塔里木盆地新和地区低幅度构造油气微生物特征及有利目标区预测

闫亮; 贾宝迁; 季苗; 陈晓彤; 高平

doi:10.11781/sysydz2020061001

塔里木盆地新和地区低幅度构造油气微生物特征及有利目标区预测

doi: 10.11781/sysydz2020061001

闫亮^1,,
贾宝迁²,
季苗^{2, 3},
陈晓彤²,
高平²

1.
中国石化石油物探技术研究院, 南京 211103
2.
中国科学院微生物研究所, 北京 100101
3.
中国科学院大学, 北京 100049

基金项目:

国家科技重大专项 2017ZX05005001-009

中石化科技部项目 P18095-4

详细信息

作者简介:
闫亮(1984-), 男, 硕士, 副研究员, 从事石油地质综合研究及储层预测研究。E-mail: yanl.swty@sinopec.com

中图分类号: TE132.4
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- 被引次数: 0
出版历程
- 收稿日期: 2020-03-12
- 修回日期: 2020-10-14
- 刊出日期: 2020-11-28

Microbial characteristics of low-amplitude structures and prediction of favorable target areas in Xinhe area, Tarim Basin

1.
SINOPEC Geophysical Research Institute, Nanjing, Jiangsu 211103, China
2.
Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
3.
University of Chinese Academy of Sciences, Beijing 100049, China

摘要

摘要: 对塔里木盆地新和地区油气微生物异常特征进行了研究，为该地区油气预测和油气微生物勘探技术的推广和应用提供依据。主要采用平板培养计数法、16SrRNA基因克隆文库法与高通量测序法对土壤样品中的油气微生物进行定量检测，利用这些方法研究了新和地区油气微生物种群构成及数量异常情况，构建出研究区油气区的油气微生物异常模式。以此为依据，对新和及三道桥地区低幅度圈闭线索区域的油气微生物进行检测，根据油气微生物异常特征预测油气有利区。最终识别出5个微生物异常区，并结合构造圈闭认识结果及油气微生物数量异常，优选出5个潜在的有利勘探目标区。
- 甲烷氧化菌 /
- 丁烷氧化菌 /
- 平板培养法 /
- 高通量测序 /
- 油气微生物勘探 /
- 新和地区 /
- 塔里木盆地
Abstract: The Microbial Prospecting for Oil and Gas (MPOG) is an exploration method based on the seepage of light gaseous hydrocarbons from oil/gas reservoirs to the surface and their utilization by hydrocarbon oxidizing bacteria. By studying the abnormal characteristics of oil and gas microorganisms in Xinhe area of the Tarim Basin, this project provides a solid scientific basis for oil and gas prediction and the validation and application of oil and gas microbial exploration technology in this area. Soil samples were collected to investigate prospects for hydrocarbon exploration. The amount of cultivable soil microorganisms in different soil samples were analyzed using the agar plating method. High-throughput sequencing has been used directly to study the composition of microbial population in soil samples. Based on the data from Xinhe area, an abnormal pattern of oil and gas microorganisms has been established. By using this pattern, five abnormal microbial areas were identified in the Xinhe and Sandaoqiao area. Combined with the results of structural traps and the abnormal number of hydrocarbon microorganisms, five potential favorable exploration target areas were identified.
- methane-oxidizing bacteria /
- butane-oxidizing bacteria /
- plating method /
- high-throughput sequencing /
- Microbial Prospecting of Oil and Gas (MPOG) /
- Xinhe area /
- Tarim Basin

HTML全文

图 1 塔里木盆地新和地区位置及采样点分布

Figure 1. Sample distribution map in Xinhe area, Tarim Basin

下载: 全尺寸图片幻灯片

图 2 塔里木盆地新和地区XH5井东北向采样线油藏剖面与微生物数量异常对应关系

圈闭位置见图 1。

Figure 2. NE-oriented geological section crossing well XH5 in Xinhe area of Tarim Basin and correspondence with microbial quantity

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图 3 塔里木盆地新和地区土壤样品物种累计曲线

Figure 3. Species accumulation curve in Xinhe area, Tarim Basin

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图 4 塔里木盆地新和地区物种属水平丰度聚类热图

Figure 4. Heat map of species abundance at the genus level in Xinhe area, Tarim Basin

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图 5 塔里木盆地新和地区物种主坐标分析(PCoA)

Figure 5. Principal Component Analysis at genus level in Xinhe area, Tarim Basin

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图 6 塔里木盆地新和地区物种树状图(UPGMA/PGMA)

Figure 6. Unweighted (a) or weighted (b) pair group method with arithmetic mean, Xinhe area, Tarim Basin

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图 7 塔里木盆地新和地区XH5井东北方油气异常区(预测)

Figure 7. Predicted oil and gas anomalous area to the northeast of well XH5 in Xinhe area, Tarim Basin

下载: 全尺寸图片幻灯片

图 8 塔里木盆地三道桥地区油气异常区(预测)

Figure 8. Predicted oil and gas anomalous area in Sandaoqiao

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表 1 塔里木盆地新和地区采样点信息

Table 1. Sample information in Xinhe area, Tarim Basin

采样点	油气成藏	圈闭(位置见图 1)	地表环境	高通量分组
1-9号	近XH5井，油气已知区	圈闭0	农田	XH-y
10~13号	油气预测区	圈闭1	沙漠	XH-s
14~18号，44~48号	油气预测区	圈闭2	沙漠	XH-s
34~43号	油气预测区	圈闭3	果树	XH-gs
19~21号	油气预测区	圈闭4	农田	XH-nt
22~30号	油气预测区	圈闭5	农田	XH-nt
31~33号	油气预测区	圈闭6	农田	XH-nt

下载: 导出CSV

表 2 塔里木盆地新和地区微生物异常样品点及异常值

Table 2. Abnormal sample points and abnormal values in Xinhe area, Tarim Basin

高异常样品点号	数量异常值	种群异常值	异常值总和	异常样品点号	数量异常值	种群异常值	异常值总和
11	1.025	-0.012	1.013	12	0.675	-0.025	0.650
27	1.565	-0.153	1.412	35	0.585	-0.036	0.549
40	2.145	-0.347	1.798	32	1.525	-0.824	0.701
46	1.845	-0.326	1.519

下载: 导出CSV

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