Earth Science Frontiers ›› 2024, Vol. 31 ›› Issue (3): 530-540.DOI: 10.13745/j.esf.sf.2023.9.56
SU Kaiming1,2(), XU Yaohui1,2,*(
), XU Wanglin3, ZHANG Yueqiao3, BAI Bin3, LI Yang1,2, YAN Gang1,2
Received:
2023-05-31
Revised:
2023-09-06
Online:
2024-05-25
Published:
2024-05-25
CLC Number:
SU Kaiming, XU Yaohui, XU Wanglin, ZHANG Yueqiao, BAI Bin, LI Yang, YAN Gang. Contribution ratio and distribution patterns of multiple oil sources in the Yanchang Formation of the Ordos Basin: A study utilizing machine learning and interpretability techniques[J]. Earth Science Frontiers, 2024, 31(3): 530-540.
化合物系列 | 生物标志物参数 |
---|---|
萜烷系列 | C29βα/C29αβ、C30βα/C30αβ、Ga/C30αβ、Ts/C30αβ、C30*/C30αβ、C30*/C29Ts、Ts/(Ts+Tm)、C29αβ/C30αβ、ΣC19~26TT/C30αβ、C24TET/C30αβ、C23TT/C30αβ、C29Ts/(C29Ts+C29αβ)、C31αβ22S/(22S+22R)、C32αβ22S/(22S+22R)、C3322S/(22S+22R)、C3422S/(22S+22R)、Ga/C31αβ、C19TT/C23TT、C20TT/C23TT、C21TT/C23TT、C22TT/C21TT、C24TT/C23TT、C26TT/C25TT、C24TET/C23TT、C24TET/C26TT、ETR、(C19TT+C20TT)/(C23TT+C24TT)、(C31αβ+C32αβ)/(C33αβ+C34αβ)和三环萜烷/(三环萜烷+藿烷) |
甾烷系列 | C29ββ/(αα+ββ)、C2920S/(20R+20S)、C27/C27~29、C28/C27~29、C29/C27~29、C28/C29、重排甾烷C2720S/(20S+20R)、重排甾烷C2920S/(20S+20R)、重排甾烷/甾烷、(孕甾烷+升孕甾烷)/甾烷、C27αα20R/C29αα20R和升孕甾烷/孕甾烷 |
其他 | 甾烷/藿烷 |
Table 1 Biomarker parameters selected as characteristic variables for feature data
化合物系列 | 生物标志物参数 |
---|---|
萜烷系列 | C29βα/C29αβ、C30βα/C30αβ、Ga/C30αβ、Ts/C30αβ、C30*/C30αβ、C30*/C29Ts、Ts/(Ts+Tm)、C29αβ/C30αβ、ΣC19~26TT/C30αβ、C24TET/C30αβ、C23TT/C30αβ、C29Ts/(C29Ts+C29αβ)、C31αβ22S/(22S+22R)、C32αβ22S/(22S+22R)、C3322S/(22S+22R)、C3422S/(22S+22R)、Ga/C31αβ、C19TT/C23TT、C20TT/C23TT、C21TT/C23TT、C22TT/C21TT、C24TT/C23TT、C26TT/C25TT、C24TET/C23TT、C24TET/C26TT、ETR、(C19TT+C20TT)/(C23TT+C24TT)、(C31αβ+C32αβ)/(C33αβ+C34αβ)和三环萜烷/(三环萜烷+藿烷) |
甾烷系列 | C29ββ/(αα+ββ)、C2920S/(20R+20S)、C27/C27~29、C28/C27~29、C29/C27~29、C28/C29、重排甾烷C2720S/(20S+20R)、重排甾烷C2920S/(20S+20R)、重排甾烷/甾烷、(孕甾烷+升孕甾烷)/甾烷、C27αα20R/C29αα20R和升孕甾烷/孕甾烷 |
其他 | 甾烷/藿烷 |
层位 (油层组) | 长7油源 样品数/个 | 长8—长10油 源样品数/个 | 长7烃源岩 贡献率/% | 长8—长10烃 源岩贡献率/% |
---|---|---|---|---|
长7及 以上层位 | 17 | 0 | 100 | 0 |
长8 | 34 | 12 | 74 | 26 |
长9 | 25 | 13 | 66 | 34 |
长10 | 7 | 4 | 64 | 36 |
总计 | 83 | 29 | 74 | 26 |
Table 2 Analysis results of sandstone and oil samples using the neural network model
层位 (油层组) | 长7油源 样品数/个 | 长8—长10油 源样品数/个 | 长7烃源岩 贡献率/% | 长8—长10烃 源岩贡献率/% |
---|---|---|---|---|
长7及 以上层位 | 17 | 0 | 100 | 0 |
长8 | 34 | 12 | 74 | 26 |
长9 | 25 | 13 | 66 | 34 |
长10 | 7 | 4 | 64 | 36 |
总计 | 83 | 29 | 74 | 26 |
[1] |
付金华, 李士祥, 牛小兵, 等. 鄂尔多斯盆地三叠系长7段页岩油地质特征与勘探实践[J]. 石油勘探与开发, 2020, 47(5): 870-883.
DOI |
[2] |
杨华, 牛小兵, 徐黎明, 等. 鄂尔多斯盆地三叠系长7段页岩油勘探潜力[J]. 石油勘探与开发, 2016, 43(4): 511-520.
DOI |
[3] | 陈建平, 黄第藩. 鄂尔多斯盆地东南缘煤矿侏罗系原油油源[J]. 沉积学报, 1997, 15(2): 100-104. |
[4] | 侯林慧, 彭平安, 于赤灵, 等. 鄂尔多斯盆地姬塬—西峰地区原油地球化学特征及油源分析[J]. 地球化学, 2007, 36(5): 497-506. |
[5] | 王传远, 段毅, 杜建国, 等. 鄂尔多斯盆地三叠系延长组原油中性含氮化合物的分布特征及油气运移[J]. 油气地质与采收率, 2009, 16(3): 7-10. |
[6] | 郭艳琴, 李文厚, 陈全红, 等. 鄂尔多斯盆地安塞—富县地区延长组—延安组原油地球化学特征及油源对比[J]. 石油与天然气地质, 2006, 27(2): 218-224. |
[7] | 张文正, 杨华, 李善鹏. 鄂尔多斯盆地长91湖相优质烃源岩成藏意义[J]. 石油勘探与开发, 2008, 35(5): 557-562. |
[8] | 张景廉. 油气“倒灌”论质疑[J]. 岩性油气藏, 2009, 21(3): 122-128. |
[9] | 李传亮. 油气倒灌不可能发生[J]. 岩性油气藏, 2009, 21(1): 6-10. |
[10] | 张文正, 杨华, 候林慧, 等. 鄂尔多斯盆地延长组不同烃源岩17α(H)-重排藿烷的分布及其地质意义[J]. 中国科学(D辑: 地球科学), 2009, 39(10): 1438-1445. |
[11] | 邹贤利, 陈世加, 路俊刚, 等. 鄂尔多斯盆地延长组烃源岩17α(H)-重排藿烷的组成及分布研究[J]. 地球化学, 2017, 46(3): 252-261. |
[12] | 张敏, 李谨, 陈菊林. 热力作用对烃源岩中重排藿烷类化合物形成的作用[J]. 沉积学报, 2018, 36(5): 1033-1039. |
[13] | 李红磊, 张敏, 姜连, 等. 利用芳烃参数研究煤系烃源岩中重排藿烷成因[J]. 沉积学报, 2016, 34(1): 191-199. |
[14] | 李姗姗, 白斌, 严刚, 等. 泥页岩热模拟排出油与滞留油中17α(H)-重排藿烷的成熟度指示规律[J]. 石油实验地质, 2022, 44(5): 887-895. |
[15] |
付锁堂, 金之钧, 付金华, 等. 鄂尔多斯盆地延长组7段从致密油到页岩油认识的转变及勘探开发意义[J]. 石油学报, 2021, 42(5): 561-569.
DOI |
[16] |
付金华, 牛小兵, 李明瑞, 等. 鄂尔多斯盆地延长组7段3亚段页岩油风险勘探突破与意义[J]. 石油学报, 2022, 43(6): 760-769.
DOI |
[17] | 范柏江, 晋月, 师良, 等. 鄂尔多斯盆地中部三叠系延长组7段湖相页岩油勘探潜力[J]. 石油与天然气地质, 2021, 42(5): 1078-1088. |
[18] | 王龙, 陈培元, 孙福亭, 等. 鄂尔多斯盆地彭阳地区延长组、延安组原油地球化学特征与油源对比[J]. 海洋地质前沿, 2019, 35(12): 49-54. |
[19] | PETERS K E, WALTERS C C, MOLDOWAN J M. The biomarker guide: Volume 2, biomarkers and isotopes in petroleum systems and Earth history[M]. 2nd ed. New York: Cambridge University Press, 2007. |
[20] | SU K M, CHEN S J, HOU Y T, et al. Application of factor analysis to investigating molecular geochemical characteristics of organic matter and oil sources: an exploratory study of the Yanchang Formation in the Ordos Basin, China[J]. Journal of Petroleum Science and Engineering, 2022, 208: 109668. |
[21] | 王遥平. 基于化学计量学的油气源对比与实例研究[D]. 广州: 中国科学院广州地球化学研究所, 2019. |
[22] | ALIZADEH B, ALIPOUR M, CHEHRAZI A, et al. Chemometric classification and geochemistry of oils in the Iranian sector of the southern Persian Gulf Basin[J]. Organic Geochemistry, 2017, 111: 67-81. |
[23] | 王遥平, 邹艳荣, 史健婷, 等. 化学计量学在油-油和油-源对比中的应用现状及展望[J]. 天然气地球科学, 2018, 29(4): 452-467. |
[24] | NIU X X, SUEN C Y. A novel hybrid CNN-SVM classifier for recognizing handwritten digits[J]. Pattern Recognition, 2012, 45(4): 1318-1325. |
[25] | LIN J D, WU X Y, CHAI Y, et al. Structure optimization of convolutional neural networks: a survey[J]. Acta Automatica Sinica, 2020, 46(1): 24-37. |
[26] | 韩玉娇. 基于AdaBoost机器学习算法的大牛地气田储层流体智能识别[J]. 石油钻探技术, 2022, 50(1): 112-118. |
[27] | KOESHIDAYATULLAH A, MORSILLI M, LEHRMANN D J, et al. Fully automated carbonate petrography using deep convolutional neural networks[J]. Marine and Petroleum Geology, 2020, 122: 104687. |
[28] |
杜炳毅, 张广智, 王磊, 等. 基于机器学习的复杂储层微小断裂系统识别方法研究与应用[J]. 石油物探, 2021, 60(4): 621-631.
DOI |
[29] | 周永章, 左仁广, 刘刚, 等. 数学地球科学跨越发展的十年: 大数据、人工智能算法正在改变地质学[J]. 矿物岩石地球化学通报, 2021, 40(3): 556-573. |
[30] | QU H J, YANG B, GAO S L, et al. Controls on hydrocarbon accumulation by facies and fluid potential in large-scale lacustrine petroliferous basins in compressional settings: a case study of the Mesozoic Ordos Basin, China[J]. Marine and Petroleum Geology, 2020, 122: 104668. |
[31] | ZHANG K, LIU R, LIU Z J. Sedimentary sequence evolution and organic matter accumulation characteristics of the Chang 8-Chang 7 members in the Upper Triassic Yanchang Formation, Southwest Ordos Basin, central China[J]. Journal of Petroleum Science and Engineering, 2021, 196: 107751. |
[32] | LI Q, WU S H, XIA D L, et al. Major and trace element geochemistry of the lacustrine organic-rich shales from the Upper Triassic Chang 7 member in the southwestern Ordos Basin, China: implications for paleoenvironment and organic matter accumulation[J]. Marine and Petroleum Geology, 2020, 111: 852-867. |
[33] | 邓南涛, 张枝焕, 鲍志东, 等. 鄂尔多斯盆地南部延长组有效烃源岩地球化学特征及其识别标志[J]. 中国石油大学学报(自然科学版), 2013, 37(2): 135-145. |
[34] | 姚泾利, 高岗, 庞锦莲, 等. 鄂尔多斯盆地陇东地区延长组非主力有效烃源岩发育特征[J]. 地学前缘, 2013, 20(2): 116-124. |
[35] | 周世颖. 鄂尔多斯盆地周家湾—高桥地区长7—长9烃源岩评价及油源研究[D]. 成都: 西南石油大学, 2017. |
[36] | MALEKI F, OVENS K, NAJAFIAN K, et al. Overview of machine learning, part 1: fundamentals and classic approaches[J]. Neuroimaging Clinics of North America, 2020, 30(4): e17-e32. |
[37] | 周永章, 张良均, 张奥多, 等. 地球科学大数据挖掘与机器学习[M]. 广州: 中山大学出版社, 2018. |
[38] | BARROW H. Connectionism and neural networks[M]//BODEN M A. Handbook of perception and cognition. New York: Academic Press, 1996: 135-155. |
[39] | SAIKIA P, BARUAH R D, SINGH S K, et al. Artificial neural networks in the domain of reservoir characterization: a review from shallow to deep models[J]. Computers & Geosciences, 2020, 135: 104357. |
[40] | 李苍柏, 肖克炎, 李楠, 等. 支持向量机、随机森林和人工神经网络机器学习算法在地球化学异常信息提取中的对比研究[J]. 地球学报, 2020, 41(2): 309-319. |
[41] | 王琪琪, 汤井田, 张良, 等. 利用多层感知机的地震数据去噪[J]. 石油地球物理勘探, 2020, 55(2): 272-281. |
[42] | LESHNO M, LIN V Y, PINKUS A, et al. Multilayer feedforward networks with a nonpolynomial activation function can approximate any function[J]. Neural Networks, 1993, 6(6): 861-867. |
[43] | 黄毅, 段修生, 孙世宇, 等. 基于改进sigmoid激活函数的深度神经网络训练算法研究[J]. 计算机测量与控制, 2017, 25(2): 126-129. |
[44] |
邓建国, 张素兰, 张继福, 等. 监督学习中的损失函数及应用研究[J]. 大数据, 2020, 6(1): 60-80.
DOI |
[45] | KINGMA D P, BA J. Adam: A method for stochastic optimization[C]//Proceeding of the 3rd international conference for learning Representations (ICLR 2015). San Diego: ArXiv, 2015. |
[46] | ENEOGWE C, EKUNDAYO O. Geochemical correlation of crude oils in the NW Niger Delta, Nigeria[J]. Journal of Petroleum Geology, 2003, 26(1): 95-103. |
[47] | AHMED M, VOLK H, ALLAN T, et al. Origin of oils in the Eastern Papuan Basin, Papua New Guinea[J]. Organic Geochemistry, 2012, 53: 137-152. |
[48] | XIAO H, LI M J, LIU J G, et al. Oil-oil and oil-source rock correlations in the Muglad Basin, Sudan and South Sudan: new insights from molecular markers analyses[J]. Marine and Petroleum Geology, 2019, 103: 351-365. |
[49] | SPAAK G, EDWARDS D S, FOSTER C B, et al. Geochemical characteristics of early Carboniferous petroleum systems in Western Australia[J]. Marine and Petroleum Geology, 2020, 113: 104073. |
[50] | ANYSZ H, ZBICIAK A, IBADOV N. The influence of input data standardization method on prediction accuracy of artificial neural networks[J]. Procedia Engineering, 2016, 153: 66-70. |
[51] | WEI X, ZHANG L L, YANG H Q, et al. Machine learning for pore-water pressure time-series prediction: application of recurrent neural networks[J]. Geoscience Frontiers, 2021, 12(1): 453-467. |
[52] | 李吉君, 吴慧, 卢双舫, 等. 鄂尔多斯盆地长9烃源岩发育与排烃效率[J]. 吉林大学学报(地球科学版), 2012(增刊1): 26-32. |
[53] | GEVREY M, DIMOPOULOS I, LEK S. Two-way interaction of input variables in the sensitivity analysis of neural network models[J]. Ecological Modelling, 2006, 195(1/2): 43-50. |
[54] | BREIMAN L. Random Forests[J]. Machine Learning, 2001, 45: 5-32. |
[55] |
MI X, ZOU B, ZOU F, et al. Permutation-based identification of important biomarkers for complex diseases via machine learning models[J]. Nature Communications, 2021, 12: 3008.
DOI PMID |
[56] | RAMIREZ S G, HALES R C, WILLIAMS G P, et al. Extending SC-PDSI-PM with neural network regression using GLDAS data and Permutation Feature Importance[J]. Environmental Modelling & Software, 2022, 157: 105475. |
[57] | LI Z, SHI H, YANG X, et al. Investigating the nonlinear relationship between surface solar radiation and its influencing factors in North China Plain using interpretable machine learning[J]. Atmospheric Research, 2022, 280: 106406. |
[58] | VIJ A, NANJUNDAN P. Comparing strategies for post-hoc explanations in machine learning models[M]//SHAKYA S, BESTAK R, PALANISAMY R, et al. Mobile computing and sustainable informatics lecture notes on data engineering and communications technologies. Singapore: Springer Nature Singapore, 2021: 585-592. |
[59] | KRUGE M A, HUBERT J F, AKES R J, et al. Biological markers in Lower Jurassic synrift lacustrine black shales, Hartford Basin, Connecticut, U.S.A.[J]. Organic Geochemistry, 1990, 15(3): 281-289. |
[60] | CONNAN J, BOUROULLEC J, DESSORT D, et al. The microbial input in carbonate-anhydrite facies of a sabkha palaeoenvironment from Guatemala: a molecular approach[J]. Organic Geochemistry, 1986, 10(1/2/3): 29-50. |
[1] | ZHANG Huanbao, HE Haiyang, YANG Shijiao, LI Yalin, BI Wenjun, HAN Shili, GUO Qinpeng, DU Qing. Machine learning-based approach for adakitic rocks tectonic setting determination [J]. Earth Science Frontiers, 2024, 31(4): 417-428. |
[2] | LIU Yang, LI Sanzhong, ZHONG Shihua, GUO Guanghui, LIU Jiaqing, NIU Jinghui, XUE Zimeng, ZHOU Jianping, DONG Hao, SUO Yanhui. Machine learning: A new approach to intelligent exploration of seafloor mineral resources [J]. Earth Science Frontiers, 2024, 31(3): 520-529. |
[3] | ZHANG Lijun, LU Wenhao, ZHANG Jiandong, PENG Guangxiong, BU Jiancai, TANG Kai, XIE Jiancheng, XU Zhibin, YANG Haiyan. Rock and mineral thin section identification based on deep learning [J]. Earth Science Frontiers, 2024, 31(3): 498-510. |
[4] | XU Haning, DENG Juzhi, XIAO Hui. Multi-dimensional geoelectrical resistivity imaging monitoring for debris flow based on neighborhood domain features [J]. Earth Science Frontiers, 2023, 30(6): 473-484. |
[5] | WANG Ziye, ZUO Renguang. Mapping Himalayan leucogranites by machine learning using multi-source data [J]. Earth Science Frontiers, 2023, 30(5): 216-226. |
[6] | SONG Xuanyu, XU Min, KANG Shichang, SUN Liping. Modeling of hydrological processes in cryospheric watersheds based on machine learning [J]. Earth Science Frontiers, 2023, 30(4): 451-469. |
[7] | ZHU Ziyi, ZHOU Fei, WANG Yu, ZHOU Tong, HOU Zhaoliang, QIU Kunfeng. Machine learning-based approach for zircon classification and genesis determination [J]. Earth Science Frontiers, 2022, 29(5): 464-475. |
[8] | HU Yiming, CHEN Teng, LUO Xuyi, TANG Chao, LIANG Zhongmin. Medium to long term runoff forecast for the Huai River Basin based on machine learning algorithm [J]. Earth Science Frontiers, 2022, 29(3): 284-291. |
[9] | ZHANG Zhenjie, CHENG Qiuming, YANG Jie, WU Guopeng, GE Yunzhao. Machine learning for mineral prospectivity: A case study of iron-polymetallic mineral prospectivity in southwestern Fujian [J]. Earth Science Frontiers, 2021, 28(3): 221-235. |
[10] | ZUO Renguang. Data science-based theory and method of quantitative prediction of mineral resources [J]. Earth Science Frontiers, 2021, 28(3): 49-55. |
[11] | ZHANG Qi,JIAO Shoutao,LI Mingchao,ZHU Yueqin,HAN Shuai,LIU Xuelong, JIN Weijun,CHEN Wanfeng,LIU Xinyu. Applicability of quantum entanglement technology in geology [J]. Earth Science Frontiers, 2019, 26(4): 159-169. |
[12] | ZUO Renguang. Exploration geochemical data mining and weak geochemical anomalies identification [J]. Earth Science Frontiers, 2019, 26(4): 67-75. |
[13] | HONG Jin,GAN Chengshi,LIU Jie. Prediction of REEs in OIB by major elements based on machine learning [J]. Earth Science Frontiers, 2019, 26(4): 45-54. |
[14] | ZHANG Baoyue,SUN Jiankun,LUO Xiong,JIN Weijun,WANG Long,DU Xueliang,CHEN Wanfeng,DU Jun,ZHANG Qi,ZHU Yueqin. Data analysis of major and trace element of gabbro clinopyroxene from different tectonic setting [J]. Earth Science Frontiers, 2019, 26(4): 33-44. |
[15] | LUO Jianmin,ZHANG Qi. Big data pioneers new ways of geoscience research: identifying relevant relationships to enhance research feasibility [J]. Earth Science Frontiers, 2019, 26(4): 6-12. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||