Numerical simulation of natural gas hydrate production: Theories, technologies, and applications

doi:10.3969/j.issn.2096-1693.2026.02.001

Abstract

Abstract:

The goal of “carbon peak and neutrality” is driving China’s energy system to accelerate transition to clean, low-carbon development. As an important new clean energy source, natural gas hydrate (NGH) exhibits high energy density, wide distribution, and substantial resource potential. Therefore, accelerating its industrial production is key to achieving a reduction in pollution and carbon emission. NGH production involves Thermal-Hydrological-Mechanical-Chemical (THMC) multi-physical field coupling. Traditional experiments and production tests fail to fully reveal underlying mechanisms, making numerical simulation—with high functionality, flexible methods, and low cost—an essential research tool. This study systematically reviews theories, technologies, and applications of numerical simulation for natural gas hydrate production to provide theoretical support for safe, efficient extraction and advance the translation of simulation technologies to engineering practice. Specifically, it clarifies evolution laws of seepage parameters (e.g., porosity, permeability) during hydrate dissociation; identifies evolution of mechanical parameters (e.g., shear strength, cohesion) with hydrate saturation, revealing the core mechanism by which hydrates dominate reservoir mechanical property evolution via decomposition behaviour; outlines approaches to constructing THMC multi-physical field coupling models; summarizes functions and advantages of major global simulators (e.g., TOUGH+Hydrate, SuGaR-TCHM), and validates applications at typical pilot sites. Current numerical simulation research has limitations: multi-phase flow models insufficiently account for continuous pore structure evolution and impacts of hydrate saturation on relative permeability; characterization of mechanical properties and sand production risk responses in unconsolidated clayey silt sediments is inadequate; and capacity to predict long-term mechanical stability risks (e.g., land subsidence, submarine landslides) induced by production is limited. Future work should establish “micro-macro” cross-scale parameter models, refine elastoplastic constitutive models for clayey silt sediments, and develop integrated geological engineering simulation tools to advance simulation technologies from mechanistic interpretation to engineering decision support.

Key words: natural gas hydrate, numerical simulation, multi-phase flow, mechanical property, production tests

摘要：

“碳达峰”与“碳中和”目标推动中国能源系统加速向清洁低碳转型，天然气水合物作为重要新型清洁能源，具能量密度高、分布广、资源潜力大等特点，加快其产业化开采是实现我国低碳利用、减污降碳的关键途径。其开采涉及传热—流动—力学—相变(THMC)多物理场耦合，传统实验与试采工程难以全面揭示内在机制，功能强大、方法灵活且成本低的数值模拟方法已成为核心研究工具。本研究系统梳理天然气水合物开采数值模拟的理论、技术与应用，旨在为水合物安全高效开采提供理论支撑、推动数值模拟技术向工程应用转化。研究厘清了孔隙度、渗透率等渗流参数随水合物分解的演化规律；明确了抗剪强度、内聚力等力学参数随饱和度演化规律，揭示了水合物分解行为主导储层力学特性演化的核心机制；梳理了THMC多物理场耦合数学模型构建思路，汇总了TOUGH+Hydrate、SuGaR-TCHM等全球主流数值模拟器功能及优势，结合典型试采场地开展验证应用。当前数值模拟研究仍存在局限：多相渗流参数模型未充分考虑孔隙结构连续性演化及水合物饱和度对相对渗透率的影响；对未固结泥质粉砂储层的力学特性及出砂风险响应刻画不足；对开采诱发的长期力学稳定性(如地面沉降，海底滑坡)预警能力有限。未来需建立“微观—宏观”跨尺度参数模型、完善泥质粉砂沉积物弹塑性本构模型、开发地质—工程一体化模拟工具，推动模拟技术从机理阐释向工程决策支持深度转化。

关键词: 天然气水合物, 数值模拟, 多相渗流, 力学性质, 试采工程应用

CLC Number:

TE311
P744.4

LI Yaobin, XU Tianfu, XIN Xin, YUAN Yilong, ZHU Huixing. Numerical simulation of natural gas hydrate production: Theories, technologies, and applications[J]. Petroleum Science Bulletin, 2026, 11(1): 257-275.

李耀斌, 许天福, 辛欣, 袁益龙, 朱慧星. 天然气水合物开采数值模拟：理论、技术与应用[J]. 石油科学通报, 2026, 11(1): 257-275.

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URL: https://sykxtb.cup.edu.cn/EN/10.3969/j.issn.2096-1693.2026.02.001

https://sykxtb.cup.edu.cn/EN/Y2026/V11/I1/257

Figures/Tables 12

References 144

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模型	表达式	优点	缺点
Kleinberg et al., 2003^[38]	颗粒包裹型：$k_{\mathrm{nor}}=\frac{k\left(S_{\mathrm{h}}\right)}{k_{0}}=\left(1-S_{\mathrm{h}}\right)^{2}$	考虑孔隙结构与水合物赋存模式	脱离实际情况
Kleinberg et al., 2003^[38]	孔隙填充型：$k_{\mathrm{nor}}=1-S_{\mathrm{h}}^{2}+\frac{2\left(1-S_{\mathrm{h}}\right)^{2}}{\lg \left(S_{\mathrm{h}}\right)}$	考虑孔隙结构与水合物赋存模式	脱离实际情况
Masuda, 1997^[39]	$k_{\mathrm{nor}}=\left(1-S_{\mathrm{h}}\right)^{N}$	形式简洁	未考虑孔隙结构与水合物赋存模式
Katagiri et al., 2017^[40]	颗粒包裹型：$k_{\mathrm{nor}}=\frac{\pi\left(1-S_{\mathrm{h}}\right)^{n+2}}{4-(4-\pi)\left(1-S_{\mathrm{h}}\right)}$	形式简洁; 拟合参数N	经验参数物理意义不明确
Katagiri et al., 2017^[40]	孔隙填充型：$k_{\mathrm{nor}}=\frac{\left(1-S_{\mathrm{h}}\right)^{n+2}}{\left[1+\sqrt{\frac{(4-\pi) S_{\mathrm{h}}}{\pi}}\right]^{2}}$	形式简洁; 拟合参数N	经验参数物理意义不明确
Van, 1980^[41]	$k_{\mathrm{nor}}=\sqrt{S_{\mathrm{nw}}}\left[1-\left(1-\sqrt[m]{S_{\mathrm{nw}}}\right)^{m}\right]^{2}$	考虑孔隙结构与水合物赋存模式	形式复杂; 参数校准困难
Delli and Grozic, 2013^[42]	$k_{\mathrm{nor}}=S_{\mathrm{h}}^{n} k_{\mathrm{r}}^{p f}+\left(1-S_{\mathrm{h}}\right)^{m} k_{\mathrm{r}}^{g c}$	描述非线性关系	计算复杂
Dai and Seol, 2014^[43]	$k_{\mathrm{nor}}=\frac{\left(1-S_{\mathrm{h}}\right)^{3}}{\left(1+2 S_{\mathrm{h}}\right)^{2}}$	结合数值模拟结果	适用性不强

模型	表达式	优点	缺点
Kleinberg et al., 2003^[38]	颗粒包裹型：$k_{\mathrm{nor}}=\frac{k\left(S_{\mathrm{h}}\right)}{k_{0}}=\left(1-S_{\mathrm{h}}\right)^{2}$	考虑孔隙结构与水合物赋存模式	脱离实际情况
Kleinberg et al., 2003^[38]	孔隙填充型：$k_{\mathrm{nor}}=1-S_{\mathrm{h}}^{2}+\frac{2\left(1-S_{\mathrm{h}}\right)^{2}}{\lg \left(S_{\mathrm{h}}\right)}$	考虑孔隙结构与水合物赋存模式	脱离实际情况
Masuda, 1997^[39]	$k_{\mathrm{nor}}=\left(1-S_{\mathrm{h}}\right)^{N}$	形式简洁	未考虑孔隙结构与水合物赋存模式
Katagiri et al., 2017^[40]	颗粒包裹型：$k_{\mathrm{nor}}=\frac{\pi\left(1-S_{\mathrm{h}}\right)^{n+2}}{4-(4-\pi)\left(1-S_{\mathrm{h}}\right)}$	形式简洁; 拟合参数N	经验参数物理意义不明确
Katagiri et al., 2017^[40]	孔隙填充型：$k_{\mathrm{nor}}=\frac{\left(1-S_{\mathrm{h}}\right)^{n+2}}{\left[1+\sqrt{\frac{(4-\pi) S_{\mathrm{h}}}{\pi}}\right]^{2}}$	形式简洁; 拟合参数N	经验参数物理意义不明确
Van, 1980^[41]	$k_{\mathrm{nor}}=\sqrt{S_{\mathrm{nw}}}\left[1-\left(1-\sqrt[m]{S_{\mathrm{nw}}}\right)^{m}\right]^{2}$	考虑孔隙结构与水合物赋存模式	形式复杂; 参数校准困难
Delli and Grozic, 2013^[42]	$k_{\mathrm{nor}}=S_{\mathrm{h}}^{n} k_{\mathrm{r}}^{p f}+\left(1-S_{\mathrm{h}}\right)^{m} k_{\mathrm{r}}^{g c}$	描述非线性关系	计算复杂
Dai and Seol, 2014^[43]	$k_{\mathrm{nor}}=\frac{\left(1-S_{\mathrm{h}}\right)^{3}}{\left(1+2 S_{\mathrm{h}}\right)^{2}}$	结合数值模拟结果	适用性不强

模型	表达式	优点	缺点
Corey$ model^[44]	气相：$k_{\mathrm{rg}}=\left[1-\left(\frac{S_{\mathrm{w}}-S_{\mathrm{rw}}}{1-S_{\mathrm{rw}}-S_{\mathrm{rg}}}\right)\right]^{2}\left[1-\left(\frac{S_{\mathrm{w}}-S_{\mathrm{rw}}}{1-S_{\mathrm{rw}}-S_{\mathrm{rg}}}\right)^{2}\right]$	形式简单	过于简化; 难捕捉非线性关系
Corey$ model^[44]	液相：$k_{\mathrm{rw}}=\left(\frac{S_{\mathrm{w}}-S_{\mathrm{rw}}}{1-S_{\mathrm{rw}}-S_{\mathrm{rg}}}\right)^{4}$	形式简单	过于简化; 难捕捉非线性关系
Optimized Van Genuchten model^[45]	气相：$k_{\mathrm{rg}}=\left(1-\frac{S_{\mathrm{w}}-S_{\mathrm{rw}}}{1-S_{\mathrm{rw}}}\right)^{1 / 2}\left(1-{\frac{S_{\mathrm{w}}-S_{\mathrm{rw}}}{1-S_{\mathrm{rw}}}}^{1 / m}\right)^{2m}$	通过调节指数灵活适应多种工况	参数校准困难
Optimized Van Genuchten model^[45]	液相：$k_{\mathrm{rw}}=\frac{{S_{\mathrm{w}}-S_{\mathrm{rw}}}}{1-S_{\mathrm{rw}}}^{1 / 2}\left[1-\left(1-\frac{S_{\mathrm{w}}-S_{\mathrm{rw}}}{1-S_{\mathrm{rw}}}^{1 / m}\right)^{m}\right]^{2}$	通过调节指数灵活适应多种工况	参数校准困难
Modified Stone model^[46]	气相：$k_{\mathrm{rg}}=\left(\frac{S_{\mathrm{g}}-S_{\mathrm{rg}}}{1-S_{\mathrm{rw}}-S_{\mathrm{rg}}}\right)^{n_{\mathrm{g}}}$	形式简洁	物理机制不明
Modified Stone model^[46]	液相：$k_{\mathrm{rw}}=\left(\frac{S_{\mathrm{w}}-S_{\mathrm{rw}}}{1-S_{\mathrm{rw}}-S_{\mathrm{rg}}}\right)^{n_{\mathrm{w}}}$	形式简洁	物理机制不明
Singh et al., 2019^[47]	气相：$k_{\mathrm{rg}}=\frac{r_{\mathrm{g}}^{2}}{R^{2}}\left[\alpha\left(1-\frac{r_{\mathrm{w}}^{2}}{r_{\mathrm{g}}^{2}}\right)-1\right] \frac{\tau\left(\varphi, 1-S_{\mathrm{h}}-S_{\mathrm{wr}}\right)}{\tau\left(\varphi, S_{\mathrm{g}}\right)}$	考虑孔隙结构效应	结构复杂
Singh et al., 2019^[47]	液相：$k_{\mathrm{rw}}=\frac{\left(r_{\mathrm{w}}^{2}-r_{\mathrm{g}}^{2}\right)^{2}}{R^{2}\left(r_{\mathrm{w}}^{2}-r_{\mathrm{g}}^{2}\right)} \frac{\tau\left(\varphi, 1-S_{\mathrm{h}}-S_{\mathrm{wr}}\right)}{\tau\left(\varphi, S_{\mathrm{w}}\right)}$	考虑孔隙结构效应	结构复杂

模型	表达式	优点	缺点
Corey$ model^[44]	气相：$k_{\mathrm{rg}}=\left[1-\left(\frac{S_{\mathrm{w}}-S_{\mathrm{rw}}}{1-S_{\mathrm{rw}}-S_{\mathrm{rg}}}\right)\right]^{2}\left[1-\left(\frac{S_{\mathrm{w}}-S_{\mathrm{rw}}}{1-S_{\mathrm{rw}}-S_{\mathrm{rg}}}\right)^{2}\right]$	形式简单	过于简化; 难捕捉非线性关系
Corey$ model^[44]	液相：$k_{\mathrm{rw}}=\left(\frac{S_{\mathrm{w}}-S_{\mathrm{rw}}}{1-S_{\mathrm{rw}}-S_{\mathrm{rg}}}\right)^{4}$	形式简单	过于简化; 难捕捉非线性关系
Optimized Van Genuchten model^[45]	气相：$k_{\mathrm{rg}}=\left(1-\frac{S_{\mathrm{w}}-S_{\mathrm{rw}}}{1-S_{\mathrm{rw}}}\right)^{1 / 2}\left(1-{\frac{S_{\mathrm{w}}-S_{\mathrm{rw}}}{1-S_{\mathrm{rw}}}}^{1 / m}\right)^{2m}$	通过调节指数灵活适应多种工况	参数校准困难
Optimized Van Genuchten model^[45]	液相：$k_{\mathrm{rw}}=\frac{{S_{\mathrm{w}}-S_{\mathrm{rw}}}}{1-S_{\mathrm{rw}}}^{1 / 2}\left[1-\left(1-\frac{S_{\mathrm{w}}-S_{\mathrm{rw}}}{1-S_{\mathrm{rw}}}^{1 / m}\right)^{m}\right]^{2}$	通过调节指数灵活适应多种工况	参数校准困难
Modified Stone model^[46]	气相：$k_{\mathrm{rg}}=\left(\frac{S_{\mathrm{g}}-S_{\mathrm{rg}}}{1-S_{\mathrm{rw}}-S_{\mathrm{rg}}}\right)^{n_{\mathrm{g}}}$	形式简洁	物理机制不明
Modified Stone model^[46]	液相：$k_{\mathrm{rw}}=\left(\frac{S_{\mathrm{w}}-S_{\mathrm{rw}}}{1-S_{\mathrm{rw}}-S_{\mathrm{rg}}}\right)^{n_{\mathrm{w}}}$	形式简洁	物理机制不明
Singh et al., 2019^[47]	气相：$k_{\mathrm{rg}}=\frac{r_{\mathrm{g}}^{2}}{R^{2}}\left[\alpha\left(1-\frac{r_{\mathrm{w}}^{2}}{r_{\mathrm{g}}^{2}}\right)-1\right] \frac{\tau\left(\varphi, 1-S_{\mathrm{h}}-S_{\mathrm{wr}}\right)}{\tau\left(\varphi, S_{\mathrm{g}}\right)}$	考虑孔隙结构效应	结构复杂
Singh et al., 2019^[47]	液相：$k_{\mathrm{rw}}=\frac{\left(r_{\mathrm{w}}^{2}-r_{\mathrm{g}}^{2}\right)^{2}}{R^{2}\left(r_{\mathrm{w}}^{2}-r_{\mathrm{g}}^{2}\right)} \frac{\tau\left(\varphi, 1-S_{\mathrm{h}}-S_{\mathrm{wr}}\right)}{\tau\left(\varphi, S_{\mathrm{w}}\right)}$	考虑孔隙结构效应	结构复杂

模型	模型参数				来源
模型	S_rw	S_rg	P₀	λ	来源
Books-Corey	-	-	-	0.5	Corey^[44]
Books-Corey	0.10	0.1	5 kPa	0.25	Konno et al.^[48]
Van Genuchten	0.14	0.0	-	0.46	Gamwo and Liu^[49]
	0.19	-	2 kPa	0.45	Moridis and Sloan^[50]
	0.19	-	2 kPa	0.77	Reagan et al.^[51]
	0.20	0.5	1 kPa	0.45	Uddin et al.^[52]

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