鄂尔多斯盆地延长组多油源贡献比例与分布规律:基于机器学习与可解释性研究

苏恺明, 徐耀辉, 徐旺林, 张月巧, 白斌, 李阳, 严刚

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地学前缘 ›› 2024, Vol. 31 ›› Issue (3) : 530-540. DOI: 10.13745/j.esf.sf.2023.9.56
人工智能与地质应用

鄂尔多斯盆地延长组多油源贡献比例与分布规律:基于机器学习与可解释性研究

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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

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摘要

鄂尔多斯盆地延长组发育多套潜在的烃源岩,但不同烃源岩之间生物标志物特征相似,常规油源对比方法效果不佳,相关认识长期存在争议。基于这样的问题,本文提出了一种基于深度学习的油源对比方案,将人工智能方法应用于油源对比研究,所开展的工作和认识有:(1)以延长组不同层位大量泥岩、页岩样品的42种生物标志物参数作为学习数据,构建了一种识别未知样品油源类别的深度神经网络模型,对长7泥页岩、长8—长10泥页岩的判别正确率分别达到了79.6%和83.0%,实现了延长组主要烃源岩生烃产物的有效区分;(2)通过模型分析了大量砂岩、原油样品的油源分类,统计了不同烃源岩对于延长组各个油层组原油的贡献比例,总结了它们的分布规律;(3)基于目前较为先进的置换特征重要性(PFI)算法,对所得模型进行了敏感性分析,初步揭示了延长组两类主要烃源岩的生物标志物差异。本文对于人工智能方法、技术在石油分子地球化学领域的发展具有积极的参考价值。

Abstract

The Yanchang Formation within the Ordos Basin hosts multiple sets of potential source rocks, all exhibiting similar biomarker properties. The conventional method of oil-source correlation has proven ineffective, leading to longstanding debates within the field. In response to these challenges, this study introduces a novel deep learning-based scheme for oil-source comparison, leveraging artificial intelligence methods for research in this domain. The study presents the following key findings and insights: (1) Development of a deep neural network model for identifying the oil source type of unknown samples by utilizing 42 biomarker parameters from a diverse set of mudstone and shale samples representing different oil groups within the Yanchang Formation as training data. The model achieved identification accuracies of 83.0% for Chang 7 mudstone and 79.6% for Chang 8-Chang 10 mudstone, successfully distinguishing the primary source rocks of the Yanchang Formation from hydrocarbon generation products. (2) Application of the model to analyze the oil source classification of numerous sandstone and oil samples. The study calculated the contribution ratios of various source rocks to each oil group within the Yanchang Formation, summarizing their distribution patterns. (3) Conducting sensitivity analysis of the model using the permutation feature importance (PFI) algorithm, revealing differences in biomarkers between the two main source rocks of the Yanchang Formation. These findings contribute to advancing artificial intelligence techniques and technologies in the field of petroleum molecular geochemistry, offering valuable insights for future research and applications.

关键词

机器学习 / 深度神经网络 / 敏感性分析 / 鄂尔多斯盆地延长组 / 油源对比

Key words

machine learning / deep neural network / sensitivity analysis / Yanchang Formation of Ordos Basin / oil-source correlation

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TE122.11

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导出引用
苏恺明 , 徐耀辉 , 徐旺林 , 等. 鄂尔多斯盆地延长组多油源贡献比例与分布规律:基于机器学习与可解释性研究. 地学前缘. 2024, 31(3): 530-540 https://doi.org/10.13745/j.esf.sf.2023.9.56
Kaiming SU, Yaohui XU, Wanglin XU, et al. 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 https://doi.org/10.13745/j.esf.sf.2023.9.56

参考文献

列表( 原文顺序 | 文献年度倒序 | 文中引用次数倒序 ) 可视化分析
[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.
本文引用 [1]
[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.
本文引用 [6]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [2]
[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.
本文引用 [2]
[8]
张景廉. 油气“倒灌”论质疑[J]. 岩性油气藏, 2009, 21(3): 122-128.
本文引用 [1]
[9]
李传亮. 油气倒灌不可能发生[J]. 岩性油气藏, 2009, 21(1): 6-10.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [4]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [3]
[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.
本文引用 [3]
[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.
本文引用 [1]
[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.
本文引用 [3]
[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.
本文引用 [1]
[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.
本文引用 [2]
[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.
本文引用 [3]
[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.
本文引用 [6]
[38]
BARROW H. Connectionism and neural networks[M]//BODEN M A. Handbook of perception and cognition. New York: Academic Press, 1996: 135-155.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [3]
[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.
本文引用 [2]
[54]
BREIMAN L. Random Forests[J]. Machine Learning, 2001, 45: 5-32.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [2]
[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.
本文引用 [1]
[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.
本文引用 [1]
[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.
本文引用 [1]

基金

中国博士后科学基金面上资助(2023M730365)
湖北省自然科学基金计划青年项目(2023AFB232)
中石油科学研究与技术开发项目(2021DJ0404)

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