三塘湖盆地条湖凹陷八道湾组煤焦油产率及富油性预测

武鸿飞; 李鑫; 王兴刚; 焦立新; 曹志雄; 李斌; 韦波; 冯烁

doi:10.13568/j.cnki.651094.651316.2025.02.08.0001

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三塘湖盆地条湖凹陷八道湾组煤焦油产率及富油性预测

新能源与绿色生产·庆祝新疆维吾尔自治区成立70周年专题 | 更新时间：2026-01-23

- 三塘湖盆地条湖凹陷八道湾组煤焦油产率及富油性预测
- 三塘湖盆地条湖凹陷八道湾组煤焦油产率及富油性预测
- 新疆大学学报（自然科学版中英文） 2025年42卷第3期页码：312-322
- 作者机构：
  
  1. 新疆大学地质与矿业工程学院
  2. 中国石油吐哈油田分公司勘探开发研究院
- 作者简介：
- 基金信息：
  
  新疆维吾尔自治区天山英才计划“新疆富油煤地质成因与薄层富油煤原位热解提取煤基油气基础研究”（2022TSYCLJ0021）;新疆维吾尔自治区科技重大专项“利用二氧化碳提高新疆难开采煤层气采收率技术体系研发”（2022A03015-3）
- DOI：10.13568/j.cnki.651094.651316.2025.02.08.0001
  中图分类号： P631.81
- 纸质出版：2025
- 稿件说明：
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[1]武鸿飞,李鑫,王兴刚,等.三塘湖盆地条湖凹陷八道湾组煤焦油产率及富油性预测[J].新疆大学学报(自然科学版中英文),2025,42(03):312-322.
[1]武鸿飞,李鑫,王兴刚,等.三塘湖盆地条湖凹陷八道湾组煤焦油产率及富油性预测[J].新疆大学学报(自然科学版中英文),2025,42(03):312-322. DOI： 10.13568/j.cnki.651094.651316.2025.02.08.0001.

DOI：10.13568/j.cnki.651094.651316.2025.02.08.0001.

摘要

富油煤集煤、油、气属性为一体，促进其勘探开发对保障我国油气资源供应、实现煤炭清洁高效利用具有重要战略价值．为此，以三塘湖盆地条湖凹陷煤样焦油产率、工业组分、元素分析、煤岩分析数据为基础，结合测井响应，建立了富油煤焦油产率测井预测模型，预测研究区八道湾组富油煤资源量．结果表明：煤焦油产率与挥发分产率、氢元素含量、镜质组含量呈正相关关系，与灰分产率、惰质组含量呈负相关关系；挥发分产率、氢元素含量、镜质组含量与声波时差、补偿密度测井值呈较好的负相关关系，与自然伽马测井值相关性较差．此外，建立了基于机器学习的煤焦油产率预测模型，预测值与实际值的相关系数为0.92

90%煤样的焦油产率预测结果相对误差小于20%

75%煤样的相对误差小于15%.

Abstract

Tar-rich coal integrates the properties of coal

oil and gas. Promoting its exploration and development has important strategic value for ensuring the supply of oil and gas resources in China

and realizing the clean and efficient utilization of coal. Therefore

based on the data of tar yield

industrial components

elemental analysis and coal petrography analysis of coal samples in Tiaohu sag of Santanghu Basin

combined with logging response

a logging prediction model of tar-rich coal tar yield is established

and the tar-rich coal resources of Badaowan formation in the study area are predicted. The results show that: the yield of coal tar is positively correlated with volatile yield

hydrogen content and vitrinite content

and negatively correlated with ash yield and inertinite content. There is a good negative correlation between volatile yield

hydrogen content

vitrinite content and acoustic time difference

compensated density logging values

and a poor correlation with natural gamma logging values. In addition

the prediction model of coal tar yield based on machine learning is established. The correlation coefficient between the predicted value and the actual value is 0.92. The relative error of tar yield prediction results of 90% coal samples is less than 20%

and the relative error of 75% coal samples is less than 15%.

关键词

Keywords

references

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