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A New Approach to Identifying Preferential Seepage Channels for Underground Water-Sealed Oil Storage Cavern During Construction

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Abstract

Identification of preferential seepage channels for a large-scale underground water-sealed oil storage cavern is of great importance for controlling groundwater seepage and ensuring water-sealing performance. In this study, a new approach to identifying preferential seepage channels in the surrounding rock of the underground water-sealed oil storage cavern is proposed, focusing on the spatial distribution and connectivity of microfractures obtained from microseismic monitoring in the surrounding rock masses during cavern construction. The focal mechanism and the spatial occurrence of microfractures are revealed through inversion and analysis of the microseismic moment tensor. By adopting the graph traversal method, the spatial connectivity of microfractures is determined and preferential seepage channels are identified. An underground water-sealed oil storage cavern in Jinzhou, China, is selected as a case study. Five preferential seepage channels, which originate from the water curtain borehole and the surrounding rock masses between the oil storage caverns, and extend downwards along microfracture clusters are identified. The feasibility and validity of the proposed method are verified by the water supply data in the water curtain borehole and field survey. The research results can provide some reference for seepage channel detection, water-sealing performance evaluation and grouting design of large-scale underground water-sealed oil storage caverns.

Highlights

  • The formation of preferential seepage channel is accompanied by the spatial connectivity of microfractures.

  • Preferential seepage channels can be identified using microseismicity and pore pressure data in rocks.

  • The microfractures caused by excavation-induced unloading are mainly tensile cracks in underground oil storage caverns.

  • The occurrence of microfracture around the water curtain tunnel differs from that near the oil storage cavern.

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Abbreviations

\({\varvec{U}}\) :

Matrix of displacements

\({\varvec{M}},\boldsymbol{ }{M}_{ij}\) :

Moment tensor and its component

\({\varvec{G}}\) :

Green’s function

\(\rho\) :

Density

\(r\) :

Distance between the seismic source and the sensor

\({v}_{P},{ v}_{S}\) :

P wave velocity and S wave velocity

\(\gamma\), \(\varphi\), \(\theta\) :

Three components of the source-sensor ray coordinate system in the global coordinate system

\({M}_{1}, \text{ }{ M}_{2},\text{ } {M}_{3}\) :

Three eigenvalues of moment tensor

\({{\varvec{E}}}^{\text{ISO}}, \text{ }{{\varvec{E}}}^{\text{DC}}, \text{ }{{\varvec{E}}}^{\text{CLVD}}\) :

Moment tensor bases

\({M}^{ISO},\text{ }{M}^{DC} ,\text{ }{M}^{CLVD}\) :

Magnitudes of the isotropic component, double-couple component and the compensated linear vector dipole component

\({P}_{DC}\) :

Proportion of the double-couple component

\(\overrightarrow{{{\varvec{e}}}_{1}}\), \(\overrightarrow{{{\varvec{e}}}_{3}}\) :

Eigenvectors corresponding to the maximum and minimum eigenvalues

\(\overrightarrow{{\varvec{n}}}\) :

Normal vector of the fracture surface

\(R\) :

Radius of a circular fracture surface

\({K}_{c}\) :

Seismic source model constant

\({f}_{0}\) :

Corner frequency

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (grant nos. 42007262, 51974055, 42122052), the Joint Fund of Natural Science Basic Research Program of Shanxi Province (Grant No. 2021JLM-11), the Fund of China Petroleum Technology and Innovation (grant no. 2020D-5007-0302) and Yunnan Fundamental Research Projects (grant no. 202001AT070150).

Author information

Authors and Affiliations

  1. State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116024, Liaoning, China

    Ke Ma

  2. Institute of Rock Instability and Seismicity Research, Dalian University of Technology, Dalian, 116024, Liaoning, China

    Ke Ma

  3. Center for Hypergravity Experiment and Interdisciplinary Research, Zhejiang University, Hangzhou, 310058, Zhejiang, China

    Duanyang Zhuang

  4. MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering, Zhejiang University, Hangzhou, 310058, Zhejiang, China

    Duanyang Zhuang & Albert Argilaga

  5. Dianxi Research Institute of Dalian University of Technology, Baoshan, 678000, Yunnan, China

    Shiqiang Yu & Jianjuan Zhang

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  1. Ke Ma
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  2. Duanyang Zhuang
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  3. Albert Argilaga
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  4. Shiqiang Yu
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Correspondence to Duanyang Zhuang.

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Appendices

Appendix

Pore water pressure in the surrounding rock masses of the underground water-sealed storage caverns after excavation obtained by numerical simulation.

Numerical Model and Boundary Conditions

As the length along the cavern axis is much greater than the other two directions, the problem can be simplified into a plane strain problem. Therefore, a typical cavern structure model (Fig. 5) consisting of oil storage caverns 1N and 1S is established to simulate and analyze the seepage field of underground water-sealed caverns during construction. The curtain system is composed of 2 water curtain tunnels (WCTs), 2 horizontal water curtain boreholes (HWCBs) and 2 vertical water curtain boreholes (VWCBs). The horizontal curtain borehole has a diameter of 100 mm and a length of 50 m, while the vertical curtain borehole has a diameter of 100 mm and a length of 58 m. During construction, the water curtain tunnels were not filled with water, while a water pressure of 0.4 MPa was maintained in the horizontal and vertical curtain boreholes so as to ensure the operation state of the curtain system.

The rock layers (Fig. 5) below the groundwater table and the cavern structure are selected to simulate the pore water pressure and seepage vectors in the surrounding rocks after cavern excavation by the F-RFPA2Dmethod. The F-RFPA2D (Tang et al. 2002) was developed on the basis of the RFPA method (Tang 1997), by applying the principle of seepage-stress-damage coupling. It can not only be applied to simulate the seepage characteristics of brittle rocks, but also can be used for numerical analysis of the seepage field and the pore pressure field around rock structures.

The numerical model has a dimension of 276 m (width) × 170 m (height), and is divided into 187,860 elements. According to field survey and packer tests, the mechanical and seepage parameters of the surrounding rocks are determined as listed in Table 4. As for the mechanical boundary condition, the bottom is fixed in the vertical direction while the top is a free boundary. A horizontal in situ stress of 4 MPa is applied on the left and right boundaries so as to simulate the in situ stress on the caverns. As for the seepage boundary, the surrounding rocks are considered saturated and the top is a permeable boundary; a constant water head is applied on the left, right and bottom boundaries, which is determined according to the hydrostatic pressure. Zero water head is applied at the water curtain tunnels and the oil storage caverns. A constant water head of 0.4 MPa is maintained in the horizontal and vertical water curtain boreholes.

Table 4 Physical and mechanical parameters for numerical simulation
Full size table

Simulation Results

Figure 10 shows the numerical results of pore water pressure distribution, where the higher the gray level, the lower the pore water pressure. The right figure shows the vector diagrams of seepage field, where the line length indicates the seepage velocity. After excavation, obvious groundwater depression cones are formed around the water curtain tunnels and the oil storage caverns. The seepage vectors point to the water curtain tunnel and the oil storage caverns, respectively. This is consistent with the calculation results obtained by Song et al. (2014). All the increased seepage vectors point to the oil storage caverns, indicating that the seepage volume will increase in the oil storage caverns with a seepage volume for one cavern of 350 m3/d and the direction of seepage vectors is the preferential seepage direction of groundwater.

Fig. 10
figure 10

a Pore water pressure and b seepage vector around the underground water-sealed caverns after excavation

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Ma, K., Zhuang, D., Argilaga, A. et al. A New Approach to Identifying Preferential Seepage Channels for Underground Water-Sealed Oil Storage Cavern During Construction. Rock Mech Rock Eng 56, 6395–6410 (2023). https://doi.org/10.1007/s00603-023-03384-2

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