页岩油微观渗流机理研究进展

王鸣川; 王燃; 岳慧; 张薇; 王付勇; 陈志强

doi:10.11781/sysydz202401098

页岩油微观渗流机理研究进展

doi: 10.11781/sysydz202401098

王鸣川^{1, 2,},
王燃^{1, 2},
岳慧^{1, 3},
张薇^{1, 2},
王付勇^{1, 3},
陈志强^{1, 2}

1.
中国石化页岩油气勘探开发重点实验室, 北京 102206
2.
中国石化石油勘探开发研究院, 北京 102206
3.
中国石油大学(北京) 非常规油气科学技术研究院, 北京 102249

基金项目:

中国石化重点基础前瞻研究项目 P22205

详细信息

作者简介:
王鸣川(1985-), 男, 博士, 副研究员, 从事地质建模与油藏数值模拟研究。E-mail: wangmc.syky@sinopec.com

中图分类号: TE311
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出版历程
- 收稿日期: 2023-08-14
- 修回日期: 2023-12-12
- 刊出日期: 2024-01-28

Research progress of microscopic percolation mechanism of shale oil

WANG Mingchuan^{1, 2
,},
WANG Ran^{1, 2},
YUE Hui^{1, 3},
ZHANG Wei^{1, 2},
WANG Fuyong^{1, 3},
CHEN Zhiqiang^{1, 2}

1.
SINOPEC Key Laboratory of Shale Oil/Gas Exploration and Production Technology, Beijing 102206, China
2.
SINOPEC Petroleum Exploration and Production Research Institute, Beijing 102206, China
3.
Unconventional Petroleum Research Institute, China University of Petroleum (Beijing), Beijing 102249, China

摘要

摘要: 页岩油已成为全球非常规油气资源勘探开发的重点，但其开发面临诸多挑战。针对页岩油赋存孔隙空间复杂、渗流机理尚不明确和研究方法亟需探索的关键问题，从孔隙尺度和岩心尺度，系统阐述了页岩油微观渗流机理在实验方法和计算模拟方面的研究现状，探讨了目前存在的问题和未来研究的发展趋势。结果显示，目前多种实验方法结合能较好表征页岩孔隙结构，但对微尺度与岩心尺度流动的表征尚存在不足；孔隙尺度流动机理研究以格子玻尔兹曼方法为代表的直接法和以孔隙网络模拟为代表的间接法为主，但对微尺度效应的考虑有待完善；岩心尺度流动机理研究主要为基于毛管束模型和分形理论，建立考虑边界层效应的渗流模型。指出充分考虑页岩油微纳米孔隙中流动边界吸附/滑移、密度/黏度非均质性、应力敏感、启动压力梯度等因素，耦合不同尺度渗流机理，构建能够准确表征页岩油多相多尺度流动特征的数学模型是未来的主要研究方向。
- 孔隙网络模型 /
- 格子玻尔兹曼方法 /
- 数字岩心 /
- 非线性渗流 /
- 渗流机理 /
- 页岩油
Abstract: Shale oil has become the focus of the exploration and development of unconventional oil and gas resources in the world, but its development faces many challenges. Aiming at the complex pore space, the unclear percolation mechanism, and the urgent need to explore research methods of shale oil, this paper systematically expounded the research status of microscopic percolation mechanism of shale oil in experimental methods and computational simulation, and discussed the existing problems and the development trend of future research from the perspective of pore-scale and core-scale. The results show that the combination of various experimental methods can well characterize the pore structure of shale, but the characterization of micro-scale and core-scale flow is still insufficient. The direct method represented by Lattice Boltzmann Method and the indirect method represented by pore network simulation are the main methods to study pore-scale flow mechanism, but the consideration of micro-scale effect needs to be improved. The study of core-scale flow mechanism is mainly to establish a percolation model considering boundary layer effect based on capillary bundle model and fractal theory. It is pointed out that the main future research direction is to fully consider the factors such as boundary adsorption/slip, density/viscosity heterogeneity, stress sensitivity, start-up pressure gradient of shale oil in micro-nano pores, realize multi-scale percolation mechanism coupling, and establish a mathematical model that can accurately characterize the multi-phase and multi-scale flow of shale oil.
- pore network model (PNM) /
- Lattice Boltzmann Method (LBM) /
- digital core /
- nonlinear percolation /
- percolation mechanism /
- shale oil
注释:

1) 所有作者声明不存在利益冲突。

1) 利益冲突声明/Conflict of Interests

2) 作者贡献/Authors’Contributions

2) 王鸣川参与论文设计、写作和修改；王燃，岳慧，张薇，王付勇，陈志强参与论文写作和修改。所有作者均阅读并同意最终稿件的提交。

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图 1 页岩储集空间表征的实验方法及适用范围

Figure 1. Experimental method and application range of shale reservoir space characterization

下载: 全尺寸图片幻灯片

图 2 基于孔隙网络模型的微观渗流模拟技术路线

Figure 2. Technical route of microscopic percolation simulation based on pore network model

下载: 全尺寸图片幻灯片

表 1 非线性渗流数学模型及特点

Table 1. Nonlinear percolation mathematical model and its characteristics

模型分类	参考文献	速度方程	模型特点
分段模型	PRADA等^[89]	$\left\{\begin{array}{cc} v=0 & \nabla_p \leq \nabla p_{\mathrm{TPG}} \\ v=\frac{k}{\mu}\left(\nabla_p-\nabla_{p_{\mathrm{TPG}}}\right) & \nabla_p>\nabla p_{\mathrm{TPG}} \end{array}\right.$	模型简单，渗流曲线不连续，不能体现非线性渗流
	黄延章^[84]	$\text { ① }\left\{\begin{array}{cl} v=0 & \nabla p \leq \nabla p_{\mathrm{a}} \\ v=\frac{k}{\mu}\left(\nabla_p-\nabla_{p_{\mathrm{a}}}\right)^n & \nabla_{p_{\mathrm{a}}}<\nabla p \leq \nabla p_{\mathrm{b}} \\ v=\frac{k}{\mu} \nabla p & \nabla p>\nabla p_{\mathrm{b}} \end{array}\right.$	模型①考虑了启动压力梯度及非线性渗流段；模型②在数学处理中应用较为便捷，未体现启动压力梯度；模型③考虑了启动压力梯度，但对于低压力梯度时大孔道中的流动预测偏低
		$② \left\{ {\begin{array}{*{20}{l}} {v = {{\left( {\frac{k}{\mu }} \right)}_1}\nabla p}&{\nabla p \leqslant \nabla {p_{\text{b}}}} \\ {v = {{\left( {\frac{k}{\mu }} \right)}_2}\nabla p}&{\nabla p > \nabla {p_{\text{b}}}} \end{array}} \right.$
		$\text { ③ }\left\{\begin{array}{cl} v=0 & \nabla p \leq \nabla p_{\mathrm{c}} \\ v=\frac{k}{\mu}\left(\nabla p-\nabla p_{\mathrm{c}}\right) & \nabla p>\nabla p_{\mathrm{c}} \end{array}\right.$
	阮敏等^[90]	$\left\{\begin{array}{cc} v=a_1(\nabla p)^n & \nabla p \leq \nabla p_{\mathrm{b}} \\ v=a_2\left(\nabla p-\nabla p_{\mathrm{c}}\right) & \nabla p>\nabla p_{\mathrm{b}} \end{array}\right.$	考虑了非线性渗流段，a₁, a₂, n由实验测量确定
	LI等^[91]	$\left\{\begin{array}{cl} v=0 & \nabla p \leq \nabla p_{\mathrm{a}} \\ v=a \frac{k}{\mu}\left(\nabla p_{\mathrm{b}}-\nabla p\right)^n & \nabla p_{\mathrm{a}}<\nabla p \leq \nabla p_{\mathrm{b}} \\ v=\frac{k}{\mu}\left(\nabla p-\nabla p_{\mathrm{b}}\right) & \nabla p>\nabla p_{\mathrm{b}} \end{array}\right.$	渗流曲线连续，a为非线性常数
多参数模型	邓英尔等^[86]	$v\left(a_1+\frac{a_2}{1+b v}\right)=-\nabla p$	模型简单，a₁，a₂，b均由实验确定
	杨清立等^[92]	$v=\frac{k}{\mu}\left(1-\frac{1}{a+b\|\nabla p\|}\right) \nabla_p$	模型简单，a为非线性渗流段的影响因子，b相当于拟启动压力梯度的倒数；a和b均由实验确定
	黄延章等^[85]	$v=\frac{k}{\mu}\left(1-\frac{\nabla p_{\mathrm{c}}}{\nabla p+\nabla p_{\mathrm{c}}-\nabla p_{\mathrm{a}}}\right) \nabla_p$	连续函数，模型参数简单
	姜瑞忠等^[81]	$v=\frac{k}{\mu}\left(1-\frac{c_1}{\nabla_p-c_2}\right) \nabla_p$	c₁和c₂是反映启动压力梯度和非线性渗流的特征参数，通过实验拟合得到
	时宇等^[93]	$v=\frac{a \pi c_{\mathrm{k}}(\nabla p)}{8 \mu}\left[\nabla p-\frac{c_{\mathrm{p}}\left(\nabla_p\right)}{c_{\mathrm{k}}(\nabla p)} \nabla p_{\mathrm{a}}\right]$	非线性渗流与拟线性渗流段的划分通过实验确定，a为喉道拟合参数
	杨仁锋等^[79]	$v=\frac{k}{\mu}\left(1-\frac{\xi_1}{\nabla p}-\frac{\xi_1 \xi_2}{\nabla p\left(\nabla p-\xi_2\right)}\right) \nabla_p$	边界层为非牛顿流体；流体存在屈服应力值；ξ₁₊ξ₂为真实启动压力梯度
	XIONG等^[94]	$v=\frac{k\left(1-\delta_{\mathrm{D}} \mathrm{e}^{-c_{\varphi} \nabla_p}\right)^4}{\mu} \nabla_p$	边界层不可动且随着压力梯度的升高而降低
	WANG等^[95]	$v=-\frac{k}{\mu}\left(\frac{1}{1+a \mathrm{e}^{-b\left\|\nabla_p\right\|}}\right) \nabla_p$	没有启动压力梯度，只有非线性渗流段。a和b由实验数据拟合得到
分形模型	CAI^[96]	$v=\frac{k_{\mathrm{f}}}{\mu_{\mathrm{d}}}\left(\nabla_p-\frac{16 \tau_0}{3} \frac{3+D_{\mathrm{T}}-D_{\mathrm{f}}}{3-D_{\mathrm{f}}} \frac{D_{\mathrm{max}}^{-D_{\mathrm{f}}}}{L_0^{1-D_{\mathrm{r}}}}\right)$	渗流曲线不连续，流体为宾汉流体，多孔介质采用分形理论描述，忽略了非线性渗流段
	HUANG等^[97]	$v = \frac{{\nabla p}}{\mu }\left[ {\frac{{\pi {D_{\text{f}}}r_{\max }^{3 + {D_{\text{T}}}}}}{{A{2^{4 - {D_{\text{T}}}}}L_0^{{D_{\text{T}}} - 1}\left( {3 - {D_{\text{f}}} + {D_{\text{T}}}} \right)}}} \right.\left. { - \frac{{\pi {D_{\text{f}}}r_{{\text{max }}}^{{D_{\text{f}}}}{a_1}{a_3}^{{a_4}}\mu T}}{{A{2^{2 - {D_{\text{T}}}}}L_0^{{D_{\text{T}}} - 1}}}\nabla {p^{{a_4}}}} \right]$	基于毛管束模型，管径分布符合分形幂关系，边界层描述采用考虑影响因素的拟合模型，a₁、a₃、a₄通过拟合非线性流实验、微管实验等实验测得
	WANG等^[98]	$v = \frac{\pi }{{32\left( {{D_{\text{T}}} + 3} \right)}}\frac{{\nabla p}}{\mu }\frac{{L_0^{1 - {D_{\text{T}}}}}}{A}{D_{\text{f}}}D_{_{\max }}^{^{{D_{\text{f}}}}}\int_{{D_{\min }}}^{{D_{\max }}} {{{\left( {1 - \frac{{2h}}{D}} \right)}^{\left( {{D_{\text{T}}} + 3} \right)}}} {D^{{D_{\text{T}}} - {D_{\text{f}}} + 2}}{\text{d}}D$	考虑边界层分布及边界层厚度随压力梯度的变化关系
注：v为渗流速度；μ为流体黏度；k为渗透率；$\nabla p$为压力梯度；$\nabla p_{\mathrm{TPG}}$为启动压力梯度；$\nabla p_{\mathrm{a}}$、$\nabla p_{\mathrm{b}}$、$\nabla p_{\mathrm{c}}$分别为最小、最大和拟启动压力梯度；c_p、c_k为喉道半径与压力梯度的分段函数；δ_D为无因次边界层厚度；c_φ为非达西参数；k_f为孔隙介质分形渗透率；τ₀为流体屈服强度；μ_d为流体塑性黏度；D_f为喉道分形维数；D_T为毛细管弯曲度分形维数；L₀为岩心样品直线长度；r_max为最大毛细管半径；A为毛管束模型横截面积；T为与孔隙和喉道特征相关的常数；D为孔喉直径；D_min，D_max为最小、最大孔喉直径；h为非流体流动边界层厚度。

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