Risk prediction for hydrogen sulfide emission based on sulfate-reducing bacteria in the water flooding oilfield

硫化氢 硫酸盐还原菌 物理 洪水(心理学) 水驱 硫酸盐 石油工程 环境化学 硫黄 冶金 化学 心理学 材料科学 量子力学 工程类 心理治疗师
作者
Hongyu Sun,Liguo Zhong,Yu Zhu,Jianjian Zhu,Yangyang Zhou
出处
期刊:Physics of Fluids [American Institute of Physics]
卷期号:36 (5) 被引量:4
标识
DOI:10.1063/5.0210061
摘要

The water quality of the injection–production systems deteriorates as the water flooding oilfields are developed more deeply, and the content of sulfate-reducing bacteria (SRB) increases. Accordingly, hydrogen sulfide (H2S) emission related to SRB is intensified, which will arise safety and health problems. In order to investigate the effect of SRB on H2S emission in the water flooding oilfield, the contents of SRB and sulfide of the different nodes of a typical injection–production system of Daqing Oilfield were measured first, and then, H2S emission from water was simulated under different conditions. Consequently, a H2S emission prediction model was established based on Henry coefficient and the correlation between sulfide content and SRB content in the water. The measured sulfide contents were ranging from 0.25 to 6.34 mg/l, and the SRB contents were from 2.5 to 25 000 pcs/ml, and the highest SRB and sulfide contents were found in the settling tank. The correlation between sulfide content and SRB content was much remarkable, and the R2 value of the correlation analysis was 0.94. Henry coefficient of H2S emission was obtained from the simulated experiments under varied conditions such as sulfate content, oil content, and temperature. The established H2S emission prediction model was much reliable for predicting H2S emission for water flooding injection–production system, and the accuracy of the predicted H2S emission of four nodes of the injection–production system was larger than 95% compared to the measured results. This study provides theoretical guidance for predicting H2S emission risks in water flooding injection–production systems.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
小草三心发布了新的文献求助10
1秒前
深情安青应助che采纳,获得10
1秒前
2秒前
科研小白发布了新的文献求助10
2秒前
2秒前
丰富若烟发布了新的文献求助20
3秒前
ZZZzzz发布了新的文献求助30
3秒前
感动傀斗发布了新的文献求助10
3秒前
3秒前
香蕉觅云应助甲乙采纳,获得10
3秒前
科研通AI6.3应助向往采纳,获得30
4秒前
zuoyueyue发布了新的文献求助10
4秒前
慕青应助酷炫香芦采纳,获得10
4秒前
空白的黑发布了新的文献求助10
4秒前
5秒前
5秒前
游云完成签到,获得积分10
5秒前
6秒前
bobo发布了新的文献求助10
6秒前
巫马婷冉完成签到,获得积分10
6秒前
6秒前
6秒前
7秒前
luxiaoyu发布了新的文献求助10
7秒前
CodeCraft应助棉花糖采纳,获得10
7秒前
天天快乐应助lynn采纳,获得10
7秒前
111完成签到,获得积分10
7秒前
Yan完成签到,获得积分10
8秒前
8秒前
orixero应助yang采纳,获得10
8秒前
端庄毛巾发布了新的文献求助10
8秒前
8秒前
8秒前
在水一方应助小草三心采纳,获得10
9秒前
9秒前
巫马婷冉发布了新的文献求助10
10秒前
SciGPT应助zzb采纳,获得10
10秒前
追寻的幻巧完成签到,获得积分10
10秒前
夜无疆发布了新的文献求助10
10秒前
cookie完成签到,获得积分10
10秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Relation between chemical structure and local anesthetic action: tertiary alkylamine derivatives of diphenylhydantoin 1000
Signals, Systems, and Signal Processing 610
Discrete-Time Signals and Systems 610
Principles of town planning : translating concepts to applications 500
Iron‐Sulfur Clusters: Biogenesis and Biochemistry 400
Healable Polymer Systems: Fundamentals, Synthesis and Applications 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 纳米技术 有机化学 物理 生物化学 化学工程 计算机科学 复合材料 内科学 催化作用 光电子学 物理化学 电极 冶金 遗传学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 6070346
求助须知:如何正确求助?哪些是违规求助? 7902121
关于积分的说明 16336561
捐赠科研通 5211097
什么是DOI,文献DOI怎么找? 2787211
邀请新用户注册赠送积分活动 1770002
关于科研通互助平台的介绍 1648037