Fracture modeling of carbonate rocks via radial basis interpolation and discrete fracture network

Li, Yuhan; Wang, Jinkai; Li, Chun; Xie, Jun; Wu, Rui

doi:10.1007/s13146-023-00904-7

Cited by 2 publications

(3 citation statements)

References 41 publications

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“…The use of interpolation and fitting techniques for simulating atmospheric pollutant concentrations has been a consistent focus of research. The accuracy and precision of interpolation have always been key considerations in these studies [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. In the study by Li et al [31], the results indicated that the optimal polynomial fitting (OPF) method accurately reconstructed PM 2.5 fields.…”

Section: Discussionmentioning

confidence: 99%

“…Spatial interpolation methods, widely employed in atmospheric studies and other fields, offer a solution by mitigating the impact of insufficient ground-based observation data on accurately characterizing the spatial and temporal distribution characteristics of PM 2.5. These methods include spatiotemporal statistical models based on Kriging interpolation, spatial and temporal regression models based on Kriging interpolation integrated with remote-sensing AOD data, neural network models based on RBF interpolation, and 3D RBF interpolation for hydrological structure analysis [22][23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

“…They established a DFN model under the multi-level constraints of seismic data and lithology data. This method significantly reduced the uncertainty of simulating and predicting discrete fractures between wells [30].…”

Section: Introductionmentioning

confidence: 99%

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Application of Trigonometric Polynomial Fitting Method in Simulating the Spatial Distribution of PM2.5 Concentration in South-Central China

Chen,

Li,

et al. 2023

Atmosphere

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Near-surface PM2.5 estimates remain a global scientific research challenge due to their effect on human fitness and atmospheric environmental quality. However, practical near-surface PM2.5 estimates are impeded by the incomplete monitoring data. In this study, we propose the trigonometric polynomial fitting (TPF) method to estimate near-surface PM2.5 concentrations in south-central China during 2015. We employ 10-fold cross-validation (CV) to assess the reliability of TPF in estimating practical PM2.5 values. When compared to alternative methods such as the orthogonal polynomial fitting (OBF) method based on Chebyshev basis functions, Kriging interpolation, and radial basis function (RBF) interpolation, our results show that utilizing TPF31, with a maximum order of 3 in the x direction and a maximum order of 1 in the y direction, leads to superior efficiency through error minimization. TPF31 reduces MAE and RMSE by 1.93%, 24%, 6.96% and 3.6%, 23.07%, 10.43%, respectively, compared to the other three methods. In addition, the TPF31 method effectively reconstructs the spatial distribution of PM2.5 concentrations in the unevenly distributed observation stations of Inner Mongolia and the marginal regions of the study area. The reconstructed spatial distribution is remarkably smooth. Despite the non-uniform distribution of observation stations and the presence of missing data, the TPF31 method demonstrates exceptional effectiveness in accurately capturing the inherent physical attributes of spatial distribution. The theoretical and experimental results emphasize that the TPF method holds significant potential for accurately reconstructing the spatial distribution of PM2.5 in China.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Trigonometric Polynomial Fitting Method in Simulating the Spatial Distribution of PM2.5 Concentration in South-Central China

Chen,

Li,

et al. 2023

Atmosphere

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Determination of the fracture closure pressure in fractural-cavity carbonate reservoirs using a failure criterion based on asperity behavior

Tian,

Chen,

et al. 2024

Front. Earth Sci.

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As fluid flow paths in fractural-cavity carbonate reservoirs, fractures have a significant impact on the production performance of carbonate reservoirs. In particular, well production depends on the apertures of the fractures, which vary with the effective stress acting on the fractures. Thus, predicting the fracture closure pressure is crucial for carbonate reservoir development. In our research, fracture closure pressures are derived using the Zienkiewicz–Pande failure criterion, which defines the pressure at which most asperities come into contact. The results reveal that fracture closure is influenced by the geo-stress field, rock mechanics, and spatial location of the fracture. Ultimately, the fracture closure pressure of typical wells located in different tectonic zones in the Shunbei Oilfield is calculated, and the results indicate that the fracture closure pressure in the Shunbei Oilfield is significantly affected by the dip of fractures and the angle between the fracture strike and maximum principal stress. To demonstrate the accuracy of the estimated fracture closure pressure, production performance corresponding to fracture closure was evaluated. It reveals that the flowing bottom pressure decreases rapidly and the recoverable oil reserves reduce when the pressure approaches the fracture closure pressure. This observation verifies that the fracture closure pressure determined using our formula is a feasible predictor of the production performance of fractural-cavity carbonate reservoirs.

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Fracture modeling of carbonate rocks via radial basis interpolation and discrete fracture network

Cited by 2 publications

References 41 publications

Application of Trigonometric Polynomial Fitting Method in Simulating the Spatial Distribution of PM2.5 Concentration in South-Central China

Application of Trigonometric Polynomial Fitting Method in Simulating the Spatial Distribution of PM2.5 Concentration in South-Central China

Determination of the fracture closure pressure in fractural-cavity carbonate reservoirs using a failure criterion based on asperity behavior

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