| Citation: | REN Junfan, XUE Liang, NIE Jie, XIAO Lei, LIAO Guangzhi. Research on the main control factors of carbon dioxide flooding and storage based on random forest algorithm[J]. Bulletin of Geological Science and Technology, 2024, 43(3): 147-156. doi: 10.19509/j.cnki.dzkq.tb20230699
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To achieve carbon peak and carbon neutrality goals, carbon dioxide flooding and storage are the main technical means for carbon emission reduction. It is crucial to clarify the main controlling factors of carbon dioxide flooding and storage under reservoir conditions, which provides the basis for realizing the efficient development of carbon dioxide flooding and storage.
In this study, with the widely used PUNQ-S3 case study as the basis, an integrated numerical simulation model of carbon dioxide flooding and geological storage is constructed. It considers the miscible interaction between carbon dioxide and crude oil as well as storage mechanisms, including structural, residual, dissolved, and mineral trapping. By employing the random forest intelligent algorithm, a feature importance analysis of reservoir and production parameters during the carbon dioxide flooding and storage process is carried out. The differences between carbon dioxide flooding and storage at different time scales are considered. A time series-based feature importance analysis method is established, and the main controlling factors in the different carbon dioxide flooding and storage stages are analysed. Through the fluctuation of the evaluation index, the influence of reservoir and production parameters on different stages of carbon dioxide flooding and storage is inferred.
The results show that the time series-based random forest model for carbon dioxide flooding and storage has high accuracy. In the early stage of carbon dioxide flooding and storage, the amount of carbon dioxide structural storage is controlled by the reservoir water saturation, and the amount of dissolved storage is controlled by the salinity of the formation brine; In the middle and later stages of carbon dioxide flooding and storage, the amount of carbon dioxide structural storage is controlled by reservoir permeability, while the amount of dissolved storage is controlled by reservoir permeability and formation water salinity; The residual storage capacity is small in the early stage of carbon dioxide flooding and storage, resulting in unclear main controlling factors.In the later stage of carbon dioxide flooding and storage, it is controlled by reservoir permeability and water saturation; The amount of mineralization storage is controlled by the pH value and the salinity of the formation brine throughout the entire CO2 flooding and storage stage; The amount of carbon dioxide production is controlled by reservoir permeability and water saturation throughout the entire carbon dioxide flooding and storage stage.
The time-series-based random forest algorithm can identify the main controlling factors of different carbon dioxide flooding and storage stages and can provide support for cimproving crude oil recovery and implementing efficient geological storage with carbon dioxide.
| [1] |
秦积舜, 李永亮, 吴德斌, 等. CCUS全球进展与中国对策建议[J]. 油气地质与采收率, 2020, 27(1): 20-28. https://www.cnki.com.cn/Article/CJFDTOTAL-QHDJ202401007.htm
QIN J S, LI Y L, WU D B, et al. CCUS global progress and China's policy suggestions[J]. Petroleum Geology and Recovery Efficiency, 2020, 27(1): 20-28. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-QHDJ202401007.htm
|
| [2] |
张宏伟, 朱海波, 吴欣茹, 等. "双碳"目标下绿色清洁能源技术现状与发展趋势[J]. 石油科学通报, 2023, 8(5): 555-576. doi: 10.3969/j.issn.2096-1693.2023.05.054
ZHANG H W, ZHU H B, WU X R, et al. Status quo and future trends of green and clean energy technology toward "dual carbon" goal[J]. Petroleum Science Bulletin, 2023, 8(5): 555-576. (in Chinese with English abstract) doi: 10.3969/j.issn.2096-1693.2023.05.054
|
| [3] |
李阳. 低渗透油藏CO2驱提高采收率技术进展及展望[J]. 油气地质与采收率, 2020, 27(1): 1-10. https://www.cnki.com.cn/Article/CJFDTOTAL-YQCS202001002.htm
LI Y. Technical advancement and prospect for CO2 flooding enhanced oil recovery in low permeability reservoirs[J]. Petroleum Geology and Recovery Efficiency, 2020, 27(1): 1-10. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-YQCS202001002.htm
|
| [4] |
胡文瑞, 魏漪, 鲍敬伟. 中国低渗透油气藏开发理论与技术进展[J]. 石油勘探与开发, 2018, 45(4): 646-656. https://www.cnki.com.cn/Article/CJFDTOTAL-SKYK201804011.htm
HU W R, WEI Y, BAO J W. Development of the theory and technology for low permeability reservoirs in China[J]. Petroleum Exploration and Development, 2018, 45(4): 646-656. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-SKYK201804011.htm
|
| [5] |
叶航, 刘琦, 彭勃. 基于二氧化碳驱油技术的碳封存潜力评估研究进展[J]. 洁净煤技术, 2021, 27(2): 107-116. https://www.cnki.com.cn/Article/CJFDTOTAL-JJMS202102010.htm
YE H, LIU Q, PENG B. Research progress in evaluation of carbon storage potential based on CO2 flooding technology[J]. Clean Coal Technology, 2021, 27(2): 107-116. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-JJMS202102010.htm
|
| [6] |
王栋. 碳达峰背景下我国石油化工企业参与碳排放权交易市场建设路径分析[J]. 现代管理科学, 2021(5): 3-9. doi: 10.3969/j.issn.1007-368X.2021.05.002
WANG D. Analysis on the participation of China's petrochemical enterprises into the constrction of carbon emission right trading market under the background of carbon peak[J]. Modern Management Science, 2021(5): 3-9. (in Chinese with English abstract) doi: 10.3969/j.issn.1007-368X.2021.05.002
|
| [7] |
唐旭, 李依霖. 迈向零碳未来: 碳排放核算与管理研究综述[J]. 石油科学通报, 2023, 8(4): 522-534. doi: 10.3969/j.issn.2096-1693.2023.04.040
TANG X, LI Y L. Towards a zero-carbon future: A literature review of carbon emission accounting and management[J]. Petroleum Science Bulletin, 2023, 8(4): 522-534. (in Chinese with English abstract) doi: 10.3969/j.issn.2096-1693.2023.04.040
|
| [8] |
CHEN Z, SU Y L, LI L, et al. Characteristics and mechanisms of supercritical CO2 flooding under different factors in low-permeability reservoirs[J]. Petroleum Science, 2022, 19(3): 1174-1184. doi: 10.1016/j.petsci.2022.01.016
|
| [9] |
AL-ABRI A, AMIN R. Phase behaviour, fluid properties and recovery efficiency of immiscible and miscible condensate displacements by SCCO2 injection: Experimental investigation[J]. Transport in Porous Media, 2010, 85(3): 743-756. doi: 10.1007/s11242-010-9589-5
|
| [10] |
李士伦, 汤勇, 侯承希. 注CO2提高采收率技术现状及发展趋势[J]. 油气藏评价与开发, 2019, 9(3): 1-8. doi: 10.3969/j.issn.2095-1426.2019.03.001
LI S L, TANG Y, HOU C X. Present situation and development trend of CO2 injection enhanced oil recovery technology[J]. Reservoir Evaluation and Development, 2019, 9(3): 1-8. (in Chinese with English abstract) doi: 10.3969/j.issn.2095-1426.2019.03.001
|
| [11] |
YANG F, BAI B J, TANG D Z, et al. Characteristics of CO2 sequestration in saline aquifers[J]. Petroleum Science, 2010, 7(1): 83-92. doi: 10.1007/s12182-010-0010-3
|
| [12] |
ALFREDO VISKOVIC V F, VLADIMIR V. Carbon capture and storage[J]. Energy, 2014, 70: 325-337. doi: 10.1016/j.energy.2014.04.007
|
| [13] |
叶斌, 叶为民. 地下咸水层封存CO2的研究现状及展望[J]. 科技资讯, 2012, 10(36): 66-69. doi: 10.3969/j.issn.1672-3791.2012.36.049
YE B, YE W M. Research status and prospect of CO2 sequestration in deep saline aquifer[J]. Science & Technology Information, 2012, 10(36): 66-69. (in Chinese with English abstract) doi: 10.3969/j.issn.1672-3791.2012.36.049
|
| [14] |
高志豪, 赵锐锐, 成建梅. 砂岩含水层CO2封存中考虑盐沉淀反馈作用的数值模拟: 以鄂尔多斯盆地为例[J]. 地质科技通报, 2022, 41(1): 269-277. doi: 10.19509/j.cnki.dzkq.2021.0073
GAO Z H, ZHAO R R, CHENG J M. Numerical simulation of CO2 sequestration in sandstone aquifers with feedback effect of salt precipitation: A case study of Ordos Basin[J]. Bulletin of Geological Science and Technology, 2022, 41(1): 269-277. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.2021.0073
|
| [15] |
BARKER J W, CUYPERS M, HOLDEN L. Quantifying uncertainty in production forecasts: Another look at the PUNQ-S3 problem[J]. SPE Journal, 2001, 6(4): 433-441. doi: 10.2118/74707-PA
|
| [16] |
付晓泰, 王振平, 卢双舫, 等. 天然气在盐溶液中的溶解机理及溶解度方程[J]. 石油学报, 2000, 21(3): 89-94. doi: 10.3321/j.issn:0253-2697.2000.03.018
FU X T, WANG Z P, LU S F, et al. Mechanism of natural gas dissolving in brines and the dissolving equation[J]. Acta Petrolei Sinica, 2000, 21(3): 89-94. (in Chinese with English abstract) doi: 10.3321/j.issn:0253-2697.2000.03.018
|
| [17] |
LAND C S. Calculation of imbibition relative permeability for two-and three-phase flow from rock properties[J]. Society of Petroleum Engineers Journal, 1968, 8(2): 149-156. doi: 10.2118/1942-PA
|
| [18] |
王建秀, 吴远斌, 于海鹏. 二氧化碳封存技术研究进展[J]. 地下空间与工程学报, 2013, 9(1): 81-90. https://www.cnki.com.cn/Article/CJFDTOTAL-BASE201301015.htm
WANG J X, WU Y B, YU H P. Review of the technology for sequestration of carbon dioxide[J]. Chinese Journal of Underground Space and Engineering, 2013, 9(1): 81-90. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-BASE201301015.htm
|
| [19] |
王奕森, 夏树涛. 集成学习之随机森林算法综述[J]. 信息通信技术, 2018, 12(1): 49-55. https://www.cnki.com.cn/Article/CJFDTOTAL-OXXT201801009.htm
WANG Y S, XIA S T. A survey of random forests algorithms[J]. Information and Communications Technologies, 2018, 12(1): 49-55. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-OXXT201801009.htm
|
| [20] |
吕红燕, 冯倩. 随机森林算法研究综述[J]. 河北省科学院学报, 2019, 36(3): 37-41. https://www.cnki.com.cn/Article/CJFDTOTAL-HBKX201903005.htm
LV H Y, FENG Q. A review of random forests algorithm[J]. Journal of the Hebei Academy of Sciences, 2019, 36(3): 37-41. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-HBKX201903005.htm
|
| [21] |
姚登举, 杨静, 詹晓娟. 基于随机森林的特征选择算法[J]. 吉林大学学报(工学版), 2014, 44(1): 137-141. https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY201401024.htm
YAO D J, YANG J, ZHAN X J. Feature selection algorithm based on random forest[J]. Journal of Jilin University (Engineering and Technology Edition), 2014, 44(1): 137-141. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY201401024.htm
|
| [22] |
汪正, 胡顺, 马瑞, 等. 基于随机森林的电容式土壤水分传感器校准研究[J]. 地质科技通报, 2023, 42(5): 249-256. doi: 10.19509/j.cnki.dzkq.2022.0133
WANG Z, HU S, MA R, et al. Calibration of capacitive soil moisture sensor based on random forest[J]. Bulletin of Geological Science and Technology, 2023, 42(5): 249-256. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.2022.0133
|
| [23] |
曹正凤. 随机森林算法优化研究[D]. 北京: 首都经济贸易大学, 2014.
CAO Z F. Study on optimization of random forests algorithm[D]. Beijing: Capital University of Economics and Business, 2014. (in Chinese with English abstract)
|
| [24] |
BREIMAN L. Randomizing outputs to increase prediction accuracy[J]. Machine Learning, 2000, 40(3): 229-242.
|
| [25] |
彭漂. 基于随机森林的变量重要性度量和核密度估计算法研究[D]. 福建厦门: 厦门大学, 2017.
PENG P. Variable importance measure and kernel density estimation based on random forest[D]. Xiamen Fujian: Xiamen University, 2017. (in Chinese with English abstract)
|
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