Probabilistic analysis of water-sealed performance in underground oil storage considering spatial variability of hydraulic conductivity

Abstract

For underground water-sealed oil storage, the spatial variability of the surrounding rock has a significant impact on the water-sealed effect of a water curtain system. This study presents a methodology for the probabilistic analysis of water curtain performance in underground oil storage, considering the spatial variability of hydraulic conductivity of the surrounding rock based on field data. Anisotropic random fields representing the spatial variability of hydraulic conductivity were established through spatial statistical analysis of field data and introduced into the finite element model of underground oil storage for water-sealed reliability analysis. The water-sealed performance of different water curtain system schemes was studied using Monte Carlo simulation (MCS). The results showed that the difference between the horizontal spatial correlation and the vertical spatial correlation of the surrounding rock has a significant impact on the water-sealed effect of the water curtain system. An excessively large pressure of water curtain boreholes provided a small contribution to improving water curtain performance. The distance between the water curtain holes and the caverns has the less significant affecting the water-sealed reliability of the storage cavern. Finally, the optimal design of the water curtain system is discussed. This study provides valuable insights and a theoretical basis for the optimisation of water curtain system design parameters for underground water-sealed oil storage.

Stability Analysis and the Random Response of Anti-Sliding Pile for Erdaogou Landslide Considering Spatial Variability

“…When the COV of failure probability is less than a certain value, this probability is regarded as the failure probability of a slope. In this paper, the COV failure probability can be determined according to Equation (16). If the value is less than 0.3, it is judged as computational convergence.…”

“…Griffiths et al [15] demonstrated that ignoring the spatial variability of soil parameters could lead to underestimating slope stability. Generally, natural spatial variability can be described by random fields and probabilistic methods are often used to evaluate landslide reliability [16][17][18]. Li et al [19] by combining the random finite element method with the Monte Carlo Simulation (MCS), thereby improving computational efficiency.…”

“…The methods proposed in this paper can provide significant guidance for optimizing the design of anti-sliding piles (Figure 1). methods are often used to evaluate landslide reliability [16][17][18]. Li et al [19] generated a random field by combining the random finite element method with the Monte Carlo Simulation (MCS), thereby improving computational efficiency.…”