Application of A∗ algorithm for microstructure and transport properties characterization from 3D rock images

Borello, Eloisa Salina; Peter, Costanzo; Panini, Filippo; Viberti, Dario

doi:10.1016/j.energy.2021.122151

Cited by 12 publications

(5 citation statements)

References 48 publications

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“…Both micromodels have been fully characterized by extracting data with dedicated procedures. In particular, the values of tortuosity were calculated via CFD simulation, and the static values of average pore size and smallest pore size using a procedure based on the A* algorithm [ 10 , 19 , 20 ]. All properties were extracted from a Representative Elementary Volume (REV).…”

Section: Design Of Microfluidic Devicesmentioning

confidence: 99%

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Design, Fabrication, and Experimental Validation of Microfluidic Devices for the Investigation of Pore-Scale Phenomena in Underground Gas Storage Systems

et al. 2023

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The understanding of multiphase flow phenomena occurring in porous media at the pore scale is fundamental in a significant number of fields, from life science to geo and environmental engineering. However, because of the optical opacity and the geometrical complexity of natural porous media, detailed visual characterization is not possible or is limited and requires powerful and expensive imaging techniques. As a consequence, the understanding of micro-scale behavior is based on the interpretation of macro-scale parameters and indirect measurements. Microfluidic devices are transparent and synthetic tools that reproduce the porous network on a 2D plane, enabling the direct visualization of the fluid dynamics. Moreover, microfluidic patterns (also called micromodels) can be specifically designed according to research interests by tuning their geometrical features and surface properties. In this work we design, fabricate and test two different micromodels for the visualization and analysis of the gas-brine fluid flow, occurring during gas injection and withdrawal in underground storage systems. In particular, we compare two different designs: a regular grid and a real rock-like pattern reconstructed from a thin section of a sample of Hostun rock. We characterize the two media in terms of porosity, tortuosity and pore size distribution using the A* algorithm and CFD simulation. We fabricate PDMS-glass devices via soft lithography, and we perform preliminary air-water displacement tests at different capillary numbers to observe the impact of the design on the fluid dynamics. This preliminary work serves as a validation of design and fabrication procedures and opens the way to further investigations.

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Section: Design Of Microfluidic Devicesmentioning

confidence: 99%

“…The two designed patterns differ in terms of tortuosity, heterogeneity, and regularity of the porous volume. The path-finding algorithm A* [ 10 ] and CFD simulation have been used to characterize the pore space of the designed patterns. Soft lithography has been chosen to fabricate PDMS-glass devices.…”

Section: Introductionmentioning

confidence: 99%

Design, Fabrication, and Experimental Validation of Microfluidic Devices for the Investigation of Pore-Scale Phenomena in Underground Gas Storage Systems

et al. 2023

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“…Digital cores built by geostatistical methods [25] mainly match the statistical characteristics of microscopic pores in real cores, but are isotropic. Based on the digital core model, many scholars have been working on new theoretical methods including the maximal ball method [26,27], percolation theory [28], pathfinding approach [29], medial axis extraction algorithm [30] and watershed segmentation algorithm [31], to build a pore network model that reflects the topology of real pore space. All the methods mentioned above have their own specific characterization ranges and scales; in order to obtain a more realistic and overall pore-throat distribution, comprehensive experimental methods for characterizing the full pore-size distribution have become the mainstream in recent years [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Prediction of Rock Pore-Throat Radius Based on Deep Neural Network

Hong,

Li,

Wang

et al. 2023

Energies

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Pore-throat radius is one of the key parameters that characterizes the microscopic pore structure of rock, which has an important impact on oil-gas seepage and the prediction of remaining oil’s microscopic distribution. Currently, the quantitative characterization of a pore-throat radius mainly relies on rock-core experiments, then uses capillary pressure functions, e.g., the J-function, to predict the pore-throat radius of rocks which have not undergone core experiments. However, the prediction accuracy of the J-function struggles to meet the requirements of oil field development during a high water-cut stage. To solve this issue, in this study, based on core experimental data, we established a deep neural network (DNN) model to predict the maximum pore-throat radius Rmax, median pore-throat radius R50, and minimum flow pore-throat radius Rmin of rocks for the first time. To improve the prediction accuracy of the pore-throat radius, the key components of the DNN are preferably selected and the hyperparameters are adjusted, respectively. To illustrate the effectiveness of the DNN model, core samples from Q Oilfield were selected as the case study. The results show that the evaluation metrics of the DNN notably outperform when compared to other mature machine learning methods and conventional J-function method; the root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are decreased by 14–57.8%, 32.4–64.3% and 13.5–48.9%, respectively, and the predicted values are closer to the true values of the pore-throat radius. This method provides a new perspective on predicting the pore-throat radius of rocks, and it is of great significance for predicting the dominant waterflow pathway and in-depth profile control optimization.

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“…Even a correlation between geometric and flux-based tortuosity for LiFePO4 electrodes 10 has been developed. For earth sciences, it provides a reliable estimation of pathways for 3D rock porous media samples as well as can be used to estimate other parameters such as permeability and effective diffusion coefficient helping in applications such as gas storage and reservoir production 11 . Additionally, for durability evaluations of porous composites such as fuel cell electrodes, filtration membranes, polymer foams, ceramics, and powder beds, geometric tortuosity is important 12 .…”

Section: Introductionmentioning

confidence: 99%

“…Although the A-star algorithm is not the only path-search algorithm, and some methods can pre-process the map to guarantee efficiencies such as contraction hierarchies 26 and transit routes 27 , previous findings have demonstrated that A-star is a powerful tool and a well-known best-first search algorithm due to its wide application range in solving problems 28 , 29 . In addition, the A-star algorithm has also been used to estimate geometric tortuosity in 3D porous media 11 .…”

Section: Introductionmentioning

confidence: 99%

Evaluation of geometric tortuosity for 3D digitally generated porous media considering the pore size distribution and the A-star algorithm

Ávila

Pagalo

Espinoza‐Andaluz

2022

Sci Rep

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Porous materials are of great interest in multiple applications due to their usefulness in energy conversion devices and their ability to modify structural and diffusive properties. Geometric tortuosity plays an important role in characterizing the complexity of a porous medium. The literature on several occasions has related it as a parameter dependent on porosity only. However, due to its direct relationship with the morphology of the medium, a deeper analysis is necessary. For this reason, in the present study, the analysis of the geometric tortuosity is proposed considering the porosity and the pore size distribution. Geometric tortuosity in artificially generated digital porous media is estimated using the A-star algorithm and the Pore Centroid method. By performing changes in the size of the medium and the distribution of the pore size, results are obtained that indicate that the geometric tortuosity does not only depend on the porosity. By maintaining the same porosity, the geometric tortuosity increases if the pore size is reduced. Similarly, these pore size effects are greater if the size of the medium is reduced. The A-star algorithm was found to be more suitable to characterize the majority of paths within the half-pore. On the other hand, to increase the size, the Pore Centroid method is the most appropriate. Finally, three types of correlations were generated relating tortuosity with porosity and pore size. All the correlations were determined with 95% of interval confidence.

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Application of A∗ algorithm for microstructure and transport properties characterization from 3D rock images

Cited by 12 publications

References 48 publications

Design, Fabrication, and Experimental Validation of Microfluidic Devices for the Investigation of Pore-Scale Phenomena in Underground Gas Storage Systems

Design, Fabrication, and Experimental Validation of Microfluidic Devices for the Investigation of Pore-Scale Phenomena in Underground Gas Storage Systems

Quantitative Prediction of Rock Pore-Throat Radius Based on Deep Neural Network

Evaluation of geometric tortuosity for 3D digitally generated porous media considering the pore size distribution and the A-star algorithm

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