油气藏CO2封存潜力评估模型与实践进展

doi:10.13809/j.cnki.cn32-1825/te.2025357

油气藏评价与开发 ›› 2026, Vol. 16 ›› Issue (1): 141-152.doi: 10.13809/j.cnki.cn32-1825/te.2025357

油气藏CO₂封存潜力评估模型与实践进展

李经纬¹^,²(), 彭勃¹^,²(), 王泽滕¹^,², 陈晓倩¹^,², 张正昊¹^,², 刘金栋¹^,², 刘双星³, 李晓枫⁴

^1.中国石油大学（北京）油气资源与工程全国重点实验室，北京 102249
^2.中国石油大学（北京）温室气体封存与石油开采利用北京市重点实验室，北京 102249
^3.中国石油安全环保技术研究院有限公司，北京 102206
^4.国家石油天然气管网集团有限公司科学技术研究总院分公司，天津 300450

收稿日期:2025-07-29 发布日期:2026-01-06 出版日期:2026-01-26
通讯作者: 彭勃（1969—），男，博士，教授，主要从事碳捕集、利用与封存和油气田化学与工程研究。地址：北京市昌平区府学路18号，邮政编码：102249。E-mail：cbopeng@cup.edu.cn
作者简介:李经纬（1994—），男，在读博士研究生，主要从事分子模拟及CO₂封存潜力评估研究。地址：北京市昌平区府学路18号，邮政编码：102249。E-mail：long-lat-itude@outlook.com
基金资助:
中国工程院重点项目“中国油气行业CCUS产业化发展战略研究”(CAS20200415);中国地质调查局油气资源调查中心“典型地区二氧化碳封存潜力主控参数处理”(2023080);国家能源集团重大研究项目“榆林化工百万吨级CCUS不同地质条件选址及关键技术研究”(GJNY-22-24);中国石油天然气股份有限公司-中国石油大学（北京）战略合作科技专项“准噶尔盆地玛湖中下组合和吉木萨尔陆相页岩油高效勘探开发理论及关键技术研究”(ZLZX2020-01-08)

CO₂ storage potential assessment models and their practical progress in oil and gas reservoirs

LI Jingwei¹^,²(), PENG Bo¹^,²(), WANG Zeteng¹^,², CHEN Xiaoqian¹^,², ZHANG Zhenghao¹^,², LIU Jindong¹^,², LIU Shuangxing³, LI Xiaofeng⁴

^1.State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing 102249, China
^2.Beijing Key Laboratory for Greenhouse Gas Storage and Enhanced Oil Recovery, China University of Petroleum (Beijing), Beijing 102249, China
^3.CNPC Research Institute of Safety & Environment Technology, Beijing 102206, China
^4.PipeChina Institute of Science and Technology, Tianjin 300450, China

Received:2025-07-29 Online:2026-01-06 Published:2026-01-26

摘要/Abstract

摘要：

CO₂地质封存潜力的定量计算是前期封存选址适宜性评估和后期量化核查的重要组成部分。油气藏作为封存的优选地质体，兼具提高采收率经济效益与CO₂封存环境效益。其中，具有良好圈闭结构的常规（枯竭）油气藏是经典碳封存潜力评估模型发展和应用的基础。而针对不同油藏条件、项目不同阶段场景以及充分考虑各种封存机制贡献的潜力评估模型仍需进一步更新。系统综述了油气藏CO₂封存机制及封存潜力的评估方法，并对模型的发展历程与应用实践进行分析总结。在封存机制方面，油藏以构造封存、残余封存、溶解封存和矿化封存为主；气藏则依赖压力补充、竞争吸附和重力分异协同作用。该研究梳理了20种主流潜力评估模型，根据模型基本原理将其划分为考虑地质储量与采收率、考虑封存可用储层孔隙空间、原始模型改进与拓展、新角度与新方法4个阶段。目前，模型仍以构造和残余封存为出发点，仅少数量化溶解与矿化贡献；输入参数精细化与不确定性量化成发展趋势；混相压力预测、溶解度计算、矿化反应动力学和气体竞争吸附的时空间演化机制仍需进一步探究。未来需耦合地质监测数据与人工智能技术并开发轻量化评估工具，支撑场地级工程决策，推动封存潜力评估从“理论可行”迈向“工程落地”。

关键词: 枯竭油气藏, CO₂-EOR（CO₂提高石油采收率）, CO₂-EGR（CO₂提高天然气采收率）, CO₂地质封存, 潜力评估

Abstract:

Quantitative calculation of CO₂ geological storage potential is a crucial component of preliminary storage site suitability assessment and subsequent quantitative verification. Oil and gas reservoirs are preferred geological formations for CO₂ storage, as they combine the economic benefits of enhanced recovery and the environmental benefits of carbon storage. Among them, conventional (depleted) oil and gas reservoirs with well-developed trap structures form the foundation for the development and application of classical carbon storage potential assessment models. However, models that account for different reservoir conditions, different project stages, and fully consider the contributions of various storage mechanisms still require further advancement. This paper systematically reviews the CO₂ storage mechanisms and storage potential assessment methods for oil and gas reservoirs, and analyzes and summarizes the development history and application practices of these models. In terms of storage mechanisms, oil reservoirs mainly involve structural, residual, solubility, and mineral trapping, whereas gas reservoirs rely on the synergistic effects of pressure replenishment, competitive adsorption, and gravitational segregation. Twenty mainstream potential assessment models are categorized into four stages based on their fundamental principles: models considering geological reserves and recovery factors, models considering available reservoir pore space for storage, improved and extended models derived from the original models, and models focusing on new perspectives and localized approaches. Current models still primarily emphasize structural and residual trapping, with only a few quantifying solubility and mineralization contributions. Refinement of input parameters and uncertainty quantification have become development trends. Further research is needed on miscibility pressure prediction, solubility calculation, mineralization reaction kinetics, and the spatiotemporal evolution mechanisms of competitive gas adsorption. In the future, integrating geological monitoring data and artificial intelligence technologies and developing lightweight assessment tools will be essential to support site-level engineering decisions and advance the transition of storage potential assessment from theoretical feasibility to engineering implementation.

Key words: depleted oil and gas reservoirs, CO₂-EOR (CO₂-enhanced oil recovery), CO₂-EGR (CO₂-enhanced gas recovery), CO₂ geological storage, potential assessment

中图分类号:

TE357

李经纬,彭勃,王泽滕, 等. 油气藏CO₂封存潜力评估模型与实践进展[J]. 油气藏评价与开发, 2026, 16(1): 141-152.

LI Jingwei,PENG Bo,WANG Zeteng, et al. CO₂ storage potential assessment models and their practical progress in oil and gas reservoirs[J]. Petroleum Reservoir Evaluation and Development, 2026, 16(1): 141-152.

图/表 9

图1

表1

基于物质平衡法的封存潜力评估模型汇总[18-22]"

模型编号	计算原理	参数含义
1	$E X T = 5.3 % (A P I ≤ 31) (1.3 A P I - 35) % (31 < A P I < 41) 18.3 % (A P I ≥ 41$ 驱油情景： $M C O 2 = V O O I P C E X T R C O 2$ 废弃情景： $M C O 2 = V O O I P R F o F V F o ρ r e s, C O 2$	$E X T$ 为额外提高采收率比例，%； $M C O 2$ 为封存量，单位t； $V O O I P$ 为原油地质储量，单位m³； $C$ 为CO₂原油接触比例，取值75%； $R C O 2$ 为换油率，单位t/t； $R F o$ 为废弃油藏的采收率，%； $F V F o$ 为原油体积系数，单位地下桶/地面桶； $ρ r e s, C O 2$ 为储层温压下CO₂密度，单位kg/m³
2	$M C O 2 = 0.75 a V O G I P R f R r e s, C O 2 C H 4 ρ s t d, C O 2$ $R r e s, C O 2 C H 4 = 2 × 10 - 7 D 2 - 0.001 5 D + 4.170 7$	$V O G I P$ 为原始天然气地质储量，单位m³； $a$ 为校正系数，取值1.0； $R f$ 为采收率，%； $R r e s, C O 2 C H 4$ 为气藏条件CO₂/CH₄摩尔比； $ρ s t d, C O 2$ 为标况CO₂密度，单位kg/m³，取值1.98kg/m³； $D$ 为气藏埋深，单位m
3	CO₂突破： $M C O 2 = ρ r e s, C O 2 R B T V O O I P / S h$ 注入HCPV： $M C O 2 = ρ r e s, C O 2 R B T + 0.6 R H C P V - R B T V O O I P / S h$	$R B T$ 为CO₂突破时采收率，%； $S h$ 为原油收缩率； $R H C P V$ 为注入HCPV时采收率，%
4	WAG： $V n e t, C O 2 = R W A G E W A G V O O I P E α, C O 2 B o / B g$ GSGI： $V n e t, C O 2 = R G S G I E G S G I V O O I P 0.7 B o / B g$	$V n e t, C O 2$ 为单位CO₂封存量，单位m³； $R W A G$ 为水气交替驱目标采收率增量，%； $E W A G$ 为WAG实施程度（0~1）； $E α, C O 2$ 为CO₂利用效率（1~2）； $B o$ 和 $B g$ 分别为石油、天然气形成系数； $R G S G I$ 为重力稳定气驱目标采收率增量，%； $E G S G I$ 为GSGI实施程度（0~1）
5	$M C O 2 = V s t d, C H 4 B f ρ r e s, C O 2 E$	$V s t d, C H 4$ 为标况下CH₄最终可采储量，单位m³； $B f$ 为储层体积系数，取值0.05； $E$ 为封存效率因子，取值0.75

表1

表2

基于有效容积法的封存潜力评估模型汇总[6， 23-26]"

模型编号	计算原理	参数含义
6	气藏： $M C O 2 = ρ r e s, C O 2 R f 1 - F I G V O G I P p s Z r T r / p r Z s T s]$ 油藏： $M C O 2 = ρ r e s, C O 2 (R f V O O I P / B f - V i w + V p w)$ $M C O 2 = ρ r e s, C O 2 [R f A h φ 1 - S w - V i w + V p w]$ $M e, C O 2 = C m C b C h C w C a M C O 2 = C e M C O 2$	$F I G$ 为注入气体体积分数； $p$ 为压力，单位Pa； $Z$ 为气体压缩系数； $T$ 为温度，单位℃；下标 $r$ 为储层条件；下标 $s$ 为地表条件； $B f$ 为储层体积系数； $V i w$ 和 $V p w$ 分别为注入和采出水体积，单位m³； $A$ 为储层面积，单位m²； $h$ 为储层厚度，单位m； $φ$ 为储层孔隙度，%； $S w$ 为束缚水饱和度； $M e, C O 2$ 是有效封存量，单位t； $C m$ 、 $C b$ 、 $C h$ 、 $C w$ 、 $C a$ 和 $C e$ 分别为流动性、浮力、非均质性、含水饱和度、邻近含水层强度和有效容量系数
7	$G C O 2 = A h n φ e 1 - S w i B f ρ s t d, C O 2 E$	$G C O 2$ 为封存量，单位t； $A$ 为储层面积，单位m²； $h n$ 为平均厚度，单位m； $φ e$ 为有效孔隙度，%； $S w i$ 为初始含水饱和度
8	$B P V = K R E S = (K O I L + K N G L) / F O I L + K G A S / F G A S$ $B S R = B P V B S E ρ r e s, C O 2$ $V S F = A S V T P I φ P I$ $R P V = V S F - B P V$ $R S R = R P V R S E R P V ρ r e s, C O 2$ $T S R = B S R + R 1, S R + R 2, S R + R 3, S R$	$B P V$ 为已知石油采收量，单位m³； $K R E S$ 为据储层条件校正体积值，单位m³； $K O I L$ 为据储层条件校正的已知油气采收量体积值，单位m³； $K N G L$ 为已知天然气液相采收量，单位m³； $F O I L$ 为石油和天然气液相地层体积系数； $K G A S$ 为已知气相采收量，单位m³； $F G A S$ 为气相馏分地层体积系数； $B S R$ 为浮力封存量，单位t； $B S E$ 为浮力封存效率，%； $V S F$ 为SF（储层）的孔隙体积，单位m³； $A S V$ 为面积，单位m²； $T P I$ 为SF中孔隙度大于等于8%的储层厚度，单位m； $φ P I$ 为多孔层平均孔隙度，%； $R P V$ 为可用于残余捕获孔隙体积，单位m³； $R S R$ 为残余封存量，单位t； $R S E$ 为残余捕获效率因子； $T S R$ 为整个SF技术可行封存量，单位t； $R 1, S R$ 、 $R 2, S R$ 和 $R 3, S R$ 分别为1类、2类和3类区域的残余捕获封存量，单位t
9	$M C O 2 = V R φ S g ρ r e s, C O 2 E$	$V R$ 为总储层体积，单位m³； $S g$ 为残余气饱和度，%

表2

表3

基于有效容积理论和物质平衡理论的改进和拓展模型汇总[14， 27-34]"

模型编号	计算原理	参数含义
10	$\begin{array}{c} M_{\mathrm{t}}=\frac{\rho_{\mathrm{res}, \mathrm{CO}_{2}}}{10^{9}}\left[E_{\mathrm{Rb}} A h \varphi\left(1-S_{\mathrm{wi}}\right)-V_{\mathrm{iw}}+V_{\mathrm{pw}}+\right. \\ \left.S_{\mathrm{W}, \mathrm{CO}_{2}}\left(A h \varphi S_{\mathrm{wi}}+V_{\mathrm{iw}}-V_{\mathrm{pw}}\right)+S_{\mathrm{O}, \mathrm{CO}_{2}}\left(1-E_{\mathrm{Rb}}\right) A h \varphi\left(1-S_{\mathrm{wi}}\right)\right] \\ M_{\mathrm{t}}=\frac{\rho_{\mathrm{res}, \mathrm{CO}_{2}}}{10^{9}}\left[\left(0.4 E_{\mathrm{Rb}}+0.6 E_{\mathrm{Rh}}\right) A h \varphi\left(1-\mathrm{S}_{\mathrm{wi}}\right)-V_{\mathrm{iw}}+V_{\mathrm{pw}}+\right. \\ \left.S_{\mathrm{W}, \mathrm{CO}_{2}}\left(A h \varphi S_{\mathrm{wi}}+V_{\mathrm{iw}}-V_{\mathrm{pw}}\right)+S_{\mathrm{O}, \mathrm{CO}_{2}}\left(1-0.4 E_{\mathrm{Rb}}-0.6 E_{\mathrm{Rh}}\right) A h \varphi\left(1-S_{\mathrm{wi}}\right)\right] \end{array}$	$M t$ 为CO₂理论封存量，单位10⁶ t； $E R b$ 为CO₂突破前原油采收率，%； $E R h$ 为注入某体积CO₂时原油采收率，%； $V i w$ 为注水量，单位m³； $V p w$ 为产水量，单位m³； $S W, C O 2$ 为CO₂水中溶解系数； $S O, C O 2$ 为CO₂油中溶解系数
11	$M e, C O 2 = ρ r e s, C O 2 A h φ 1 - S w i S C O 2$ $S C O 2 = C e [1 - S w i R f + S w i R w] + E f S p w 1 - R w S W, C O 2 + E f 1 - S p w 1 - R w S O, C O 2$	$M e, C O 2$ 为有效封存量，单位10⁶ t； $S C O 2$ 为综合封存系数； $R w$ 为水采收率，%； $E f$ 为CO₂波及效率，%； $S p w$ 为当前含水饱和度
12	$M_{\mathrm{CO}_{2}}=\left(\sum_{i=1}^{n} A_{i} h_{i} \varphi_{i}\right)\left(1-S_{\mathrm{w}}\right) B_{\mathrm{f}} \rho_{\mathrm{res}, \mathrm{CO}_{2}} E_{\mathrm{S}}$	$A i$ 为单一网格面积，单位ft²； $h i$ 为对应网格厚度，单位ft； $φ i$ 为该网格孔隙度，%；i为网格号，i=1，2，3，…，n； $E S$ 为包含储层非均质性、CO₂浮力和波及效率的总封存有效系数
13	$M C O 2 = V P o r e 1 000 S g i R e u 0 + ∆ R e u C O 2 ρ r e s, C O 2 + E f S w d S W, C O 2$	$V P o r e$ 为气藏孔隙体积，单位m³； $S g i$ 为原始含气饱和度； $R e u 0$ 为天然气藏衰竭开采标定采收率，%； $∆ R e u C O 2$ 为CO₂驱提高天然气采收率，%； $E f$ 为注入CO₂的波及系数； $S w d$ 为最终含水饱和度
14	$M 1 = R u V O O I P B f C a q (ρ r e s, C O 2 / ρ s t d, C O 2)$ $M 2 = V O O I P B f 1 - R u E f S O, C O 2 (ρ r e s, C O 2 / ρ s t d, C O 2)$ $M 3 = M w E f S W, C O 2 (ρ r e s, C O 2 / ρ s t d, C O 2)$	$M 1$ 为采出油原占据体积用于封存容量，单位m³； $R u$ 为最终采收率，%； $C a q$ 是咸水入侵效率因子（强侵入0.5，弱侵入0.97）； $M 2$ 为残余油中溶解CO₂量，单位m³； $M 3$ 为地层水中溶解CO₂量，单位m³； $M w$ 为储层含水量，单位m³
15	$\begin{array}{c} M_{\mathrm{co}_{2}, \mathrm{oil}}=\left(V_{\mathrm{OOIP}} / \rho_{\mathrm{res}, \mathrm{CO}_{2}}\right) B_{\mathrm{f}}\left(1-R_{\mathrm{f}}\right) C_{\mathrm{co}_{2}, \mathrm{oil}} m_{\mathrm{co}_{2}} \\ M_{\mathrm{co}_{2}, \mathrm{w}}=\left(V_{\mathrm{OOIP}} / \rho_{\mathrm{res}, \mathrm{CO}_{2}}\right) B_{\mathrm{f}} / S_{\mathrm{oi}} S_{\mathrm{w}} \rho_{\mathrm{w}} w_{\mathrm{f}, \mathrm{co}_{2}} / 10^{7} \\ M_{\mathrm{co}_{2}, \mathrm{~m}}=\sum_{j=1}^{n}\left(3.1536 r_{j} m_{\mathrm{co}_{2}} \times 10^{-5}\right) t \end{array}$	$M c o 2, o i l$ 为CO₂在残余油中的溶解封存量，单位Mt； $C c o 2, o i l$ 为平衡状态CO₂在油中摩尔溶解度，单位mol/L； $m c o 2$ 为CO₂分子量44 g/mol； $M c o 2, w$ 为CO₂地层水中溶解封存量，单位Mt； $S o i$ 为初始含油饱和度； $ρ w$ 为饱和CO₂后地层水密度，单位kg/m³； $w f, c o 2$ 为饱和地层水中CO₂质量分数； $M c o 2, m$ 为CO₂矿化封存量，单位Mt；j为矿化反应矿物种类的编号，j=1，2，3，…，n；r_j为第j种能与CO₂发生反应的矿物的溶解速度，单位mol/s；t为时间，单位a
16	$M e, C O 2 = 0.85 V s t d, g R r e s, C O 2 C H 4 ρ s t d, C O 2 + M C O 2 r j + M C O 2 k h$ $M C O 2 r j = A h φ S w ρ H 2 O C k h X C O 2 / 103$ $M C O 2 k h = 0.85 V s t d, g R r e s, C O 2 C H 4 ρ s t d, C O 2 K k h$	$V s t d, g$ 为标况下最终可采天然气体积，单位10⁹ m³； $M C O 2 r j$ 为CO₂水中溶解封存量，单位t； $M C O 2 k h$ 为矿化封存量，单位t； $ρ H 2 O$ 为地层水密度，单位kg/m³； $C k h$ 为矿化度修正系数； $X C O 2$ 为地层水被CO₂饱和时的CO₂占地层水的平均质量分数； $K k h$ 为矿化封存系数，取值0.015

表3

表4

考虑新分类和新计算原理的封存潜力评估模型汇总[35-38]"

模型	计算原理	参数含义
17	$\begin{array}{c} M_{\mathrm{HCPV}}=V_{n_{\mathrm{dep}}}+\int_{n_{\mathrm{dep}}}^{n_{\mathrm{dep}}+\Delta n_{\mathrm{Gi}}} \bar{V}_{\mathrm{x}} \mathrm{~d} n_{\mathrm{dep}} \end{array}$	$M H C P V$ 为注入目标HCPV的CO₂时封存量，单位ft³； $V n d e p$ 为根据气液平衡理论计算的虚构封存体积，单位ft³； $n d e p$ 为枯竭期油气摩尔数，单位mol； $Δ n G i$ 为注入含杂质CO₂摩尔数，单位mol； $V ¯ x$ 为广义偏摩尔体积，单位ft³/lb-mol
18	$\begin{array}{c} p_{\mathrm{c}}=\Delta \rho g H \\ p_{\mathrm{t}}=\gamma \cos \theta / R \\ p_{\mathrm{chc}}=\Delta \rho_{\mathrm{hc}} g H_{\mathrm{hc}} \\ H_{\mathrm{CO}_{2}}=\frac{p_{\mathrm{chc}} M}{\Delta \rho_{\mathrm{CO}_{2}} g} \end{array}$	$p c$ 为毛细压力，单位Pa； $Δ ρ$ 为润湿相和非润湿相间的密度差，单位kg/m³； $g$ 为重力，单位m/s²； $H$ 为非润湿相柱的高度，单位m； $P t$ 为临界压力，单位Pa； $γ$ 为侵入相的界面张力，单位N/m； $θ$ 为润湿角，单位（°）； $R$ 为孔喉半径，单位m； $p c h c$ 为地层水和油气之间的毛细压力，单位Pa； $Δ ρ h c$ 为地层水和油气之间的密度差，单位kg/m³； $H h c$ 为油气相柱高，单位m； $H C O 2$ 为CO₂的可持续柱高，单位m； $M$ 为压力转换乘数； $Δ ρ C O 2$ 为CO₂密度差，单位kg/m³
19	$\begin{array}{c} M_{\mathrm{tb}, \mathrm{CO}_{2}}=\sum_{k=1}^{n} Q_{\mathrm{og}, k}\left[S_{\mathrm{c}, \mathrm{o}}-\frac{\rho_{\mathrm{g}, \mathrm{surf}}}{1000}\left(G_{\mathrm{OR}}-R_{\mathrm{si}}\right)\right] \end{array}$	$M t b, C O 2$ 为同步封存量，单位10⁴ t；k为以年为单位的项目评价时间序号，k=1，2，3，…，n； $Q o g, k$ 为第k年气驱产量，单位t/a； $S c, o$ 为CO₂换油率，单位t/t； $ρ g, s u r f$ 为注入气地面密度，单位 kg/m³，取值1.9 kg/m³； $G O R$ 为CO₂驱生产气油比，单位m³/m³； $R s i$ 为原始溶解气油比，单位m³/m³
20	$M s u m = M p r o d, C O 2 + M r e s, C O 2 + M s u b, C O 2$ $M p r o d, C O 2 = C p V p r o d, P ρ p o s s, C O 2$ $M r e s, C O 2 = C r V r e s, P (1 - P i n i t / P o s s) ρ p o s s, C O 2$ $M s u b, C O 2 = C s V s u b, P (1 - P i n i t / P o s s) ρ p o s s, C O 2$	$M s u m$ 为总封存量，是生产封存量 $M p r o d, C O 2$ 、残余封存量 $M r e s, C O 2$ 和亚经济封存量 $M s u b, C O 2$ 加和，单位t； $C p$ 、 $C r$ 和 $C s$ 分别为产出气、残余气和亚经济气藏注入效率，%； $V p r o d, P$ 、 $V r e s, P$ 和 $V s u b, P$ 分别为储层条件下产出气、剩余气和亚经济区天然气资源所占据孔隙体积，单位m³； $P i n i t$ 为气藏初始压力，单位Pa； $P o s s$ 为气藏最佳安全封存压力，单位Pa； $ρ p o s s, C O 2$ 为储层温度和最佳安全封存压力下CO₂密度，单位kg/m³

表4

图2

图3

图4

图5

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油气藏CO₂封存潜力评估模型与实践进展

CO₂ storage potential assessment models and their practical progress in oil and gas reservoirs

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