东营凹陷页岩油岩相要素测井评价新方法及其应用

doi:10.13809/j.cnki.cn32-1825/te.2024.03.013

Abstract

Abstract:

The lithofacies of shale oil within the Dongying Sag of the Jiyang Depression are distinguished by their complex lithology, strong heterogeneity, and marked regional distribution variances. Current logging methods inadequately identify and evaluate the lithofacial characteristics of shale oil in this region. This study delves into the lithofacies characteristics of the upper submember of Chunhuazhen Formation of the fourth member of the Shahejie Formation in Dongying Sag, employing core calibration logging integrated with core, thin section, experimental analysis, and testing data. Utilizing the “three terminal elements and four elements” shale lithofacies partitioning scheme as a guiding principle, this research selects responsive characteristics of different lithofacies logging and sensitive parameter logging curves to develop an appropriate shale oil logging lithofacies partitioning method. The approach combines stratification, clustering through Agglomerative Hierarchical Clustering（AHC）, Fisher discriminant analysis, wavelet frequency extraction, and genetic optimization neural networks to discern the rock composition, sedimentary structure, rock texture, and organic matter content of different lithofacies. This methodology addresses the challenges posed by complex lithology, limited logging resolution, incomplete special logging data, and inadequate Total Organic Carbon（TOC）model accuracy. By identifying the “four characteristics” of lithofacies, the study establishes a quantitative logging identification method and technology for shale oil lithofacies in Dongying Sag, pinpointing concentrated lithofacies segments. The findings provide a critical geological basis for the large-scale exploration and development of shale oil in the region.

Key words: the upper submember of Chunhuazhen Formation of the fourth member in Dongying Sag, shale oil lithofacies, rock structure, bedding structure, organic carbon content, logging evaluation method

CLC Number:

TE122

GUAN Qianqian,JIANG Long,CHENG Ziyan,ZHANG Diandong,WANG Yunhe,ZHANG Fan. A new method of shale oil facies element logging evaluation and its application in Dongying Sag[J].Petroleum Reservoir Evaluation and Development, 2024, 14(3): 435-445.

Figures/Tables 8

Fig. 1

Table 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

References 26

[1]	杨涛涛, 范国章, 吕福亮, 等. 烃源岩测井响应特征及识别评价方法[J]. 天然气地球科学, 2013, 24(2): 414-422.
	YANG Taotao, FAN Guozhang, LYU Fuliang, et al. The logging features and identification methods of source rock[J]. Natural Gas Geoscience, 2013, 24(2): 414-422.
[2]	李国永. 复杂断块油藏精细描述关键技术与应用[J]. 油气藏评价与开发, 2023, 13(2): 152-162.
	LI Guoyong. Key technology of fine description of complex fault block reservoir and its application[J]. Petroleum Reservoir Evaluation and Development, 2023, 13(2): 152-162.
[3]	孙焕泉. 济阳坳陷页岩油勘探实践与认识[J]. 中国石油勘探, 2017, 22(4): 1-14. doi: 10.3969/j.issn.1672-7703.2017.04.001
	SUN Huanquan. Exploration practice and prospect of shale oil in Jiyang depression[J]. China Petroleum Exploration, 2017, 22(4): 1-14. doi: 10.3969/j.issn.1672-7703.2017.04.001
[4]	曾棒, 刘小平, 刘国勇, 等. 陆相泥页岩层系岩相测井识别与预测: 以南堡凹陷拾场次洼为例[J]. 地质科技通报, 2021, 40(1): 69-79.
	ZENG Bang, LIU Xiaoping, LIU Guoyong, et al. Logging identification and prediction of lithofacies of lacustrine shale system in Shichang Sub-Sag, Nanpu Depression[J]. Bulletin of Geological Science and Technology, 2021, 40(1): 69-79.
[5]	卢双舫, 马延伶, 曹瑞成, 等. 优质烃源岩评价标准及其应用: 以海拉尔盆地乌尔逊凹陷为例[J]. 地球科学, 2012, 37(3): 535-544.
	LU Shuangfang, MA Yanling, CAO Ruicheng, et al. Evaluation criteria and application of high-quality source rocks and its applications: Taking the Wuerxun Sag in Hailar Basin as an example[J]. Earth Science, 2012, 37(3): 535-544.
[6]	李昌, 沈安江, 孟贺. 电成像测井新参数在碳酸盐岩岩相识别中的应用[J]. 科学技术与工程, 2021, 21(26): 11130-11135.
	LI Chang, SHEN Anjiang, MENG He. Application of new parameters of electrical imaging logging in carbonate facies identification[J]. Science Technology and Engineering, 2021, 21(26): 11130-11135.
[7]	朱振宇, 刘洪, 李幼铭. ΔlogR技术在烃源岩识别中的应用与分析[J]. 地球物理学进展, 2003, 18(4): 647-649.
	ZHU Zhenyu, LIU Hong, LI Youming. The analysis and application of ΔlogR method in the source rock's identification[J]. Advances in Geophysics, 2003, 18(4): 647-649.
[8]	彭军, 杨一茗, 刘惠民, 等. 陆相湖盆细粒混积岩的沉积特征与成因机理——以东营凹陷南坡陈官庄地区沙河街组四段上亚段为例[J]. 石油学报, 2022, 43(10): 1409-1426. doi: 10.7623/syxb202210005
	PENG Jun, YANG Yiming, LIU Huimin, et al. Sedimentary characteristics and genetic mechanism of fine-grained hybrid sedimentary rocks in continental lacustrine basin: A case study of the upper submember of Member 4 of Shahejie Formation in Chenguanzhuang area, southern slope of Dongying Sag[J]. Acta Petrolei Sinica, 2022, 43(10): 1409-1426. doi: 10.7623/syxb202210005
[9]	彭君, 周勇水, 李红磊, 等. 渤海湾盆地东濮凹陷盐间细粒沉积岩岩相与含油性特征[J]. 断块油气田, 2021, 28(2): 212-218.
	PENG Jun, ZHOU Yongshui, LI Honglei, et al. Lithofacies and oil-bearing characteristics of fine-grained sedimentary rocks of salt-layers in Dongpu Sag, Bohai Bay Basin[J]. Fault-Block Oil & Gas Field, 2021, 28(2): 212-218.
[10]	林中凯, 张少龙, 李传华, 等. 湖相页岩油地层岩相组合类型划分及其油气勘探意义——以博兴洼陷沙河街组为例[J]. 油气藏评价与开发, 2023, 13(1): 39-51.
	LIN Zhongkai, ZHANG Shaolong, LI Chuanhua, et al. Types of shale lithofacies assemblage and its significance for shale oil exploration: A case study of Shahejie Formation in Boxing Sag[J]. Petroleum Reservoir Evaluation and Development, 2023, 13(1): 39-51.
[11]	王永诗, 唐东. 咸化断陷湖盆典型页岩剖面地质特征——以东营凹陷为例[J]. 油气藏评价与开发, 2022, 12(1): 181-191.
	WANG Yongshi, TANG Dong. Geological characteristics of typical shale profile in a saline lacustrine rift basin: A case study of Dongying Sag[J]. Petroleum Reservoir Evaluation and Development, 2022, 12(1): 181-191.
[12]	ZHAO X Z, PU X G, JIANG W Y, et al. An exploration breakthrough in Paleozoic petroleum system of Huanghua Depression in Dagang Oilfield and its significance, North China[J]. Petroleum Exploration and Development, 2019, 46(4): 651-663.
[13]	张顺. 济阳坳陷页岩油富集要素及地质甜点类型划分[J]. 科学技术与工程, 2021, 21(2): 504-511.
	ZHANG Shun. Shale oil enrichment elements and geological dessert types in Jiyang Depression[J]. Science Technology and Engineering, 2021, 21(2): 504-511.
[14]	张佳佳, 李宏兵, 姚逢昌, 等. 油页岩的地球物理识别和评价方法[J]. 石油学报, 2012, 33(4): 625-632. doi: 10.7623/syxb201204012
	ZHANG Jiajia, LI Hongbing, YAO Fengchang, et al. A geophysical method for the identification and evaluation of oil shale[J]. Acta Petrolei Sinica, 2012, 33(4): 625-632. doi: 10.7623/syxb201204012
[15]	王晓明, 陈军斌, 任大忠. 陆相页岩油储层孔隙结构表征和渗流规律研究进展及展望[J]. 油气藏评价与开发, 2023, 13(1): 23-30.
	WANG Xiaoming, CHEN Junbin, REN Dazhong. Research progress and prospect of pore structure representation and seepage law of continental shale oil reservoir[J]. Petroleum Reservoir Evaluation and Development, 2023, 13(1): 23-30.
[16]	LIU B, SHI J X, FU X F, et al. 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[J]. Petroleum Exploration and Development, 2018, 45(5): 884-894.
[17]	唐凡, 朱永刚, 张彦明, 等. CO₂注入对储层多孔介质及赋存流体性质影响实验研究[J]. 石油与天然气化工, 2021, 50(1): 72-76.
	TANG Fan, ZHU Yonggang, ZHANG Yanming, et al. Experimental research of the effect of CO₂injection on porous media and fluid property in reservoir[J]. Chemical Engineering of Oil & Gas, 2021, 50(1): 72-76.
[18]	滕建彬. 东营凹陷页岩油储层中方解石的成因及证据[J]. 油气地质与采收率, 2020, 27(2): 18-25.
	TENG Jianbin. Origin and evidence of calcite in shale oil reservoir of Dongying Sag[J]. Petroleum Geology and Recovery Efficiency, 2020, 27(2): 18-25.
[19]	陈恋, 袁梅, 向维, 等. PCA-Fisher判别模型在煤层底板突水预测中的应用[J]. 数学的实践与认识, 2021, 51(6): 103-111.
	CHEN Lian, YUAN Mei, XIANG Wei, et al. Application of PCA-Fisher discriminant model in prediction of water inrush from coal seam floor[J]. Mathematics in Practice and Theory, 2021, 51(6): 103-111.
[20]	陈红江, 李夕兵, 刘爱华, 等. 用Fisher判别法确定矿井突水水源[J]. 中南大学学报(自然科学版), 2009, 40(4): 1114-1120.
	CHEN Hongjiang, LI Xibing, LIU Aihua, et al. Identifying of mine water inrush source by Fisher discriminant analysis method[J]. Journal of Central South University(Science and Technology), 2009, 40(4): 1114-1120.
[21]	张海桥. 海拉尔盆地红旗凹陷烃源岩评价及有利区预测[J]. 大庆石油地质与开发, 2020, 39(2): 21-27.
	ZHANG Haiqiao. Evaluation of the hydrocarbon source rock and prediction of the favorable zone in Hongqi Sag of Hailar Basin[J]. Petroleum Geology & Oilfield Development in Daqing, 2020, 39(2): 21-27.
[22]	王健, 石万忠, 舒志国, 等. 富有机质页岩TOC含量的地球物理定量化预测[J]. 石油地球物理勘探, 2016, 51(3): 596-604.
	WANG Jian, SHI Wanzhong, SHU Zhiguo, et al. TOC content quantitative prediction in organic-rich shale[J]. Petroleum Geophysical Exploration, 2016, 51(3): 596-604.
[23]	PASSEY Q R, CREANEY S, KULLA J B, et al. Practical model for organic richness from porosity and resistivity logs[J]. AAPG Bulletin, 1990, 74(12): 1777-1794.
[24]	SCHMOKER J W. Determination of organic content of Appalachian Devonian shales from formation-density logs[J]. AAPG Bulletin, 1979, 63(9): 1504-1537.
[25]	陈钢花, 梁莎莎, 王军, 等. 卷积神经网络在岩性识别中的应用[J]. 测井技术, 2019, 43(2): 130-135.
	CHEN Ganghua, LIANG Shasha, WANG Jun, et al. Application of convolutional neural Network in lithology identification[J]. Well Logging Technology, 2019, 43(2): 130-135.
[26]	刘巍, 刘威, 谷建伟, 等. 利用卡尔曼滤波和人工神经网络相结合的油藏井间连通性研究[J]. 油气地质与采收率, 2020, 27(2): 118-124.
	LIU Wei, LIU Wei, GU Jianwei, et al. Research on interwell connectivity of oil reservoirs based on Kalman filter and artificial neural network[J]. Petroleum Geology and Recovery Efficiency, 2020, 27(2): 118-124.

岩性分类/种	数量/个	自然伽马/ API	地层真电阻率/ （Ω·m）	声波时差/ (μs/ft)	黏土含量/%	灰岩含量/%	砂岩含量/%	岩性
5	146	83.8	3.63	96.3	57	23	20	含灰泥岩
6	606	82.3	3.11	101.1	63	21	16	含灰泥岩
2	354	70.0	5.21	127.1	18	53	29	含泥灰岩
7	540	72.2	6.87	97.1	23	56	21	含泥灰岩
3	91	62.9	1.31	81.0	14	78	8	灰岩
8	80	78.6	4.65	98.2	59	29	12	灰质泥岩
10	176	79.1	4.57	93.7	57	26	17	灰质泥岩
9	300	90.5	2.56	105.1	82	10	8	泥岩
4	207	78.7	1.20	91.6	26	58	16	泥质灰岩
1	534	75.9	2.07	97.6	28	51	21	泥质灰岩

A new method of shale oil facies element logging evaluation and its application in Dongying Sag

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 26

Related Articles 15

Metrics

Comments

Recommended 10

[1]	WANG Xin,HAN Jianqiang,ZAN Ling,LI Xiaolong,PENG Xingping. Logging evaluation of shale oil in the second member of Funing Formation of Qintong Sag, Subei Basin [J]. Petroleum Reservoir Evaluation and Development, 2024, 14(3): 364-372.
[2]	SUN Yaxiong,ZHU Xiangyu,QIU Xuming,LIU Qidong,DUAN Hongliang,QIU Yongfeng,GONG Lei. Characteristics of shale fractures in the second member of Funing Formation in Gaoyou Sag of Subei Basin [J]. Petroleum Reservoir Evaluation and Development, 2024, 14(3): 414-424.
[3]	ZANG Suhua,JING Xiaoming,LIU Zhihua,YIN Yanling. Geological conditions for shale oil formation in the fourth member of Funing Formation of Eocene series in Jintan Basin [J]. Petroleum Reservoir Evaluation and Development, 2024, 14(3): 425-434.
[4]	GAO Quanfang,ZHANG Peixian,GUAN Linlin,LI Yanjing,NI Feng. Influence of lower-level reverse faults on shale gas enrichment and high yield: A case study of Pingqiao Dong-1 Fault in Nanchuan area, southeast margin of Sichuan Basin [J]. Petroleum Reservoir Evaluation and Development, 2024, 14(3): 458-467.
[5]	ZHOU Feng,HUANG Shilin,LI Xiaoming,LIAO Kaigui,LI Yong. Quantitative evaluation of tight gas reservoir classification based on analytic hierarchy process: A case study of Penglaizhen Formation gas reservoir in Xinchang Gas Field [J]. Petroleum Reservoir Evaluation and Development, 2024, 14(3): 468-474.
[6]	ZOU Yi,LIU Chengchuan,DENG Meizhou,NIU Na,HAN Zhiying. “Four-properties” relationship and application in the fifth member of Xujiahe Formation in Ziyang-Dongfengchang Block of central Sichuan [J]. Petroleum Reservoir Evaluation and Development, 2024, 14(3): 484-491.
[7]	XUE Gang,GUO Tao,ZHANG Ye,XU Xiangyang,WANG Wei,HAN Kening,GUO Dongxin,JIN Xiaobo. Analysis of general geological conditions of coalbed methane in coal seam C₂₅ of Permian Longtan Formation, south Chongqing [J]. Petroleum Reservoir Evaluation and Development, 2024, 14(3): 492-503.
[8]	MA Daixin,REN Xianjun,ZHAO Mifu,HAN Jiaoyan,LIU Yuhu. Theories, technologies and practices of exploration and development of volcanic gas reservoirs: A case study of Cretaceous volcanic rocks in Songnan fault depression [J]. Petroleum Reservoir Evaluation and Development, 2024, 14(2): 167-175.
[9]	LI Ruilei,CAO Lei,FAN Xuepei,FENG Xiaohui,LI Ning. Comparison, optimization and application of multiple prestack inversion algorithm for intermediate basic volcanic reservoirs: A case study of Huoshiling Formation in Chaganhua area [J]. Petroleum Reservoir Evaluation and Development, 2024, 14(2): 207-215.
[10]	SHEN Yanjie,LI Junru,ZHANG Liya,ZHOU Yang,CHENG Rihui. Volcanic facies development characteristics of Yingcheng Formation in Songliao Basin: A case study of field outcrops in Jiutai area of Jilin Province [J]. Petroleum Reservoir Evaluation and Development, 2024, 14(2): 224-236.
[11]	ZHANG Wen,LIANG Lixi,LIU Xiangjun,XIONG Jian,ZHANG Yinan. Etching morphology and mechanical properties of carbonate rocks under acid action [J]. Petroleum Reservoir Evaluation and Development, 2024, 14(2): 247-255.
[12]	CHEN Hongcai, LI Zhaorui. Seismic prediction technology for thin sandstone reservoir of Dainan Formation in Majiazui Oilfield [J]. Petroleum Reservoir Evaluation and Development, 2024, 14(1): 108-116.
[13]	HE Dongbo, REN Lu, HAO Jie, LIU Xiaoping, CAO Qian. Quantitative evaluation system of geothermal resources based on analytic hierarchy process: A case study of middle-deep hydrothermal sandstone reservoir in Caofeidian of Hebei Province [J]. Petroleum Reservoir Evaluation and Development, 2023, 13(6): 713-725.
[14]	YAN Lini, ZHU Hongquan, YE Sujuan, ZHU Li. Origin of “cake-like” fractures and its significance for gas exploration in the second member of Xujiahe Formation of Xinchang structural belt [J]. Petroleum Reservoir Evaluation and Development, 2023, 13(5): 559-568.
[15]	ZHENG Gongying, LYU Qibiao, YANG Yongjian, XU Shoucheng. Prestack seismic prediction of sandstone reservoirs in the fifth member of Xujiahe Formation in Dongfengchang area of Ziyang [J]. Petroleum Reservoir Evaluation and Development, 2023, 13(5): 569-580.