基于随机森林算法的泥页岩岩相测井识别

王民; 杨金路; 王鑫; 李进步; 徐亮; 言语

doi:10.3799/dqkx.2022.181

Volume 48 Issue 1

Jan. 2023

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Article Navigation > Earth Science > 2023 > 48(1): 130-142

Wang Min, Yang Jinlu, Wang Xin, Li Jinbu, Xu Liang, Yan Yu, 2023. Identification of Shale Lithofacies by Well Logs Based on Random Forest Algorithm. Earth Science, 48(1): 130-142. doi: 10.3799/dqkx.2022.181

Citation:

Wang Min, Yang Jinlu, Wang Xin, Li Jinbu, Xu Liang, Yan Yu, 2023. Identification of Shale Lithofacies by Well Logs Based on Random Forest Algorithm. Earth Science, 48(1): 130-142. doi: 10.3799/dqkx.2022.181

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Identification of Shale Lithofacies by Well Logs Based on Random Forest Algorithm

doi: 10.3799/dqkx.2022.181

Wang Min^{1, 2
,
,},
Yang Jinlu^{1, 2},
Wang Xin^{1, 2},
Li Jinbu^{1, 2},
Xu Liang^{1, 2},
Yan Yu^{1, 2}

1.
Key Laboratory of Deep Oil and Gas, China University of Petroleum, Qingdao 266580, China
2.
School of Geosciences, China University of Petroleum, Qingdao 266580, China

Received Date: 2022-02-11
Available Online: 2023-02-01
Publish Date: 2023-01-25

Abstract

Abstract

Shale lithofacies identification is an important task in the spatial distribution of shale oil and exploration target prediction, but it is difficult to identify lithofacies based on logging response equations due to the formation heterogeneity and redundancy of logging information. In this paper, a lithofacies identification model based on random forest algorithm is proposed, which uses the SHAP method to quantify the contribution of logging parameters. The results show that the random forest algorithm can identify shale lithofacies well, and its accuracy is higher than support vector machine, k-nearest neighbors and XGBoost; SP, CAL and AC contribute the most to the model's identification of lithofacies. The model can quickly identify the lithofacies of a single well, and determine the favorable lithofacies by combining total porosity, free hydrocarbon S₁, TOC, etc., and then determine the distribution of favorable lithofacies in the whole area, providing a basis for subsequent "sweet spot" prediction.
- random forest,
- machine learning,
- logging,
- lithofacies identification,
- shale

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References(42)

References

Al-Mudhafar, W. J., 2015. Integrating Component Analysis & Classification Techniques for Comparative Prediction of Continuous & Discrete Lithofacies Distributions. In: Offshore Technology Conference. SPE, Houston.

Al-Mudhafar, W. J., Al Lawe, E. M., Noshi, C. I., 2019. Clustering Analysis for Improved Characterization of Carbonate Reservoirs in a Southern Iraqi Oil Field. In: Offshore Technology Conference. SPE, Houston.

Atchley, S. C., Crass, B. T., Prince, K. C., 2021. The Prediction of Organic-Rich Reservoir Facies within the Late Pennsylvanian Cline Shale (Also Known as Wolfcamp D), Midland Basin, Texas. AAPG Bulletin, 105(1): 29-52. https://doi.org/10.1306/07272020010

Bhattacharya, S., Carr, T. R., Pal, M., 2016. Comparison of Supervised and Unsupervised Approaches for Mudstone Lithofacies Classification: Case Studies from the Bakken and Mahantango-Marcellus Shale, USA. Journal of Natural Gas Science and Engineering, 33: 1119-1133. https://doi.org/10.1016/j.jngse.2016.04.055

Biau, G., Scornet, E., 2016. A Random Forest Guided Tour. Test, 25(2): 197-227. https://doi.org/10.1007/s11749-016-0481-7

Breiman, L., 2001. Random Forests. Machine Learning, 45(1): 5-32. https://doi.org/10.1023/A:1010933404324

Feng, R. H., 2021. Improving Uncertainty Analysis in Well Log Classification by Machine Learning with a Scaling Algorithm. Journal of Petroleum Science and Engineering, 196: 107995. https://doi.org/10.1016/j.petrol.2020.107995

Gifford, C. M., Agah, A., 2010. Collaborative Multi-Agent Rock Facies Classification from Wireline Well Log Data. Engineering Applications of Artificial Intelligence, 23(7): 1158-1172. https://doi.org/10.1016/j.engappai.2010.02.004

Goral, J., Walton, I., Andrew, M., et al., 2019. Pore System Characterization of Organic-Rich Shales Using Nanoscale-Resolution 3D Imaging. Fuel, 258: 116049. https://doi.org/10.1016/j.fuel.2019.116049

Hackley, P. C., Fishman, N., Wu, T., et al., 2016. Organic Petrology and Geochemistry of Mudrocks from the Lacustrine Lucaogou Formation, Santanghu Basin, Northwest China: Application to Lake Basin Evolution. International Journal of Coal Geology, 168: 20-34. https://doi.org/10.1016/j.coal.2016.05.011

Hu, S. Y., Zhao, W. Z., Hou, L. H., et al., 2020. Development Potential and Technical Strategy of Continental Shale Oil in China. Petroleum Exploration and Development, 47(4): 819-828 (in Chinese with English abstract).

Li, B. Y., Pang, X. Q., Dong, Y. X., et al., 2019a. Lithofacies and Pore Characterization in an Argillaceous-Siliceous-Calcareous Shale System: A Case Study of the Shahejie Formation in Nanpu Sag, Bohai Bay Basin, China. Journal of Petroleum Science and Engineering, 173: 804-819. https://doi.org/10.1016/j.petrol.2018.10.086

Li, J. B., Wang, M., Chen, Z. H., et al., 2019b. Evaluating the Total Oil Yield Using a Single Routine Rock-Eval Experiment on As-Received Shales. Journal of Analytical and Applied Pyrolysis, 144: 104707. https://doi.org/10.1016/j.jaap.2019.104707

Li, J. B., Jiang, C. Q., Wang, M., et al., 2020. Adsorbed and Free Hydrocarbons in Unconventional Shale Reservoir: A New Insight from NMR T1-T2 Maps. Marine and Petroleum Geology, 116: 104311. https://doi.org/10.1016/j.marpetgeo.2020.104311

Li, Q. Q., Lan, B. F., Li, G. Q., et al., 2021. Element Geochemical Characteristics and Their Geological Significance of Wufeng-Longmaxi Formation Shales in North Margin of the Central Guizhou Uplift. Earth Science, 46(9): 3172-3188 (in Chinese with English abstract).

Li, S. X., Zhou, X. P., Guo, Q. H., et al., 2021. Research on Evaluation Method of Movable Hydrocarbon Resources of Shale Oil in the Chang 7₃ Sub-Member in the Ordos Basin. Natural Gas Geoscience, 32(12): 1771-1784 (in Chinese with English abstract).

Lin, M. R., Xi, K. L., Cao, Y. C., et al., 2021. Petrographic Features and Diagenetic Alteration in the Shale Strata of the Permian Lucaogou Formation, Jimusar Sag, Junggar Basin. Journal of Petroleum Science and Engineering, 203: 108684. https://doi.org/10.1016/j.petrol.2021.108684

Liu, B., Shi, J. X., Fu, X. F., et al., 2018. Petrological Characteristics and Shale Oil Enrichment of Lacustrine Fine-Grained Sedimentary System: A Case Study of Organic-Rich Shale in First Member of Cretaceous Qingshankou Formation in Gulong Sag, Songliao Basin, NE China. Petroleum Exploration and Development, 45(5): 828-838 (in Chinese with English abstract).

Liu, Z. B., Liu, G. X., Hu, Z. Q., et al., 2019. Lithofacies Types and Assemblage Features of Continental Shale Strata and Their Significance for Shale Gas Exploration: A Case Study of the Middle and Lower Jurassic Strata in the Sichuan Basin. Natural Gas Industry, 39(12): 10-21 (in Chinese with English abstract).

Lu, S. F., Li, J. Q., Zhang, P. F., et al., 2018. Classification of Microscopic Pore-Throats and the Grading Evaluation on Shale Oil Reservoirs. Petroleum Exploration and Development, 45(3): 436-444 (in Chinese with English abstract).

Lundberg, S. M., Lee, S. I., 2017. A Unified Approach to Interpreting Model Predictions. 31st Conference on Neural Information Processing Systems, Long Beach.

Nie, H. K., Zhang, P. X., Bian, R. K., et al., 2016. Oil Accumulation Characteristics of China Continental Shale. Earth Science Frontiers, 23(2): 55-62 (in Chinese with English abstract).

Su, S. Y., Jiang, Z. X., Shan, X. L., et al., 2019. Effect of Lithofacies on Shale Reservoir and Hydrocarbon Bearing Capacity in the Shahejie Formation, Zhanhua Sag, Eastern China. Journal of Petroleum Science and Engineering, 174: 1303-1308. https://doi.org/10.1016/j.petrol.2018.11.048

Wang, G. C., Carr, T. R., Ju, Y. W., et al., 2014. Identifying Organic-Rich Marcellus Shale Lithofacies by Support Vector Machine Classifier in the Appalachian Basin. Computers & Geosciences, 64: 52-60. https://doi.org/10.1016/j.cageo.2013.12.002

Wang, M., Ma, R., Li, J. B., et al., 2019. Occurrence Mechanism of Lacustrine Shale Oil in the Paleogene Shahejie Formation of Jiyang Depression, Bohai Bay Basin, China. Petroleum Exploration and Development, 46(4): 789-802 (in Chinese with English abstract).

Wang, P., Chen, X. H., Wang, B. F., et al., 2020. An Improved Method for Lithology Identification Based on a Hidden Markov Model and Random Forests. Geophysics, 85(6): IM27-IM36. https://doi.org/10.1190/geo2020-0108.1

Wang, Z. M., Jiang, Y. Q., Fu, Y. H., et al., 2022. Characterization of Pore Structure and Heterogeneity of Shale Reservoir from Wufeng Formation-Sublayers Long-1₁ in Western Chongqing Based on Nuclear Magnetic Resonance. Earth Science, 47(2): 490-504 (in Chinese with English abstract).

Wu, S. T., Zhu, R. K., Cui, J. G., et al., 2015. Characteristics of Lacustrine Shale Porosity Evolution, Triassic Chang 7 Member, Ordos Basin, NW China. Petroleum Exploration and Development, 42(2): 167-176 (in Chinese with English abstract).

Zeng, H. B., Wang, F. R., Luo, J., et al., 2021. Characteristics of Pore Structure of Intersalt Shale Oil Reservoir by Low Temperature Nitrogen Adsorption and High Pressure Mercury Pressure Methods in Qianjiang Sag. Bulletin of Geological Science and Technology, 40(5): 242-252 (in Chinese with English abstract).

Zhang, B., Mao, Z. G., Zhang, Z. Y., et al., 2021. Black Shale Formation Environment and Its Control on Shale Oil Enrichment in Triassic Chang 7 Member, Ordos Basin, NW China. Petroleum Exploration and Development, 48(6): 1127-1136 (in Chinese with English abstract).

胡素云, 赵文智, 侯连华, 等, 2020. 中国陆相页岩油发展潜力与技术对策. 石油勘探与开发, 47(4): 819-828. https://www.cnki.com.cn/Article/CJFDTOTAL-SKYK202004021.htm

李琪琪, 蓝宝锋, 李刚权, 等, 2021. 黔中隆起北缘五峰‒龙马溪组页岩元素地球化学特征及其地质意义. 地球科学, 46(9): 3172-3188. doi: 10.3799/dqkx.2020.354

李士祥, 周新平, 郭芪恒, 等, 2021. 鄂尔多斯盆地长7₃亚段页岩油可动烃资源量评价方法. 天然气地球科学, 32(12): 1771-1784. https://www.cnki.com.cn/Article/CJFDTOTAL-TDKX202112003.htm

柳波, 石佳欣, 付晓飞, 等, 2018. 陆相泥页岩层系岩相特征与页岩油富集条件——以松辽盆地古龙凹陷白垩系青山口组一段富有机质泥页岩为例. 石油勘探与开发, 45(5): 828-838. https://www.cnki.com.cn/Article/CJFDTOTAL-SKYK201805009.htm

刘忠宝, 刘光祥, 胡宗全, 等, 2019. 陆相页岩层系岩相类型、组合特征及其油气勘探意义——以四川盆地中下侏罗统为例. 天然气工业, 39(12): 10-21. https://www.cnki.com.cn/Article/CJFDTOTAL-TRQG201912003.htm

卢双舫, 李俊乾, 张鹏飞, 等, 2018. 页岩油储集层微观孔喉分类与分级评价. 石油勘探与开发, 45(3): 436-444. https://www.cnki.com.cn/Article/CJFDTOTAL-SKYK201803009.htm

聂海宽, 张培先, 边瑞康, 等, 2016. 中国陆相页岩油富集特征. 地学前缘, 23(2): 55-62. https://www.cnki.com.cn/Article/CJFDTOTAL-DXQY201602009.htm

王民, 马睿, 李进步, 等, 2019. 济阳坳陷古近系沙河街组湖相页岩油赋存机理. 石油勘探与开发, 46(4): 789-802. https://www.cnki.com.cn/Article/CJFDTOTAL-SKYK201904020.htm

王子萌, 蒋裕强, 付永红, 等, 2022. 基于核磁共振表征渝西地区五峰组‒龙一₁亚段页岩储层孔隙结构及非均质性. 地球科学, 47(2): 490-504. doi: 10.3799/dqkx.2021.076

吴松涛, 朱如凯, 崔京钢, 等, 2015. 鄂尔多斯盆地长7湖相泥页岩孔隙演化特征. 石油勘探与开发, 42(2): 167-176. https://www.cnki.com.cn/Article/CJFDTOTAL-SKYK201502006.htm

曾宏斌, 王芙蓉, 罗京, 等, 2021. 基于低温氮气吸附和高压压汞表征潜江凹陷盐间页岩油储层孔隙结构特征. 地质科技通报, 40(5): 242-252. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ202105025.htm

张斌, 毛治国, 张忠义, 等, 2021. 鄂尔多斯盆地三叠系长7段黑色页岩形成环境及其对页岩油富集段的控制作用. 石油勘探与开发, 48(6): 1127-1136. https://www.cnki.com.cn/Article/CJFDTOTAL-SKYK202106006.htm

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Identification of Shale Lithofacies by Well Logs Based on Random Forest Algorithm

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