油气藏评价与开发 >
2024 , Vol. 14 >Issue 1: 35 - 41
DOI: https://doi.org/10.13809/j.cnki.cn32-1825/te.2024.01.005
咸水层中CO2溶解性能预测方法优选
收稿日期: 2023-04-03
网络出版日期: 2024-03-05
基金资助
重庆市基础研究与前沿探索(面上项目)“基于微观接触力学的页岩气藏远场分支裂缝自支撑机理及导流能力评价”(CSTB2022NSCQ-MSX1135);重庆市教委科学技术研究项目(重点项目)“页岩气藏次级裂缝自支撑作用及其影响因素研究”(KJZD-K202201204);三峡库区地质灾害教育部重点实验室（三峡大学）开放基金“基于压力补偿的库区欠压水层CO2埋存潜力研究”(2022KDZ04);重庆三峡学院研究生科研创新项目“复合地基承载力分析及沉降预测”(YJSKY22061)
Optimal prediction method for CO2 solubility in saline aquifers
Received date: 2023-04-03
Online published: 2024-03-05
CO2在咸水层中的溶解性能是估算CO2溶解埋存量的重要参数。为了快速经济地评价分析CO2在咸水层中溶解性能，基于目前不同温度、压强、矿化度下CO2在水中溶解性能数据，开展灰色GM（1，1）模型预测，分析预测相对误差，应用马尔科夫理论，划分状态区间并构造状态转移概率矩阵，对预测结果进行修正，提出基于灰色马尔科夫理论的CO2在咸水层中溶解性能预测模型。结果表明：灰色马尔科夫模型预测值与实测值的平均相对误差分别为1.52%、17.73%、0.21%、3.97%，灰色GM（1，1）模型预测结果的平均相对误差分别为2.37%、19.29%、3.62%、3.94%，灰色马尔科夫预测值与相应实测数据更吻合，模型预测性能较好，可为CO2在地下咸水中的溶解度预测提供1种新方法。
董利飞 , 董文卓 , 张旗 , 钟品志 , 王苗 , 余波 , 韦海宇 , 杨超 . 咸水层中CO2溶解性能预测方法优选[J]. 油气藏评价与开发, 2024 , 14(1) : 35 -41 . DOI: 10.13809/j.cnki.cn32-1825/te.2024.01.005
CO2 solubility in saline aquifer is an important parameter for estimating the volume of CO2 that can be dissolved and stored underground. To rapidly and economically evaluate and analyze the solubility of CO2 in saline aquifers, a study was conducted using grey GM（1,1） modeling based on existing data of CO2 solubility in water under various temperatures, pressures, and salinities. By using Markov theory, the state interval was divided, the state transition probability matrix was constructed, and the prediction results were revised. A prediction model of CO2 solubility in saline aquifer based on grey Markov theory was proposed. The results showed that the average relative errors between the predicted values of the grey Markov theory and the measured values were 1.52%、17.73%、0.21% and 3.97%, respectively. The average relative errors between the prediction results of the gray GM（1,1） model were 2.37%、19.29%、3.62% and 3.94%, respectively. The predicted values of the grey Markov model were more consistent with the measured data, and the prediction performance of the model was better, so as to provide a new method for predicting the solubility of CO2 in underground salt water.
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