Petroleum Reservoir Evaluation and Development >
2025 , Vol. 15 >Issue 3: 434 - 442
DOI: https://doi.org/10.13809/j.cnki.cn32-1825/te.2025.03.010
Study on well logging reservoir fluid evaluation method based on 2D cloud model: A case study of Kuqa Depression, Tarim Basin
Received date: 2024-09-11
Online published: 2025-05-28
Accurate interpretation of well logging data is crucial for the evaluation of reservoir fluid properties in oil and gas exploration. Conventional well logging methods rely on petrophysical models that correlate parameters such as porosity, permeability, and oil and gas saturation with reservoir fluid properties to achieve reservoir classification. However, complex geological conditions often lead to issues such as anomalies, multi-factor coupling, and ambiguous fluid boundaries in well logging data. These challenges limit the adaptability of conventional methods and bring uncertainties in interpretation results. To improve the accuracy of reservoir fluid evaluation, this study incorporated cloud model theory into conventional well logging evaluation and proposed an evaluation method for reservoir fluid based on a 2D cloud model. The method selected porosity and gas saturation as key logging parameters and utilized cloud models to process the fuzziness and randomness in well logging data, thereby establishing a mathematical model for reservoir fluid classification. First, a 2D cloud model for well logging evaluation was derived based on cloud model theory, with clarified geophysical significance assigned to its mathematical parameters (expectation, entropy, and hyper-entropy). 2D cloud diagrams of the reservoir were generated using a cloud generator. Subsequently, similarity analysis was applied to quantitatively classify reservoir types, enhancing interpretation accuracy. To validate the effectiveness of this method, well logging data from the Kuqa Depression in the Tarim Basin were used for application analysis, with results compared with those obtained from conventional methods, cloud model evaluation, and well testing. The results showed that the proposed method accurately characterized reservoir fluid properties in complex reservoirs. Compared with conventional methods, the 2D cloud model not only provided qualitative classification of reservoir types but also quantified uncertainties in fluid properties, thus improving the stability and reliability of evaluation results. The findings indicate that the reservoir fluid evaluation method based on 2D cloud model effectively reflects reservoir fluid characteristics and exhibits strong adaptability in complex reservoir environments. The final evaluation results demonstrate strong consistency with well testing results, verifying the method’s feasibility and effectiveness. As a valuable supplement to conventional well logging interpretation, this method provides a new approach for improving the accuracy of well logging data interpretation and optimizing fluid property identification in complex reservoirs.
Key words: Kuche Sag; 2D cloud model; evaluation criteria; well logging evaluation; fuzziness
WANG Shuli , WANG Jinguo , ZHANG Chengsen , ZHANG Zhean , Kaysar PARHAT , LIU Longcheng . Study on well logging reservoir fluid evaluation method based on 2D cloud model: A case study of Kuqa Depression, Tarim Basin[J]. Petroleum Reservoir Evaluation and Development, 2025 , 15(3) : 434 -442 . DOI: 10.13809/j.cnki.cn32-1825/te.2025.03.010
1 | 唐磊, 王建峰, 曹敬华, 等. 塔里木盆地顺北地区超深断溶体油藏地质工程一体化模式探索[J]. 油气藏评价与开发, 2021, 11(3): 329-339. |
TANG Lei, WANG Jianfeng, CAO Jinghua, et al. Geology-engineering integration mode of ultra-deep fault-karst reservoir in Shunbei area, Tarim Basin[J]. Petroleum Reservoir Evaluation and Development, 2021, 11(3): 329-339. | |
2 | 潘保芝, 房春慧, 郭宇航, 等. 基于岩石物理转换模型的苏里格致密砂岩储层测井评价与产能预测[J]. 地球物理学报, 2018, 61(12): 5115-5124. |
PAN Baozhi, FANG Chunhui, GUO Yuhang, et al. Logging evaluation and productivity prediction of Sulige tight sandstone reservoirs based on petrophysics transformation models[J]. Chinese Journal of Geophysics, 2018, 61(12): 5115-5124. | |
3 | 尹成芳, 刘炜辰, 杨虹, 等. 国外测井技术发展现状与趋势[J]. 世界石油工业, 2024, 31(6): 77-87. |
YIN Chengfang, LIU Weichen, YANG Hong, et al. Advances in foreign well logging technology development[J]. World Petroleum Industry, 2024, 31(6): 77-87. | |
4 | 李震, 张金海, 李桂山, 等. 低渗透裂缝性油藏测井侧向剩余油挖潜措施研究: 以长庆A油田为例[J]. 石油地质与工程, 2024, 38(1): 90-94. |
LI Zhen, ZHANG Jinhai, LI Guishan, et al. Potential tapping measures for lateral remaining oil in low permeability fractured reservoir logging: A case study of Changqing A oilfield[J]. Petroleum Geology and Engineering, 2024, 38(1): 90-94. | |
5 | LAI J, WANG G W, FAN Q X, et al. Toward the scientific interpretation of geophysical well logs: Typical misunderstandings and countermeasures[J]. Surveys in Geophysics, 2023, 44(2): 463-494. |
6 | 王岩泉, 边伟华, 刘宝鸿, 等. 辽河盆地火成岩储层评价标准与有效储层物性下限[J]. 中国石油大学学报(自然科学版), 2016, 40(2): 13-22. |
WANG Yanquan, BIAN Weihua, LIU Baohong, et al. Evaluation criterion and cut-off value of igneous rock reservoirs in Liaohe Basin[J]. Journal of China University of Petroleum(Edition of Natural Science), 2016, 40(2): 13-22. | |
7 | 刘堂宴, 郑勇, 傅容珊, 等. 提高测井建模精度的一种方法[J]. 石油地球物理勘探, 2002, 37(1): 44-47. |
LIU Tangyan, ZHENG Yong, FU Rongshan, et al. A method for improving precision of log model-building[J]. Oil Geophysical Prospecting, 2002, 37(1): 44-47. | |
8 | 李德毅, 孟海军, 史雪梅. 隶属云和隶属云发生器[J]. 计算机研究与发展, 1995, 32(6): 15-20. |
LI Deyi, MENG Haijun, SHI Xuemei. Membership clouds and membership cloud generators[J]. Journal of Computer Research and Development, 1995, 32(6): 15-20. | |
9 | 杨朝晖, 李德毅. 二维云模型及其在预测中的应用[J]. 计算机学报, 1998, 21(11): 961-969. |
YANG Zhaohui, LI Deyi. Planar model and its application in prediction[J]. Chinese Journal of Computers, 1998, 21(11): 961-969. | |
10 | 王景春, 张法. 基于熵权二维云模型的深基坑施工风险评价[J]. 安全与环境学报, 2018, 18(3): 849-853. |
WANG Jingchun, ZHANG Fa. Risk assessment of the deep foundation pit based on the entropy weight and 2-dimensional cloud model[J]. Journal of Safety and Environment, 2018, 18(3): 849-853. | |
11 | 豆靖涛, 郭玉娟. 典型产业集聚区地下水污染风险评价与分区[J]. 能源与环保, 2023, 45(6): 203-208. |
DOU Jingtao, GUO Yujuan. Risk assessment and zoning of groundwater pollution in typical industrial clusters[J]. China Energy and Environmental Protection, 2023, 45(6): 203-208. | |
12 | 霍小森, 舒鑫宇, 焦柳丹. 突发公共卫生事件下城市轨道交通系统适灾韧性评估[J]. 都市快轨交通, 2023, 36(5): 152-158. |
HUO Xiaosen, SHU Xinyu, JIAO Liudan. Disaster resilience assessment of urban rail transit systems under public health emergencies[J]. Urban Rapid Rail Transit, 2023, 36(5): 152-158. | |
13 | LI Q F, LU L F, MA Q. Construction risk evaluation of poor geological channels based on cloud model-improved AHP-matter-element theory[J]. Sustainability, 2021, 13(17): 9632. |
14 | 赵晨程, 高玉琴, 刘钺, 等. 基于云模型的生态河道建设评价[J]. 水资源保护, 2022, 38(2): 183-189. |
ZHAO Chencheng, GAO Yuqin, LIU Yue, et al. Evaluation of ecological river construction based on cloud model[J]. Water Resources Protection, 2022, 38(2): 183-189. | |
15 | 张亚光, 李玉龙. 云模型在测井曲线分层中的应用[J]. 计算机与数字工程, 2014, 42(9): 1613-1616. |
ZHANG Yaguang, LI Yulong. Application of cloud model in logging curve hierarchical[J]. Computer & Digital Engineering, 2014, 42(9): 1613-1616. | |
16 | 丁恺, 赵福海, 高莲凤, 等. 基于变分模态分解的营四段厚层砂砾岩地层细分层序[J]. 吉林大学学报(地球科学版), 2024, 54(4): 1406-1418. |
DING Kai, ZHAO Fuhai, GAO Lianfeng, et al. Subdivision sequence of thick glutenite strata in the fourth member of Yingcheng formation based on variational mode decomposition[J]. Journal of Jilin University(Earth Science Edition), 2024, 54(4): 1406-1418. | |
17 | 吴泽兵, 谷亚冰, 姜雯, 等. 基于遗传优化算法的井底钻压智能预测模型[J]. 石油钻采工艺, 2023, 45(2): 151-159. |
WU Zebing, GU Yabing, JIANG Wen, et al. Intelligent prediction models of downhole weight on bit based on genetic optimization algorithm[J]. Oil Drilling & Production Technology, 2023, 45(2): 151-159. | |
18 | 刘桂花, 宋承祥, 刘弘. 云发生器的软件实现[J]. 计算机应用研究, 2007, 24(1): 46-48. |
LIU Guihua, SONG Chengxiang, LIU Hong. Software implementation of cloud generators[J]. Application Research of Computers, 2007, 24(1): 46-48. | |
19 | 吕辉军, 王晔, 李德毅, 等. 逆向云在定性评价中的应用[J]. 计算机学报, 2003, 26(8): 1009-1014. |
LU Huijun, WANG Ye, LI Deyi, et al. The application of backward cloud in qualitative evaluation[J]. Chinese Journal of Computers, 2003, 26(8): 1009-1014. | |
20 | 刘常昱, 冯芒, 戴晓军, 等. 基于云X信息的逆向云新算法[J]. 系统仿真学报, 2004, 16(11): 2417-2420. |
LIU Changyu, FENG Mang, DAI Xiaojun, et al. A new algorithm of backward cloud[J]. Journal of System Simulation, 2004, 16(11): 2417-2420. | |
21 | 王惠君, 卢双舫, 乔露, 等. 南川页岩气地质工程一体化优化中的参数敏感性分析[J]. 地球科学, 2023, 48(1): 267-278. |
WANG Huijun, LU Shuangfang, QIAO Lu, et al. Parameter sensitivity analysis in geology-engineering integration optimization for shale gas in Nanchuan Block[J]. Earth Science, 2023, 48(1): 267-278. | |
22 | 王珂, 张荣虎, 赵继龙, 等. 塔里木盆地库车坳陷克拉苏构造带走滑作用对构造裂缝的影响[J]. 天然气地球科学, 2023, 34(8): 1316-1327. |
WANG Ke, ZHANG Ronghu, ZHAO Jilong, et al. Implication of strike-slipping to tectonic fractures in the Kelasu structural belt, Kuqa Depression, Tarim Basin[J]. Natural Gas Geoscience, 2023, 34(8): 1316-1327. | |
23 | 王轲, 慈兴华, 杜焕福, 等. 塔里木盆地顺北碳酸盐岩元素地球化学特征与油气富集机制[J]. 世界石油工业, 2024, 31(2): 55-64. |
WANG Ke, Xinghua CI, DU Huanfu, et al. Geochemical characteristics and oil & gas enrichment mechanisms of carbonate rocks in Shunbei area of Tarim Basin[J]. World Petroleum Industry, 2024, 31(2): 55-64. | |
24 | 胡来东, 张志林, 徐雷良, 等. 塔里木盆地顺北地区碳酸盐岩断控储集体连通性量化表征[J]. 世界石油工业, 2024, 31(6): 30-37. |
HU Laidong, ZHANG Zhilin, XU Leiliang, et al. Internal connectivity quantitative characterization of fault-controlled grid reservoirs in Shunbei area, Tarim Basin[J]. World Petroleum Industry, 2024, 31(6): 30-37. | |
25 | 杨宁, 王贵文, 李潮流, 等. 塔里木盆地大北地区巴什基奇克组成岩相测井识别[J]. 中国石油大学学报(自然科学版), 2014, 38(5): 18-24. |
YANG Ning, WANG Guiwen, LI Chaoliu, et al. Reservoir diagenetic facies of Bashijiqike Formation in Dabei gas field compartmentalization and quantitative evaluation[J]. Journal of China University of Petroleum(Edition of Natural Science), 2014, 38(5): 18-24. | |
26 | 邓美洲, 牛娜, 尹霜, 等. 各向异性致密砂岩气藏分段压裂水平井气水两相产能预测模型[J]. 油气地质与采收率, 2024, 31(3): 99-111. |
DENG Meizhou, NIU Na, YIN Shuang, et al. Gas-water two-phase productivity prediction model of multistage fractured horizontal wells in anisotropic tight sandstone gas reservoirs[J]. Petroleum Geology and Recovery Efficiency, 2024, 31(3): 99-111. |
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