Permeability Prediction Research Articles

Prediction of Mycobacterium tuberculosis cell wall permeability using machine learning methods.

Tuberculosis (TB) caused by the bacteria Mycobacterium tuberculosis (M. tb), continues to pose a significant worldwide health threat. The advent of drug-resistant strains of the disease highlights the critical need for novel treatments. The unique cell wall of M. tb provides an extra layer of protection for the bacteria and hence only compounds that can penetrate this barrier can reach their targets within the bacterial cell wall. The creation of a reliable machine learning (ML) model to predict the mycobacterial cell wall permeability of small molecules is presented in this work and four ML algorithms, including Random Forest, Support Vector Machines (SVM), k-nearest Neighbour (k-NN) and Logistic Regression were trained on a dataset of 5368 compounds. RDKit and Mordred toolkits were used to calculate features. To determine the most effective model, various performance metrics were used such as accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve. The best-performing model was further refined with hyperparameter tuning and tenfold cross-validation. The SVM model with filtering outperformed the other machine learning models and demonstrated 80.26% and 81.13% accuracy on the test and validation datasets, respectively. The study also provided insights into the molecular descriptors that play the most important role in predicting the ability of a molecule to pass the M. tb cell wall, which could guide future compound design. The model is available at https://github.com/PGlab-NIPER/MTB_Permeability .

Permeability prediction method of unconsolidated sandstone reservoirs using CT scanning technology and random forest model

Developing unconsolidated sandstone reservoirs presents a formidable challenge due to their loose formation, which often triggers alterations in pore parameters and seepage characteristics during water injection processes. This study focuses on a specific reservoir, utilizing micro-CT scanning to examine the intricate relationship between permeability and pore throat structure. Leveraging a random forest model, we establish an empirical formula tailored for high permeability reservoirs. Furthermore, we conduct in-situ CT scanning experiments across various displacement multiples to analyze the pore structure of unconsolidated sandstone cores. The derived relationship curves elucidate the positive correlations between porosity and average pore throat radius with displacement multiples, while revealing a negative correlation with tortuosity. These findings enable the formulation of quantitative formulas for permeability and displacement multiples within the studied block. Such insights prove instrumental in devising effective water injection development strategies, predicting dynamic reserves, and projecting water drive development for analogous unconsolidated sandstone reservoirs undergoing high water cut phases.

Applying machine learning for hydraulic flow unit classification and permeability prediction: case study from carbonate reservoir in the Southern part, Song Hong basin

Machine learning (ML) is an artificial intelligence (AI) that enables computer systems to classify, cluster, identify, and analyze vast and complex sets of data while eliminating the need for explicit instructions and programming. For decades, machine learning has become helpful for complex reservoir characterization such as carbonate reservoirs. Permeability prediction from well logs is a significant challenge, especially when the core data is rarely available due to its high cost. In this study, we aimed to bridge this gap by demonstrating the practical application of integrating Hydraulic Flow Units (HFU) and machine learning methods. Our goal was to provide a reliable estimation of permeability using core and wireline logging data in the complex Middle Miocene carbonate reservoir of the CX gas field in the southern part of the Song Hong basin. In the first step, due to the reservoir’s heterogeneity, the core plug dataset was classified into 5 HFUs based on the flow zone indicators (FZI) concept from the modified Kozeny-Carman equation using unsupervised machine learning - K-means method. The porosity - permeability for each HFU was defined after HFU clustering. In the second step, we designed three different workflows to predict permeability and HFU using supervised machine learning from a combination of core and log data. These workflows were rigorously test and compared with the core data. The most accurate result was chosen as the base, providing a high confidence level in our predictions’ reliability.

Optimizing pervious concrete with machine learning: Predicting permeability and compressive strength using artificial neural networks

This study makes a significant contribution to the field of pervious concrete by using machine learning to innovatively predict both mechanical and hydraulic performance. Unlike existing methods that rely on labor-intensive trial-and-error experiments, our proposed approach leverages a multilayer perceptron network. To develop this approach, we compiled a comprehensive dataset comprising 271 sets and 3,252 experimental data points. Our methodology involved evaluating 22,246 network configurations, employing Monte Carlo cross-validation over 20 iterations, and using 4 training algorithms, resulting in a total of 1,779,680 training iterations. This results in an optimized model that integrates diverse mix design parameters, enabling accurate predictions of permeability and compressive strength even in the absence of experimental data, achieving R² values of 0.97 and 0.98, respectively. Sensitivity analyses validate the model's alignment with established principles of pervious concrete behavior. By demonstrating the efficacy of machine learning as a complementary tool for optimizing pervious concrete mix designs, this research not only addresses current methodological limitations but also lays the groundwork for more efficient and effective approaches in the field.

Development of a new hydraulic electric index for rock typing in carbonate reservoirs

Rock typing techniques have relied on either electrical or hydraulic properties. The study introduces a novel approach for reservoir rock typing, the hydraulic-electric index (HEI), which combines the strengths of traditional electrical and hydraulic rock typing methods to characterize carbonate reservoirs more accurately. By normalizing the ratio of permeability and formation resistivity factor (K/FRF) with respect to porosity, the HEI method is applied to two datasets of carbonate core samples: dataset 1 consists of 112 carbonate core samples from the Tensleep formation in the Bighorn basin of Wyoming and Montana, and dataset 2 includes 81 carbonate core samples from the Asmari formation in the south-west of Iran. Statistical analysis confirms the effectiveness of the HEI in predicting permeability, with high determination coefficients for both datasets (resulting in determination coefficients (R2) of 0.965 and 0.904 for dataset 1 and dataset 2, respectively). The results classify the rock samples into distinct rock types, nine rock types for dataset 1 and four rock types for dataset 2, and demonstrate the HEI ability to capture the relationship between hydraulic conductivity and electrical resistivity in carbonate reservoir rocks. Applying the HEI method to the validation dataset yielded highly accurate permeability predictions, with average of determination coefficients of 0.883 and 0.859 for dataset 1 and dataset 2, respectively. Validation of the HEI method further confirms (20% of the dataset was set aside for validation, while the remaining 80% was used for the rock typing approach (5 folds)) its accuracy in predicting permeability, highlighting its robust predictive capacity for estimating permeability in carbonate reservoirs.

Multi-output machine learning for addressing the trade-off between water permeability and wetting resistance in membrane distillation

Membrane distillation (MD) has gained extensive attention for the desalination of hypersaline brine. Nevertheless, the performance of MD is often hampered by the inherent trade-off between water permeability and wetting resistance. Elucidating the fundamental basis for this trade-off is crucial for the development of membrane that minimize this limitation. To address this, we trained a multi-output machine learning (ML) model that incorporates various factors, including membrane properties, operational conditions, and solution composition, to predict the performance of MD process. Our model exhibited the best performances for prediction of permeability and wetting resistance, indicted by R2 of 0.901 and 0.873, respectively. The Shapley Additive exPlanations (SHAP) method were employed to interpret the model and extracted significant insights into the contributions of various factors to the trade-off between permeability and wetting resistance, which revealed the surprising importance of the LEP and porosity, as a route for minimizing the trade-off effect. Subsequently, multi-target optimization was performed to address the current permeability-wetting trade-off limitation and the optimal combination of LEP and porosity tailored to different application requirements were provided. This study presents a novel multi-output ML model for predicting the performance of MD process and offers mechanistic insights into the wetting resistance-water permeability trade-off, which represents a paradigm shift for the design of high-performance MD process.

Deep learning-based prediction of velocity and temperature distributions in metal foam with hierarchical pore structure

Constrained by the substantial computational time required for numerical simulation, a deep learning technique is applied to investigate fluid flow and heat transfer processes in metal foam with a hierarchical pore structure. This work adopted a 3D convolutional neural network (CNN) combining U-Net architecture to predict velocity and temperature distributions, alongside corresponding permeability and overall heat transfer coefficient. This approach demonstrates excellent capability in intricate image segmentation. The training sets were acquired by lattice Boltzmann method (LBM) simulations. The CNN model, trained on a substantial amount of data, demonstrates remarkable precision, exhibiting mean relative errors of 0.57% for permeability prediction and 2.27% for overall heat transfer coefficient prediction. Moreover, in CNN prediction, a broader range of structure parameters and boundary conditions beyond those in the training set was used to evaluate the practicability of the trained CNN model. In contrast to numerical simulation, the CNN model economizes approximately 95.41% and 99.57% of computational time for velocity and temperature distribution prediction, respectively, providing a novel approach for exploring transport processes in metal foam with hierarchical pore structure.

Reservoir Permeability Prediction Based on Machine Learning

Reservoir Permeability Prediction Based on Machine Learning

A Morphological Method for Predicting Permeability in Fractured Tight Sandstone Reservoirs Based on Electrical Imaging Logging Porosity Spectrum

Summary Permeability is a crucial parameter in formation evaluation, which reflects reservoir fluid mobility and directly influences subsequent development and production. In conventional to tight sandstone formations without fractures, numerous methods have been developed to predict permeability. However, permeability prediction accuracy based on the current method is low in fractured tight sandstone reservoirs due to the influence of heterogeneity, and little research is focused on permeability evaluation in such reservoirs. Conventional wireline and nuclear magnetic resonance (NMR) logging lose their role in fracture parameter evaluation, whereas electrical imaging logging is available for reflecting fracture information. Hence, we adopt electrical imaging logging, instead of conventional wireline and NMR logging, to predict permeability in fractured tight sandstone reservoirs. In this study, we propose a morphological method for permeability prediction using electrical imaging logging. Initially, we process the electrical imaging logging data to obtain the porosity spectra and determine the peak of the primary porosity spectrum. We then extract the mean and standard deviation of the primary porosity spectrum based on its distribution morphology. Meanwhile, we also use the normal distribution function to fit the primary porosity spectrum and accumulate the amplitudes of the original and fitted porosity spectra, respectively. When the cumulative amplitude of the fitted spectra stabilizes, the corresponding porosity indicates the boundary between primary and secondary pores. This boundary allows us to calculate the proportion of primary pores to total pores. Permeability in fractured tight sandstone formations is influenced by both primary and secondary pores, showing a strong correlation with the proportion of primary porosity. Finally, we establish a permeability prediction model based on total porosity and the proportion of matrix pores. The reliability of our established permeability evaluation model is confirmed by comparing the predicted permeabilities with core-derived results in the Triassic Chang 63 Member of the Jiyuan area in the Ordos Basin. In unfractured formations, the predicted permeability based on our raised model closely matches the results derived solely from total porosity. In fractured formations, however, the calculated permeability using our proposed model aligns more closely with the core-derived result, while predictions based on current and NMR models tend to underestimate permeability.

CycPeptMP: enhancing membrane permeability prediction of cyclic peptides with multi-level molecular features and data augmentation.

Cyclic peptides are versatile therapeutic agents that boast high binding affinity, minimal toxicity, and the potential to engage challenging protein targets. However, the pharmaceutical utility of cyclic peptides is limited by their low membrane permeability-an essential indicator of oral bioavailability and intracellular targeting. Current machine learning-based models of cyclic peptide permeability show variable performance owing to the limitations of experimental data. Furthermore, these methods use features derived from the whole molecule that have traditionally been used to predict small molecules and ignore the unique structural properties of cyclic peptides. This study presents CycPeptMP: an accurate and efficient method to predict cyclic peptide membrane permeability. We designed features for cyclic peptides at the atom-, monomer-, and peptide-levels and seamlessly integrated these into a fusion model using deep learning technology. Additionally, we applied various data augmentation techniques to enhance model training efficiency using the latest data. The fusion model exhibited excellent prediction performance for the logarithm of permeability, with a mean absolute error of $0.355$ and correlation coefficient of $0.883$. Ablation studies demonstrated that all feature levels contributed and were relatively essential to predicting membrane permeability, confirming the effectiveness of augmentation to improve prediction accuracy. A comparison with a molecular dynamics-based method showed that CycPeptMP accurately predicted peptide permeability, which is otherwise difficult to predict using simulations.

Carbonate reservoir characterization and permeability modeling using Machine Learning ـــ a study from Ras Fanar field, Gulf of Suez, Egypt

Predicting facies and petrophysical properties along and between wells is challenging in carbonate reservoir modeling. In the Nullipore carbonate reservoir, Ras Fanar field, depositional and long-term diagenetic processes result in a high degree of heterogeneity and complex distribution of facies, which in turn affect the reservoir quality. This provides a significant obstacle to building accurate geological models. This study integrates thin sections, routine core analyses, and well logging data to overcome such difficulties and model the Nullipore carbonate facies and permeability. The detailed petrographic analysis revealed the existence of seven microfacies in the reservoir, which are summed up into three facies associations (FAs), each of which represents a specific reservoir rock type (RRT): (1) supratidal FA, (2) intertidal FA, and (3) shallow subtidal FA. The three FAs were correlated with the gamma-ray logs to create facies logs for the studied wells, which were further populated via the Truncated Gaussian Simulation method. Cross-validation was used to evaluate the model's accuracy. The analysis of the available core data infers that the three RRTs are prospective and have a wide permeability distribution. However, RRT3 constitutes the best reservoir quality. The sedimentological analysis revealed that the long-term diagenetic events, involving the dolomitization of limestone and the dissolution of allochems have a major role in improving the pore connectivity and permeability of the reservoir. Fracture characterization discloses that fractures play a significant role in fluid storage and migration. Three Machine Learning (ML) models, including Adaptive boosting (AdaBoost), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB), were developed to integrate the RRTs, porosity, and permeability to improve permeability prediction. Statistical analysis revealed that the XGB model outperforms other models and exhibits the highest prediction performance. The present study provides further insights into the characterization and modeling of facies and permeability of complex carbonate reservoirs. It can be applied in similar geological settings to better interpretation of depositional and diagenetic controls on reservoir quality assessment and aid in the field development plan.

Study on the Mechanical Parameters and Permeability of Coal Reservoirs at Different Temperatures.

Deep coal reservoirs, as opposed to their shallower counterparts, exhibit characteristics of higher temperatures and pressures. These conditions affect the fracture structure and mechanical properties of coal, which in turn controls permeability. Substantial studies have been conducted to determine the effects of overburden pressure on permeability, but the correlation between the temperature and mechanical parameters/permeability of coal remains unclear. This study focused on low-rank bituminous coal from the southern edge of the Junggar Basin in Xinjiang. Using experiments conducted on seepage and mechanics at different depths (considering effective stress and temperature), the study investigated how temperature affects the mechanical parameters and permeability of coal column samples. A permeability prediction model was established incorporating temperature, mechanical parameters, and effective stress. The results show that from 20 to 80 °C, the elastic modulus of coal column samples decreases by 31.0%, and the Poisson ratio increases by 72.0%. Permeability decreases between 48.37 and 90.12% under different depths. The stress sensitivity coefficient under various temperature conditions decreased exponentially as the effective stress increased, and the temperature sensitivity coefficient under various effective stress conditions decreased with increasing temperature. The permeability was more sensitive to a temperature below 40 °C. In the permeability prediction model, the fracture compressibility coefficient is bifurcated into two coefficients, each controlled by temperature and effective stress. The permeability prediction error of the model was 12.7% under constant effective stress and 17.2% under varying effective stress and temperature conditions. The study could provide guidance for fracturing and coalbed methane production in deep coal reservoirs.

Predicting Peptide Permeability Across Diverse Barriers: A Systematic Investigation.

Peptide-based therapeutics hold immense promise for the treatment of various diseases. However, their effectiveness is often hampered by poor cell membrane permeability, hindering targeted intracellular delivery and oral drug development. This study addressed this challenge by introducing a novel graph neural network (GNN) framework and advanced machine learning algorithms to build predictive models for peptide permeability. Our models offer systematic evaluation across diverse peptides (natural, modified, linear and cyclic) and cell lines [Caco-2, Ralph Russ canine kidney (RRCK) and parallel artificial membrane permeability assay (PAMPA)]. The predictive models for linear and cyclic peptides in Caco-2 and RRCK cell lines were constructed for the first time, with an impressive coefficient of determination (R2) of 0.708, 0.484, 0.553, and 0.528 in the test set, respectively. Notably, the GNN framework behaved better in permeability prediction with larger data sets and improved the accuracy of cyclic peptide prediction in the PAMPA cell line. The R2 increased by about 0.32 compared with the reported models. Furthermore, the important molecular structural features that contribute to good permeability were interpreted; the influence of cell lines, peptide modification, and cyclization on permeability were successfully revealed. To facilitate broader use, we deployed these models on the user-friendly KNIME platform (https://github.com/ifyoungnet/PharmPapp). This work provides a rapid and reliable strategy for systematically assessing peptide permeability, aiding researchers in drug delivery optimization, peptide preselection during drug discovery, and potentially the design of targeted peptide-based materials.

Permeability Prediction from Well Log Data Using Artificial Neural Networks: A Case Study of the Hawaz Formation in D-Field, Libya

Permeability is an important parameter in the characterization of any hydrocarbon reservoir. Yet, despite its vital importance, it is one of the most difficult and controversial petrophysical properties to calculate accurately due to nonlinearity and uncertainty in the dataset. Formation permeability is often measured in the laboratory from cores or evaluated from well test data. Core analysis and well test data, however, are only available from a few wells in a field, while the majority of wells are logged. The aim of this paper is to design an artificial neural network (ANN) model to predict formation permeability using a dataset from the Hawaz Formation in the D-field NC-186 concession, East Murzuq Basin, Libya. In this study, a back-propagation neural network (BP-ANN) model was built using the Python programming language. A total of 950 core horizontal permeability measurements and their corresponding well log data were collected from four wells to build the model. Traditional statistical analysis of the porosity/permeability relationship in cored well data yielded no reliable correlations for predicting permeability in uncored wells with R2 less than 15%. A supervised BP-ANN model was trained successfully and was successfully able to predict the permeability of the formations. Despite the presence of high reservoir heterogeneity, the permeability profile predicted by the ANN model using well log data agrees well with core permeability, which clarifies the applicability of this method.

Knowledge Extraction via Machine Learning Guides a Topology‐Based Permeability Prediction Model

AbstractThe complexity and heterogeneity of pore structure present significant challenges in accurate permeability estimation. Commonly used empirical formulas neglect its microscopic and topological characteristics, thus lacking accuracy and adaptability. While machine learning (ML) and deep learning (DL) models demonstrate promising performance, but encounter challenges of data availability, computational cost, and model interpretability. The present study aims to develop a more robust and accurate permeability prediction model via knowledge extraction from ML model. We first establish an ML model between permeability and the geometry‐topology characteristics of porous media using Extreme Gradient Boosting (XGBoost) algorithm. The data set used to fit ML model is prepared from 458 samples of different types of porous media. Using the SHapley Additive exPlanations (SHAP) value, the influence of each feature on permeability prediction is quantified. It is found that the closeness centrality (topology feature), tortuosity, porosity (macroscopic features) and throat diameter, throat length, pore diameter (pore network features) are vital for permeability prediction. Guided by partial dependence calculation, the unknown function relationship between permeability and the top six important features is established. The novel permeability prediction model incorporating topology feature improves the prediction accuracy and demonstrates strong applicability across diverse data sets. This new model presents an optimal balance between simplicity and performance, rendering it a compelling alternative for permeability prediction in porous media. The research provides a novel referable framework of knowledge extraction via ML to reveal the important features and establish the potential relationship that can be extended and applied in other research fields.

Parameterization of Physiologically Based Biopharmaceutics Models: Workshop Summary Report.

This Article shares the proceedings from the August 29th, 2023 (day 1) workshop "Physiologically Based Biopharmaceutics Modeling (PBBM) Best Practices for Drug Product Quality: Regulatory and Industry Perspectives". The focus of the day was on model parametrization; regulatory authorities from Canada, the USA, Sweden, Belgium, and Norway presented their views on PBBM case studies submitted by industry members of the IQ consortium. The presentations shared key questions raised by regulators during the mock exercise, regarding the PBBM input parameters and their justification. These presentations also shed light on the regulatory assessment processes, content, and format requirements for future PBBM regulatory submissions. In addition, the day 1 breakout presentations and discussions gave the opportunity to share best practices around key questions faced by scientists when parametrizing PBBMs. Key questions included measurement and integration of drug substance solubility for crystalline vs amorphous drugs; impact of excipients on apparent drug solubility/supersaturation; modeling of acid-base reactions at the surface of the dissolving drug; choice of dissolution methods according to the formulation and drug properties with a view to predict the in vivo performance; mechanistic modeling of in vitro product dissolution data to predict in vivo dissolution for various patient populations/species; best practices for characterization of drug precipitation from simple or complex formulations and integration of the data in PBBM; incorporation of drug permeability into PBBM for various routes of uptake and prediction of permeability along the GI tract.

Permeability prediction of soft clay based on digital models reconstructed by an improved Markov chain Monte Carlo method

Permeability prediction of soft clay based on digital models reconstructed by an improved Markov chain Monte Carlo method

A Fractal Model of Fracture Permeability Considering Morphology and Spatial Distribution

Summary In fractured reservoirs, the fracture system is considered to be the main channel for fluid flow. To better investigate the impacts of fracture morphology (tortuosity and roughness) and spatial distribution on the flow capacity, a fractal model of fracture permeability was developed. Based on micro-computed tomography (CT) images, the 3D structure of the fracture was reconstructed, and the fractal characteristics were systematically analyzed. Finally, the control of permeability by fracture morphology and spatial distribution in different fractured reservoirs was identified. The results demonstrate that the complexity of the fracture distribution in 2D slices can represent the nature of the fracture distribution in 3D space. The permeability fractal prediction model was developed based on porosity (φ), spatial distribution fractal dimension (Df), tortuosity fractal dimension (DT), and opening fractal dimension of the maximum width fracture (Db). The permeability prediction results of the fractal model for Samples L-01 (limestone), BD-01 (coal), BD-02 (coal), S-01 (sandstone), M-01 (mudstone), and C-01 (coal) are 0.011 md, 0.239 md, 0.134 md, 0.119 md, 1.429 md, and 27.444 md, respectively. For different types of rocks, the results predicted by the model show good agreement with numerical simulations (with an average relative error of 2.51%). The factors controlling the permeability of fractured reservoirs were analyzed through the application of the mathematical model. The permeability is positively exponentially correlated with the fractal dimension of spatial distribution and negatively exponentially correlated with the fractal dimension of morphology. When Df < 2.25, the fracture spatial structure is simple, and the morphology and spatial distribution jointly control the seepage capacity of fractured reservoirs. When Df > 2.25, the fracture spatial structure is complex, and the impact of morphology on seepage capacity can be disregarded. This work can effectively lay the foundation for the study of fluid permeability in fractured reservoirs by investigating the effects of fracture morphology (tortuosity and roughness) and spatial distribution on flow capacity.

Influence of semi-crystalline microstructure on gas permeability of Poly(Ether-Ketone-Ketone)

In a context of ecological transition, aeronautics is moving towards the development of hydrogen-powered aircraft for which lightweight, long-lasting and tough storage tanks must be developed. Among other properties, the tightness of tanks made of polymer and composite materials is of prime importance. This study thus aims at understanding the permeation phenomena through Poly-Ether-Ketone-Ketone (PEKK), which is one of the thermoplastic polymers considered for tank manufacture. In order to better understand the phenomena governing gas permeability, tests were carried out on different PEKK grades of different T/I ratios and degree of crystallinity. The results show that the crystallinity ratio is not the only factor that governs gas diffusion and solubility, but that permeability also depends on PEKK semi-crystalline microstructure. Even if considering crystallites organization through a tortuosity factor improves permeability predictions, permeability can be better understood using a 3 phases description of PEKK microstructure considering distinct contributions of the rigid and mobile amorphous fractions (RAF and MAF). It appears that the MAF permeation does not depend on PEKK T/I ratio, but that gas diffusion and solubility increase for RAF when increasing T/I ratio, thus counterbalancing the blocking effect of crystallites.

Machine Learning Assisted Prediction of Porosity and Related Properties Using Digital Rock Images.

Accurately estimating reservoir rock properties is paramount for modeling the storage and flow of fluids (hydrocarbon, carbon dioxide, and groundwater) in porous media. However, existing laboratory techniques to measure rock properties are usually time-consuming, expensive, and computationally intensive. This work proposes an efficient workflow that uses the machine learning algorithm, based on the convolutional neural network (CNN) framework, to predict rock properties from microcomputed tomography (micro-CT) X-ray images. The workflow involves data preprocessing, label extraction, training, and prediction using the segmented images of the rock to predict porosity, throat area, and pore surface area, which are essential for pore-scale modeling. The model was trained and validated on the Bentheimer sandstone, which was then used to predict properties of other sandstones (Castlegate and Leopard) with different pore structures and flow properties. The model yielded a good prediction for the throat and pore surface area but a significant error for porosity. Subsequently, a new complex model was trained and validated using diverse images from Bentheimer and an additional rock Castlegate, which was then used to predict the properties of Leopard sandstone. The new model improved the prediction of each property, resulting in mean absolute percentage error (MAPE) values of 2.19%, 3.04%, and 6.08% for porosity, pore surface area, and throat area with the binary images, respectively. In addition, we present a novel data-driven method using a simple regression model to predict the absolute permeability of a digital rock sample using the pore network parameters as predictors. The extreme gradient boost (XGBoost), which performed the best among several machine algorithms, was trained and validated using digital rock images from Bentheimer and Castlegate sandstone. The generated model was then used to predict the absolute permeability of the Leopard sandstone with an R 2 of 0.813, which was a significant improvement over the model generated solely by using either the Bentheimer or the Castlegate sandstone images. Furthermore, our analysis showed that the tortuosity had the most significant effect on the absolute permeability prediction of the rock sample. This study showed that we can reliably predict the morphological properties of porous media using computationally efficient models generated from digital rock images, which can be used to build a regression model to predict the crucial petrophysical properties needed to model the flow of fluids in porous media.