arXiv subjects

Behzad Ghanbarian

Publications and source records attributed to Behzad Ghanbarian.

At least 19 recordsLinked to original sources

arXiv2026

Quantum-Enhanced Vision Transformer for Flood Detection using Remote Sensing Imagery

Reliable flood detection is critical for disaster management, yet classical deep learning models often struggle with the high-dimensional, nonlinear complexities inherent in remote sensing data. To mitigate these limitations, we introduced a novel Quantum-Enhanced Vision Transformer (ViT) that synergizes the global context-awareness of transformers with the expressive feature extraction capabilities of quantum computing. Using remote sensing imagery, we developed a hybrid architecture that processes inputs through parallel pathways, a ViT backbone and a quantum branch utilizing a 4-qubit parameterized quantum circuit for localized feature mapping. These distinct representations were fused to optimize binary classification. Results showed that the proposed hybrid model significantly outperformed a classical ViT baseline, increased overall accuracy from 84.48% to 94.47% and the F1-score from 0.841 to 0.944. Notably, the quantum integration substantially improved discriminative power in complex terrains for both class. These findings validate the potential of quantum-classical hybrid models to enhance precision in hydrological monitoring and earth observation applications.

cs.LG↗

arXiv2026

Boundary-Induced Biases in Climate Networks of Extreme Precipitation and Temperature

To address spatial boundary effects in climate networks, two surrogate-based correction methods, (1) subtraction and (2) division, have been widely applied in the literature. In the subtraction method, an original network measure is adjusted by subtracting the expected value obtained from a surrogate ensemble, whereas in the division method, it is normalized by dividing by this expected value. However, to the best of our knowledge, no prior study has assessed whether these two correction approaches yield statistically different results. In this study, we constructed complex networks of extreme precipitation and temperature events (EPEs and ETEs) across the CONUS for both summer (June-August, JJA) and winter (December-February, DJF) seasons. We computed key network metrics degree centrality (DC), clustering coefficient (CC), mean geographic distance (MGD), and betweenness centrality (BC) and applied both correction methods. Although the corrected spatial patterns generally appeared visually similar, statistical analyses revealed that the network measures derived from the subtraction and division methods were significantly different at the 95 percent confidence level. Across the CONUS, network hubs of EPEs were primarily concentrated in the northwestern United States during summer and shifted toward the east during winter, reflecting seasonal differences in the dominant atmospheric drivers. In contrast, the ETE networks showed strong spatial coherence and pronounced regional teleconnections in both seasons, with higher connectivity and longer synchronization distances in winter, consistent with large-scale circulation patterns such as the Pacific-North American and North Atlantic Oscillation modes. Our results indicated that the network metrics CC and MGD were more sensitive to the correction methods than the DC and BC, particularly in the EPE networks.

stat.AP↗

arXiv2026

Regional-Scale Estimation of Soil Hydraulic Conductivity Using the Kansas Mesonet

In soil physics, saturated hydraulic conductivity, K_sat, is among the most important hydraulic properties with broad applications to modeling flow and transport under saturated conditions. Its accurate estimation, however, is challenging and requires precise characterization of pore space. In this study, we applied concepts of critical path analysis (CPA) to estimate K_sat from soil water retention curve. To evaluate the CPA, we used 313 undisturbed soil samples from the Kansas Mesonet database in which the value of K_sat spans over five orders of magnitude in variation. We found that the CPA estimated K_sat reasonably well with root mean square log-transformed error RMSLE = 0.87. For most samples, the predicted values were around the 1:1 line within a factor of 10 of the measurements. We also estimated K_sat using five other methods but none was more accurate than the CPA.

physics.geo-ph↗

arXiv2024

Numerical Simulations of Geomechanical Deformation, Fluid Flow and Reactive Transport in Shale Rough-Walled Microfractures

Improving hydrocarbon production with hydraulic fracturing from unconventional reservoirs requires investigating transport phenomena at the single fracture level. In this study, we simulated geomechanical deformation, fluid flow, and reactive transport to understand the effect of hydraulic fracturing treatment on permeability evolution in shale rough-walled fractures. Using concepts of fractional Brownian motion and surface roughness characterizations with laser profilometer, we first generated three rough-walled microfractures consistent with three laboratory experiments (i.e., E4, E5 and E6). After that, the generated microfractures were subjected to a confining pressure in accord with experimental conditions, and geomechanical deformation was simulated. We used the OpenFOAM software package to simulate the fluid flow and permeability. By comparing the simulated permeability values with the experimentally measured ones we found relative errors equal to 28, 15 and 200% respectively for the experiments E4, E5 and E6. After calibration, however, the relative error dropped below 4%. We next simulated the reactive transport using the GeoChemFOAM solver and investigated permeability evolution in the deformed microfractures. We found that after 10 hrs of reactive transport simulations, permeability increased by 47%, on average, in all cases studied here.

physics.flu-dyn↗

arXiv2024

Modeling and scaling spontaneous imbibition with generalized fractional flow theory and non-Boltzmann transformation

Spontaneous imbibition (SI) is a process by which liquid is drawn into partially saturated porous media by capillary forces, relevant for subsurface processes like underground fluid storage and withdrawal. Accurate modeling and scaling of counter-current SI have long been challenging. In this study, we proposed a generalized fractional flow theory (GFFT) using the Hausdorff fractal derivative, combined with non-Boltzmann scaling. The model links imbibition distance to time through the power law exponent alpha/2, where alpha is the fractal index (0 < alpha < 2 in this study). We applied the GFFT to various experimental and stimulated datasets of both porous and fractured media, finding that alpha varied with factors such as contact angle (of the imbibing fluid), dynamic viscosity, pore structure, and fracture properties. By analyzing SI data from sandstones, diatomite, carbonate, and synthetic porous media, we demonstrated that the non-Boltzmann scaling provided a better collapse of the SI data than the traditional Boltzmann approach alpha = 1), with alpha values ranging from 0.88 to 1.54. These deviations illustrate the model's adaptability to different porous materials. Using the GFFT, we expect to better predict fluid imbibition rates when properties like porosity, permeability, initial and maximum saturations, viscosity, and wettability are known, offering a more accurate alternative to traditional models.

physics.flu-dyn↗

arXiv2024

Scaling mean annual peak flow scaling with upstream basin area

Understanding how annual peak flow, $Q_p$, relates to upstream basin area, $A$, and their scaling have been one of the challenges in surface hydrology. Although a power-law scaling relationship (i.e., $Q_p \propto A^α$) has been widely applied in the literature, it is purely empirical, and due to its empiricism the interpretation of its exponent, a, and its variations from one basin to another is not clear. In the literature, different values of a have been reported for various datasets and drainage basins of different areas. Invoking concepts of percolation theory as well as self-affinity, we derived universal and non-universal scaling laws to theoretically link $Q_p$ to $A$. In the universal scaling, we related the exponent $α$ to the fractal dimensionality of percolation, $D_x$. In the non-universal scaling, in addition to $D_x$, the exponent a was related to the Hurst exponent, $H$, characterizing the boundaries of the drainage basin. The $D_x$ depends on the dimensionality of the drainage system (e.g., two or three dimensions) and percolation class (e.g., random or invasion percolation). We demonstrated that the theoretical universal and non-universal bounds were in well agreement with experimental ranges of a reported in the literature. More importantly, our theoretical framework revealed that greater a values are theoretically expected when basins are more quasi two-dimensional, while smaller values when basins are mainly quasi three-dimensional. This is well consistent with the experimental data. We attributed it to the fact that small basins most probably display quasi-two-dimensional topography, while large basins quasi-three-dimensional one.

cond-mat.stat-mech↗

arXiv2022

Estimating oil recovery factor using machine learning: Applications of XGBoost classification

In petroleum engineering, it is essential to determine the ultimate recovery factor, RF, particularly before exploitation and exploration. However, accurately estimating requires data that is not necessarily available or measured at early stages of reservoir development. We, therefore, applied machine learning (ML), using readily available features, to estimate oil RF for ten classes defined in this study. To construct the ML models, we applied the XGBoost classification algorithm. Classification was chosen because recovery factor is bounded from 0 to 1, much like probability. Three databases were merged, leaving us with four different combinations to first train and test the ML models and then further evaluate them using an independent database including unseen data. The cross-validation method with ten folds was applied on the training datasets to assess the effectiveness of the models. To evaluate the accuracy and reliability of the models, the accuracy, neighborhood accuracy, and macro averaged f1 score were determined. Overall, results showed that the XGBoost classification algorithm could estimate the RF class with reasonable accuracies as high as 0.49 in the training datasets, 0.34 in the testing datasets and 0.2 in the independent databases used. We found that the reliability of the XGBoost model depended on the data in the training dataset meaning that the ML models were database dependent. The feature importance analysis and the SHAP approach showed that the most important features were reserves and reservoir area and thickness.

cs.LG↗

arXiv2022

Estimating oil and gas recovery factors via machine learning: Database-dependent accuracy and reliability

With recent advances in artificial intelligence, machine learning (ML) approaches have become an attractive tool in petroleum engineering, particularly for reservoir characterizations. A key reservoir property is hydrocarbon recovery factor (RF) whose accurate estimation would provide decisive insights to drilling and production strategies. Therefore, this study aims to estimate the hydrocarbon RF for exploration from various reservoir characteristics, such as porosity, permeability, pressure, and water saturation via the ML. We applied three regression-based models including the extreme gradient boosting (XGBoost), support vector machine (SVM), and stepwise multiple linear regression (MLR) and various combinations of three databases to construct ML models and estimate the oil and/or gas RF. Using two databases and the cross-validation method, we evaluated the performance of the ML models. In each iteration 90 and 10% of the data were respectively used to train and test the models. The third independent database was then used to further assess the constructed models. For both oil and gas RFs, we found that the XGBoost model estimated the RF for the train and test datasets more accurately than the SVM and MLR models. However, the performance of all the models were unsatisfactory for the independent databases. Results demonstrated that the ML algorithms were highly dependent and sensitive to the databases based on which they were trained. Statistical tests revealed that such unsatisfactory performances were because the distributions of input features and target variables in the train datasets were significantly different from those in the independent databases (p-value < 0.05).

cs.LG↗

arXiv2021

Effects of particle shape and size distribution on hydraulic properties of grain packs: An experimental study

Uniform and multi-dispersed grain packs have been frequently used to conceptually study flow in porous media. Numerical simulations were previously used to address the effect of particle shape on characteristics, such as pore space fractal dimension, moisture characteristic curve (MCC) and saturated hydraulic conductivity (SHC) of grain packs. However, experimental observations are still required since fractal-based approaches have been extensively proposed to model various properties in porous media. In this study, 16 angular sand and 16 spherical glass bead samples with different particle size distributions (PSDs) from well- to poorly-sorted were packed. The MCC was measured using the combination of sandbox and pressure plates methods. The pore space fractal dimension (DMCC), calculated from the measured MCC, ranged from 0.80 to 2.86 in sand and from -0.18 to 2.81 in glass bead packs, which indicated that DMCC may be negative in homogenous media (e.g., glass bead packs) consistent with several studies in the literature. Results showed greater DMCC for the sand packs than the glass bead packs with the same geometric mean diameter values and PSDs. This clearly demonstrated the effect of particle shape on DMCC in the studied packs. The critical path analysis (CPA) approach was used to estimate the SHC measured using the constant-head method. We found that the CPA estimated the SHC accurately, within a factor of four of the measurements on average. Although the CPA is theoretically known to be accurate in media with broad pore size distributions, we experimentally found that it estimated the SHC in various types of grain packs reasonably well.

physics.geo-ph↗

arXiv2021

Determining effective permeability at reservoir scale: Numerical simulations and theoretical modeling

Determining the effective permeability (keff) of geological formations has broad applications to site remediation, aquifer discharge or recharge, hydrocarbon production, and enhanced oil recovery. The objectives of this study are: (1) to explore an approach to estimating keff at the reservoir scale using the critical path analysis (CPA), (2) to evaluate the accuracy of this new approach by comparing the estimated keff to the numerically simulated effective permeability, and (3) to compare the performance of CPA estimates of keff to estimates by three other models i.e., perturbation theory (PT), effective-medium approximation (EMA), and renormalization group theory (RGT). We construct two- and three-dimensional random (uncorrelated) geologic formations based on permeability measurements from the Borden site and assume that the permeability distribution conforms to the log-normal probability density function over a wide range of means and standard deviations. Comparing keff estimated via CPA to keff values derived from numerical flow simulations indicates that CPA provides accurate estimations in both two and three dimensions over a wide range of heterogeneity levels, similar to RGT. Inter-model comparisons show that although PT and EMA provide reasonable keff estimations in rather homogeneous formations, they substantially overestimate the effective permeability in highly heterogeneous formations.

physics.geo-ph↗

arXiv2021

Incorporating effects of surface roughness and probing molecule size for estimation of soil specific surface area

The pore-solid interface and its characteristics play a key role in chemical interactions between minerals in the solid soil matrix and the liquid in pore space and, consequently, solute transport in soils. Specific surface area (SSA), typically measured to characterize the pore-solid interface, depends not only on the particles size distribution, but also particle shapes and surface roughness. In this note, we investigate the effects of surface roughness and probing molecule size on SSA estimation, employ concepts from fractals, and theoretically estimate specific surface area from particle size distribution and water retention curve (WRC). The former is used to characterize the particle sizes and the latter to approximately quantify the pore-solid interface roughness by determining the surface fractal dimension Ds. To evaluate our approach, we use five Washington and twenty one Arizona soils for which both particle size distributions and water retention curves were accurately measured over a wide range of particle sizes and matric potentials. Comparison with the experiments show that the proposed method estimates the SSA reasonably well with root mean square error RMSE = 16.8 and 30.1 m2/g for the Washington and Arizona datasets, respectively.

physics.geo-ph↗

arXiv2021

Scale-dependent permeability and formation factor in porous media: Applications from percolation theory

Understanding porous media properties and their scale dependence have been an active subject of research in the past several decades in hydrology, geosciences and petroleum engineering. The scale dependence of flow in porous media is attributed to small- and large-scale heterogeneities, such as pore size distribution, pore connectivity, long-range correlations, fractures and faults orientations, and spatial and temporal variations. The main objective of this study was to investigate how permeability (k) and formation factor (F) vary with sample dimension at small scales by means of a combination of pore-network modeling and percolation theory. For this purpose, the permeability and formation factor were simulated in twelve three-dimensional pore networks with different levels of pore-scale heterogeneities. Simulations were carried out at five different network sizes, i.e., 1130, 2250, 3380, 4510 and 6770 microns. Four theoretical models were also developed based on percolation theory to estimate the scale dependence of permeability and formation factor from the pore-throat radius distribution. In addition, two other theoretical scale-dependent permeability models were proposed to estimate permeability at different scales from the pore-throat radius distribution and formation factor. Comparing theoretical estimations with numerical simulations showed that the proposed models estimate the scale dependence of permeability and formation factor reasonably. The calculated relative error (RE) ranged between -3.7 and 3.8% for the permeability and between 0.21 and 4.04% for the formation factor in the studied pore-networks.

physics.geo-ph↗

arXiv2020

Gas relative permeability in unconventional reservoir rocks

Accurate modeling of gas relative permeability has practical applications in oil and gas exploration, production and recovery of unconventional reservoirs. In this study, we apply concepts from the effective-medium approximation (EMA) and universal power-law scaling from percolation theory. Although the EMA has been successfully used to estimate relative permeability in conventional porous media, to the best of our knowledge, its applications to unconventional reservoir rocks have not been addressed yet. The main objective of this study, therefore, is to evaluate the efficiency of EMA, in combination with universal power-law scaling from percolation theory, in estimating gas relative permeability from pore size distribution and pore connectivity. We presume that gas flow is mainly controlled by two main mechanisms contributing in parallel (1) hydraulic flow and (2) molecular flow. We then apply the EMA to determine effective conductances and, consequently, gas relative permeability at higher gas saturations, and the universal scaling from percolation theory at lower gas saturation values. Comparisons with two pore-network simulations and six experimental measurements from the literature show that, in the absence of microfractures, the proposed model estimates gas relative permeability reasonably well in shales and tight porous rocks. More specifically, we found that the crossover point, gas saturation at which transport crosses from percolation theory to the EMA, is non-universal. The value of crossover gas saturation is a function of pore space characteristics such as pore size distribution broadness and critical gas saturation. This means that one should expect the crossover gas saturation to vary from one rock sample to another.

physics.geo-ph↗

arXiv2020

Modeling single-phase permeability in uniform grain packs

Accurate estimation of single-phase permeability (k) has broad application in numerous areas, particularly modeling flow and transport in porous materials. Various techniques have been proposed in the literature to estimate k from other medium's properties, such as porosity, grain and/or pore size distribution, and pore connectivity. Among them critical path analysis (CPA) from statistical physics, first developed to model fluid flow in media with broad conductance distributions, has been successfully applied to heterogeneous soils and rocks. However, its application to uniform sphere and/or glass bead packs that represent homogeneous porous media with narrow conductance distributions needs to be investigated. In this study, we invoke concepts from CPA and estimate k from average grain diameter and formation factor in uniform sand and glass bead packs. By comparing theory with eight datasets including 105 packs from the literature, we demonstrate that CPA estimates permeability in homogeneous media accurately. We also compare our CPA-based model estimations with those from the Kozeny-Carman, Revil and Cathles, and RGPZ models. Results indicate that the CPA approach estimates k more precisely than other three models studied here.

physics.geo-ph↗

arXiv2020

Modeling water relative permeability in uniform grain packs

Accurate estimation of water relative permeability has been of great interest in various research areas because of its broad applications in soil physics and hydrology as well as oil and gas production and recovery. Critical path analysis (CPA), a promising technique from statistical physics, is well known to be applicable to heterogeneous media with broad conductance or pore size distribution (PSD). By heterogeneity, we mean variations in the geometrical properties of pore space. In this study, we demonstrate that CPA is also applicable to packings of spheres of the same size, known as homogeneous porous media. More specifically, we apply CPA to model water relative permeability (krw) in mono-sized sphere packs whose PSDs are fairly narrow. We estimate the krw from (1) the PSD and (2) the PSD and saturation-dependent electrical conductivity (σ_r) for both drainage and imbibition processes. We show that the PSD of mono-sized sphere packs approximately follows the log-normal probability density function. Comparison with numerical simulations indicate that both the imbibition and drainage krw are estimated from the PSD and σ_r data more accurately than those from the PSD. We show that CPA can estimate krw in mono-sized sphere packs precisely.

physics.geo-ph↗

arXiv2019

Surface fractal dimension and its theoretical relationship with adsorbed water content

Surface fractal dimension Ds is a quantity describing the roughness of pore-solid interface where all interactions between solid matrix and fluid in the pore space occur. Ds also quantifies surface area; the higher the surface fractal dimension the greater the surface area. Therefore, at some high enough tension head, where a thin layer of water covers the pore-solid interface, one should expect adsorbed water content to be related to Ds in water-wet porous media. In this technical note, we develop a theoretical relationship between the surface fractal dimension, Ds, and the adsorbed water content, θ_ads, using concepts from van der Waals and electrostatic forces. The proposed model sheds light on constant coefficients of logarithmic equations found empirically between Ds and water contents retained at 1500 and 10000 kPa tension heads. Results also show that our theoretical model estimates Ds from first physical principles for 164 soil samples accurately.

physics.geo-ph↗

arXiv2019

Wetting-phase relative permeability in porous media with bi-modal pore size distributions

Modeling fluid flow in dual-porosity media with bi-modal pore size distributions has practical applications to understanding transport in multi-scale systems such as natural soils. Dual-porosity media are typically formed of two domains: (1) structure and (2) texture. The former mainly incorporates macropores, while the latter contains micropores. Although there exist models based on the series-parallel tubes approach, here we apply concepts from critical path analysis, a theoretical technique from statistical physics, to estimate water relative permeability (krw) in dual-porosity media. For this purpose, we use two datasets from the literature collected under two different cultivation conditions: (i) conventional tillage (CT) and (ii) non-tillage (NT). Each dataset consists of 13 soil samples for which capillary pressure curve and water relative permeability were measured at 500 data point over a wide range of water saturation. We estimate the water relative permeability from the measured capillary pressure curve using two methods: (1) critical path analysis (CPA), and (2) series-parallel tubes (vG-M), both models adapted for dual-porosity media. Comparing the theoretical estimations with the experimental measurements shows that CPA resulted in more accurate krw estimations than vG-M. We demonstrate that precise estimation of krw via CPA requires accurate characterization of capillary pressure curve and precise determination of the crossover point separating the structure domain from the texture one.

physics.geo-ph↗

arXiv2018

Gas transport in partially-saturated sand packs

Understanding gas transport in porous media and its mechanism has broad applications in various research areas, such as carbon sequestration in deep saline aquifers and gas explorations in reservoir rocks. Gas transport is mainly controlled by pore space geometrical and morphological characteristics. In this study, we apply a physically-based model developed using concepts from percolation theory (PT) and the effective-medium approximation (EMA) to better understand diffusion and permeability of gas in packings of angular and rounded sand grains as well as glass beads. Two average sizes of grain i.e., 0.3 and 0.5 mm were used to pack sands in a column of 6 cm height and 4.9 cm diameter so that the total porosity of all packs was near 0.4. Water content, gas-filled porosity (also known as gas content), gas diffusion, and gas permeability were measured at different capillary pressures. The X-ray computed tomography method and the 3DMA-Rock software package were applied to determine the average pore coordination number z. Results showed that both saturation-dependent diffusion and permeability of gas showed almost linear behavior at higher gas-filled porosities, while deviated substantially from linear scaling at lower gas saturations. Comparing the theory with the diffusion and permeability experiments showed that the determined value of z ranged between 2.8 and 5.3, not greatly different from X-ray computed tomography results. The obtained results clearly indicate that the effect of the pore-throat size distribution on gas diffusion and permeability was minimal in these sand and glass bead packs.

physics.geo-ph↗