofan, Sergeev, Nikita, Doulati Ardejani, Faramarz, Meng, Tao, Butscher, Christoph

Issue&Volume: 2024-03-22

Abstract: Hydraulic fracturing is an effective technology for hydrocarbon extraction from unconventional shale and tight gas reservoirs. A potential risk of hydraulic fracturing is the upward migration of stray gas from the deep subsurface to shallow aquifers. The stray gas can dissolve in groundwater leading to chemical and biological reactions, which could negatively affect groundwater quality and contribute to atmospheric emissions. The knowledge of light hydrocarbon solubility in the aqueous environment is essential for the numerical modelling of flow and transport in the subsurface. Herein, we compiled a database containing 2129 experimental data of methane, ethane, and propane solubility in pure water and various electrolyte solutions over wide ranges of operating temperature and pressure. Two machine learning algorithms, namely regression tree (RT) and boosted regression tree (BRT) tuned with a Bayesian optimization algorithm (BO) were employed to determine the solubility of gases. The predictions were compared with the experimental data as well as four well-established thermodynamic models. Our analysis shows that the BRT-BO is sufficiently accurate, and the predicted values agree well with those obtained from the thermodynamic models. The coefficient of determination (R²) between experimental and predicted values is 0.99 and the mean squared error (MSE) is 9.97×10^-8. The leverage statistical approach further confirmed the validity of the model developed.

DOI: 10.1007/s11631-024-00680-8

Source: https://link.springer.com/article/10.1007/s11631-024-00680-8