鄂尔多斯盆地延安气田深部煤层气甲烷碳同位素分馏机理及其指示意义

万永平; 王振川; 乔钰; 韩双彪; 马冬; 黄劼; 边疆; 刘宇; 乔良

doi:10.7623/syxb202604005

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鄂尔多斯盆地延安气田深部煤层气甲烷碳同位素分馏机理及其指示意义

地质勘探 | 更新时间：2026-08-04

- 鄂尔多斯盆地延安气田深部煤层气甲烷碳同位素分馏机理及其指示意义
- Carbon isotope fractionation mechanism of deep coalbed methane in Yan’an gas field of Ordos Basin and its indication significance
- 石油学报 2026年47卷第4期页码：791-813
- 作者机构：
  
  1. 陕西延长石油(集团)有限责任公司气田公司,陕西,延安,716000
  2. 中国矿业大学(北京)地球科学与测绘工程学院,北京,100083
- 作者简介：
- 基金信息：
- DOI：10.7623/syxb202604005
  中图分类号： TE135.1
- 网络首发：2026-05-11，
  
  纸质出版：2026
- 稿件说明：
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万永平, 王振川, 乔钰, 韩双彪, 马冬, 黄劼, 边疆, 刘宇, 乔良. 鄂尔多斯盆地延安气田深部煤层气甲烷碳同位素分馏机理及其指示意义[J]. 石油学报, 2026, 47(4): 791-813.

Wan Yongping, Wang Zhenchuan, Qiao Yu, et al. Carbon isotope fractionation mechanism of deep coalbed methane in Yan’an gas field of Ordos Basin and its indication significance[J]. ACTA PETROLEI SINICA, 2026, 47(4): 791-813.
万永平, 王振川, 乔钰, 韩双彪, 马冬, 黄劼, 边疆, 刘宇, 乔良. 鄂尔多斯盆地延安气田深部煤层气甲烷碳同位素分馏机理及其指示意义[J]. 石油学报, 2026, 47(4): 791-813. DOI： 10.7623/syxb202604005.

Wan Yongping, Wang Zhenchuan, Qiao Yu, et al. Carbon isotope fractionation mechanism of deep coalbed methane in Yan’an gas field of Ordos Basin and its indication significance[J]. ACTA PETROLEI SINICA, 2026, 47(4): 791-813. DOI： 10.7623/syxb202604005.

摘要

面向鄂尔多斯盆地东南缘延安气田深部煤层气勘探开发需求

聚焦高温高压条件下甲烷碳同位素分馏机制这一关键科学问题

通过多方法融合研究

揭示了深部煤层气中甲烷的赋存与运移规律

为深层煤层气资源评价与高效开发提供理论支撑。利用分子模拟手段

探究了甲烷在不同纳米尺度及不同温压条件下的吸附-扩散行为

厘清了甲烷碳同位素分馏的微观机制(如扩散速率差异、吸附能垒效应)

进一步探讨了甲烷的赋存状态、富集特征和流动产出规律。研究结果表明:①深部煤在解吸过程中的甲烷碳同位素分馏主要受控于煤的孔隙结构、气体赋存以及气体传输过程中的动力学特征;②在深部煤样品现场解吸的煤层气中

甲烷的碳同位素值(δ

)普遍较重

碳同位素分馏过程呈现3种模式;③分子模拟证实

微孔中的CH

吸附性能差异与热运动动能增强效应共同影响着碳同位素分馏;④深部煤层气井井口气样的δ

(-31.46 ‰~-30.68 ‰)的微弱变化与岩心解吸数据的强分馏特征形成鲜明对比

揭示储层内部多尺度运移路径的耦合效应。通过对比现场实测数据和分子模拟结果发现

二者在碳同位素分馏特征上具有高度一致性。这一结果验证了分子模拟模型的可靠性

同时也为深部煤层气的地质研究提供了新的技术手段。

Abstract

This study addresses the exploration and development requirements for deep coalbed methane (CBM) in Yan’an gas field on the southeastern margin of Ordos Basin. It focuses on the critical scientific issue regarding methane carbon isotope fractionation mechanisms under high-temperature and high-pressure conditions. Through the integration of various research methods

the study reveals the o

ccurrence and migration patterns of methane in deep CBM

providing theoretical support for the evaluation and high-efficient development of deep CBM resources. Molecular simulation techniques were utilized to investigate the adsorption-diffusion behavior of methane across nanoscales under various temperature and pressure conditions

and thus clarify the microscopic mechanisms of isotope fractionation

such as differences in diffusion rates and adsorption energy barrier effects. Further

the study explores the occurrence state

enrichment characteristics

and flow and emission patterns of methane gas. The results show as follows. (1) Methane carbon isotope fractionation during the desorption process of deep coal is primarily controlled by the coal’s pore structure

gas occurrence

and the kinetic characteristics observed during the gas transport process. (2) In CBM liberated through on-site desorption of deep coal samples

significant

C enrichment was commonly observed in methane carbon isotope value (δ

)

and the isotope fractionation process presents three distinct patterns. (3) Molecular simulations confirm that carbon isotope fractionation is controlled by both the differential CH

adsorption capacities in micropores and the kinetic-energy enhancement via thermal motion. (4) The slight variations (-31.46 ‰ to -30.68 ‰) in δ

of gas samples from deep coal seam wells contrasts sharply with the strong fractionation characteristics as demonstrated by core desorption data

highlighting the coupling effects of multi-scale migration pathways within the reservoir. A comparison between the field-measured data and molecular simulation results reveals strong consistency in the carbon isotope fractionation characteristics. This finding not only validates the reliability of the molecular simulation model

but also introduces a new technical approach for the geological research of deep CBM.

关键词

Keywords

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