渤海湾盆地渤中凹陷西南部深层烃源岩有机相预测

王祥; 马劲风; 王飞龙; 王震亮; 陈容涛; 闫昕宇

doi:10.11781/sysydz2022061070

渤海湾盆地渤中凹陷西南部深层烃源岩有机相预测

doi: 10.11781/sysydz2022061070

王祥^{1, 2, 3,},
马劲风^{1, 2, 3, ,},
王飞龙⁴,
王震亮^{1, 2, 3},
陈容涛⁴,
闫昕宇^{1, 3}

1.
西北大学地质学系，西安 710069
2.
二氧化碳捕集与封存技术国家地方联合工程研究中心，西安 710069
3.
大陆动力学国家重点实验室，西安 710069
4.
中海石油(中国)有限公司天津分公司勘探开发研究院，天津 300452

基金项目:

中海石油(中国)有限公司天津分公司课题“渤中凹陷深层成烃成藏机理与规律研究” CCL2018TJTZDST0579

详细信息

作者简介:
王祥(1997—)，男，硕士研究生，从事烃源岩地球物理预测研究。E-mail：wangxiang1@stumail.nwu.edu.cn

通讯作者:
马劲风(1965—)，男，教授，博士生导师，从事地震勘探和储层预测等研究。E-mail：jinfengma@sina.com

中图分类号: TE122.113
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出版历程
- 收稿日期: 2021-07-29
- 修回日期: 2022-09-28
- 刊出日期: 2022-11-28

Prediction of organic facies of deep source rocks in southwestern part of Bozhong Sag, Bohai Bay Basin

WANG Xiang^{1, 2, 3
,},
MA Jinfeng^{1, 2, 3
, ,},
WANG Feilong⁴,
WANG Zhenliang^{1, 2, 3},
CHEN Rongtao⁴,
YAN Xinyu^{1, 3}

1.
Department of Geology, Northwest University, Xi'an, Shaanxi 710069, China
2.
National & Local Joint Engineering Research Center of Carbon Capture and Storage Technology, Xi'an, Shaanxi 710069, China
3.
State Key Laboratory of Continental Dynamics, Xi'an, Shaanxi 710069, China
4.
Exploration and Development Research Institute, Tianjin Branch of CNOOC, Tianjin 300452, China

摘要

摘要: 针对渤海湾盆地渤中凹陷西南部烃源岩层系多、非均质性较强等特点，开展了烃源岩干酪根显微组分、元素分析、岩石热解、气相色谱等地球化学分析测试，在沉积相的约束下对比了ΔlogR系列方法、多元回归法、BP神经网络法预测有机质丰度参数的优劣，优选BP神经网络法进行烃源岩总有机碳含量、裂解烃含量的单井预测，进而计算得到氢指数预测曲线，结合总有机碳含量进行有机相分析，定量刻画各层系烃源岩的有机相，分析烃源岩的优劣和展布特征。结果表明，BP神经网络法的预测精度相对较高，东营组二段下亚段烃源岩主要为Ⅱ₂型干酪根，氢指数为125~400 mg/g，总有机碳含量基本小于3%，其有机相主要为BC相、C相和CD相；东营组三段烃源岩主要为Ⅱ₁—Ⅱ₂型干酪根，沙河街组沙一二段、沙三段烃源岩主要为Ⅰ—Ⅱ₁型干酪根，这三套烃源岩的氢指数为250~650 mg/g，总有机碳含量为3%左右或大于3%，其有机相主要为B相、BC相和C相。沙河街组优质烃源岩主要发育于研究区中南部，是下一步勘探的重点区域。
- 总有机碳含量 /
- ΔlogR方法 /
- 有机相 /
- 烃源岩 /
- BP神经网络 /
- 渤中凹陷 /
- 渤海湾盆地
Abstract: Considering the characteristics of abundant source rock formations and heterogeneity in the southwestern part of the Bozhong Sag, Bohai Bay Basin, based on the geochemical and petrographic analysis of source rocks, the methods of ΔlogR series, multiple regression and BP neural network are applied to predict the organic matter abundance under the constraints of sedimentary facies.The BP neural network method is preferably used to predict the total organic carbon content and cracked hydrocarbon content of source rocks in a single well, and then the hydrogen index prediction curve is calculated.Combined with total organic carbon content, the organic phase of each source rock layer is quantitatively described, and the advantages and disadvantages and distribution characteristics of source rocks are analyzed. Results show that the prediction accuracy of BP neural network method is relatively higher. The source rocks in the lower section of the second member of Dongying Formation are featured by kerogen of type Ⅱ₂, hydrocarbon index of 125-400 mg/g, TOC content basically less than 3%, and organic facies of BC, C and CD types. The source rocks in the third member of Dongying Formation are featured by kerogen of typesⅡ₁ to Ⅱ₂. The source rocks in the first, second and third members of Shahejie Formation are featured by kerogen of types Ⅰ to Ⅱ₁, TOC content about 3% or higher than 3%, and organic facies of B, BC and C types.The high-quality source rocks of the Shahejie Formation are mainly developed in the central and southern parts of the study area, and are the key areas for further exploration.
- total organic carbon content /
- ΔlogR method /
- organic facies /
- source rock /
- BP neural network /
- Bozhong Sag /
- Bohai Bay Basin

HTML全文

图 1 渤海湾盆地渤中凹陷区域概况(a)、研究区位置(b)及地层综合柱状图(c)

据文献[19, 37]修改。

Figure 1. Regional overview of Bozhong Sag (a), location of study area (b), and generalized stratigraphic histogram (c), Bohai Bay Basin

下载: 全尺寸图片幻灯片

图 2 渤海湾盆地渤中凹陷西南部烃源岩干酪根显微组分(a)、干酪根H/C原子比和O/C原子比(b)、TOC含量(c)和Pr/nC₁₇和Ph/nC₁₈值相关图(d)

Figure 2. Microscopic composition of kerogen (a), H/C and O/C ratio of kerogen (b), TOC content (c), and correlation between Pr/nC₁₇ and Ph/nC₁₈ (d) of source rocks from southwestern part of Bozhong Sag, Bohai Bay Basin

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图 3 渤海湾盆地渤中凹陷西南部烃源岩岩石热解氢指数、总有机碳含量、生烃潜力交会图

Figure 3. Intersection of pyrolysis hydrogen index, TOC content and S₁+S₂ of source rocks from southwestern part of Bozhong Sag, Bohai Bay Basin

下载: 全尺寸图片幻灯片

图 4 渤海湾盆地渤中凹陷西南部烃源岩测井参数与TOC、S₂交会图

Figure 4. Intersection of logging parameters and TOC and S₂ of source rocks from southwestern part of Bozhong Sag, Bohai Bay Basin

下载: 全尺寸图片幻灯片

图 5 三层BP神经网络预测TOC、S₂原理

Figure 5. Schematic diagram of TOC and S₂ prediction by three-layer BP neural network

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图 6 四种方法预测TOC判定系数R²、平均相对误差分析

Figure 6. Coefficient R² and average relative error of four methods for TOC prediction

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图 7 渤海湾盆地渤中凹陷西南部Q3井TOC、S₂、I_H预测与实测综合地球化学剖面

Figure 7. Generalized geochemical profiles showing predicted and measured TOC, S₂, and I_H values of well Q3, southwestern part of Bozhong Sag, Bohai Bay Basin

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图 8 渤海湾盆地渤中凹陷西南部烃源岩沉积相和各有机相分布

a.E₃d₂^L沉积相；b.E₃d₃沉积相；c.E₂s₁₊₂沉积相；d.E₂s₃沉积相；e.E₃d₂^LC相；f.E₃d₃B相；g.E₂s₁₊₂B相；h.E₂s₃B相；i.E₃d₂^LCD相；j.E₃d₃BC相；k.E₂s₁₊₂BC相；l.E₂s₃BC相

Figure 8. Sedimentary facies and organic facies distribution of source rocks in southwestern part of Bozhong Sag, Bohai Bay Basin

下载: 全尺寸图片幻灯片

表 1 有机相分类

Table 1. Classification of organic facies

表 2 渤海湾盆地渤中凹陷西南部各TOC预测方法判定系数R²对比

Table 2. Coefficient R² calculated by various methods for TOC prediction, southwestern part of Bozhong Sag, Bohai Bay Basin

层位	沉积相	ΔlogR法	广义ΔlogR法	多元回归法	BP神经网络法
E₃d₂^L	辫状河三角洲	0.712 3	0.722 7	0.818 1	0.933 5
	滨浅湖	0.780 6	0.800 3	0.581 4	0.878 9
	半深湖—深湖	0.008 8	0.321 5	0.322 3	0.839 2
	湖底扇	0.220 0	0.592 4	0.609 6	0.890 2
E₃d₃	滨浅湖	0.550 6	0.818 1	0.719 8	0.804 1
E₃d₃	半深湖—深湖	0.346 0	0.580 2	0.705 9	0.891 1
E₂s₁₊₂	滨浅湖	0.312 5	0.469 5	0.347 7	0.862 5
E₂s₃	半深湖—深湖	0.215 3	0.433 4	0.325 5	0.743 2
E₂s₃	辫状河三角洲	0.558 3	0.693 2	0.700 9	0.811 3

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