Permeability Prediction Research Articles

Clinical side-effects based drug repositioning for anti-epileptic activity

Several generations of anti-epileptic drugs (AEDs) are available but have several associated side effects apart from a limited success rate. Drug repositioning strategies have gained importance in the last two decades owing to lower failure rates and economic burden. Drugs with similar side effect profiles may share a common mechanism of action and thus can be linked to other disease treatments. The present study was carried out to identify the newly approved drug candidate(s) as AEDs using clinical side-effects drug repositioning strategy. The clinical side effect similarity of drugs available in the SIDER v4.1 database was estimated against common side effects of 5 major marketed AEDs, using the ‘dplyr’ package library in the R. Further drugs were filtered based on Blood Brain Barrier permeability prediction and FDA-approval status. Molecular docking studies were performed for selected 26 hits (drugs) against previously identified epilepsy target receptors: Voltage-gated sodium channel α2 (Nav1.2), GABA receptor α1-β1 (GABAr α1-β1), and Voltage-gated calcium channel α-1 G (Cav3.1). Only 2 drugs (Ziprasidone and Paroxetine) showed better binding affinities against studied epilepsy receptors Nav1.2, GABAr α1-β1, and Cav3.1, than their corresponding standard AEDs, i.e. Carbamazepine, Clonazepam, and Pregabalin, respectively. Ziprasidone reportedly showed seizure-like symptoms in ∼3% of patients and was hence omitted from further study. The MDS study of docked complexes of Paroxetine with selected epilepsy target receptors showed stable RMSD values and better interaction energies. The study reveals Paroxetine as a potential candidate to be repurposed for 1st line epileptic seizure medication. Communicated by Ramaswamy H. Sarma

Benchmark exercise on image-based permeability determination of engineering textiles: Microscale predictions

Permeability measurements of engineering textiles exhibit large variability as no standardization method currently exists; numerical permeability prediction is thus an attractive alternative. It has all advantages of virtual material characterization, including the possibility to study the impact of material variability and small-scale parameters. This paper presents the results of an international virtual permeability benchmark, which is a first contribution to permeability predictions for fibrous reinforcements based on real images. In this first stage, the focus was on the microscale computation of fiber bundle permeability. In total 16 participants provided 50 results using different numerical methods, boundary conditions, permeability identification techniques. The scatter of the predicted axial permeability after the elimination of inconsistent results was found to be smaller (14%) than that of the transverse permeability (∼24%). Dominant effects on the permeability were found to be the boundary conditions in tangential direction, number of sub-domains used in the renormalization approach, and the permeability identification technique.

Representation of Fully Three‐Dimensional Interfacial Curvature in Pore‐Network Models

AbstractQuasi two‐dimensional approximations of interfacial curvature, present in current network models of multiphase flow in porous media, are extended to three dimensions. The new expressions for threshold capillary pressure are validated and calibrated using high‐resolution direct numerical simulations on synthetic geometries. The effects of pore‐space expansion and sagittal interface curvature on displacement are quantified, and are shown to be a key step in improving the physical accuracy of network models. Finally, the calibrated network model is used to obtain predictions for relative permeability and capillary pressure in a water‐wet Bentheimer sandstone. The predictions are compared to experimental measurements, revealing that the inclusion of three‐dimensional interfacial curvature leads to more accurate predictions.

Estimating permeability in a limestone geothermal reservoir from NMR laboratory experiments

A wireline NMR (Nuclear Magnetic Resonance) logging tool has recently been deployed in the Dogger geothermal aquifer of the Paris Basin to provide a continuous permeability estimation throughout the reservoir. The complex pore structure of heterogeneous carbonate systems means that careful consideration must be given to standard permeability prediction. A laboratory study was performed on cores from a geothermal well at Bobigny, north of Paris. Petrographic and petrophysical analyses of thin sections, water permeability and laboratory NMR relaxation time T2 were conducted on 72 samples. A classification was established using four main facies and the impact of microporosity and micritization on flow properties was investigated. The range of permeability is wide [0.05–1000 mD] and the evaluation addresses different relationships between permeability and a combination of porosity and T2 distributions. Since the latter distributions can provide an estimation of the pore size distributions and in particular the fraction of microporosity within the total porosity, several possibilities arise for better constraining the permeability relationship. For one facies, permeability is nearly independent of porosity over a porosity range of [0.12–0.22], illustrating the well-known difficulty of predicting permeability in carbonate lithologies. The best permeability prediction is obtained when considering only macroporosity instead of total porosity in a classical power law.

Fast prediction of antibiotic permeability through membrane channels using Brownian dynamics

The efficient permeation across the Gram-negative bacterial membrane is an important step in the overall process of antibacterial action of a molecule and the one that has posed a significant hurdle on the way toward approved antibiotics. Predicting the permeability for a large library of molecules and assessing the effect of different molecular transformations on permeation rates of a given molecule is critical to the development of effective antibiotics. We present a computational approach for obtaining estimates of molecular permeability through a porin channel in a matter of hours using a Brownian dynamics approach. The fast sampling using a temperature acceleration scheme enables the approximate estimation of permeability using the inhomogeneous solubility diffusion model. Although the method is a significant approximation to similar all-atom approaches tested previously, we show that the present approach predicts permeabilities that correlate fairly well with the respective experimental permeation rates from liposome swelling experiments and accumulation rates from antibiotic accumulation assays, and is significantly, i.e., about 14 times, faster compared with a previously reported approach. The possible applications of the scheme in high-throughput screening for fast permeators are discussed.

A new method for dynamic predicting porosity and permeability of low permeability and tight reservoir under effective overburden pressure based on BP neural network

Under the influence of effective overburden pressure, dynamic prediction of porosity and permeability is very important for the formulation and dynamic adjustment of low permeability and tight reservoir development plan. However, the high heterogeneity and complex influencing factors of reservoirs bring great difficulties to the prediction. Firstly, the variation law of porosity and permeability with the increase of effective overburden pressure is determined by testing. On this foundation, data preprocessing is carried out, a sample database is established, and the main controlling factors of geology, fluid and lithology are identified by grey correlation. Finally, based on BP neural network, porosity and permeability prediction training is carried out, and prediction model is established. Considering the prediction error and whether the model is over or under fitting, the results show that the prediction error of the model established by 10 hidden layer neurons and trainbr training function is the best, and the prediction error of porosity and permeability are 3.22% and 8.67% respectively. The model is applied to the field data, and the prediction error of porosity and permeability are 4.18% and 15.85% respectively, which are in line with the oil production law.

A percolation-threshold model for evaluating permeability in Portland pastes with high water-cement ratio

In a perspective of improved sustainability, the definition of policies for the management of the heritage of concrete buildings and infrastructures, aiming at the suitable mitigation of the harm to environment and at the reduction of societal costs, requires reliable methodologies to estimate the service-life and maintenance periodicity time for a given class of concrete structures. Concrete watertightness is a basic property for assessing durability of the built heritage to weathering exposure and is mainly dependent on permeability of the cement paste embedding the aggregates which typically have much lower permeability in an ordinary concrete. Fundamental research of the past century has shown that a reliable experimental determination of permeability in Portland concretes and pastes can be achieved in terms of intrinsic permeability by uniaxial water-permeation tests under open-flow conditions.Powers and Brownyard have early assessed in 1947 the ability of the Kozeny-Carman relation in providing predictions of intrinsic permeability of Portland cement pastes, although achieving predictivity only below a water-cement ratio (w/c) of about 0.5. Above such threshold, as they observe, their model is no longer predictive since permeation through the capillary space formed due to excess water becomes predominant over permeation through the gel-space.In the present study an enhancement of Powers and Brownyard's canonical model is proposed by addressing capillary percolation and the possibility of simultaneous flow at the gel and capillary scales. The onset of capillary percolation for w/c slightly above 0.5 is naturally predicted. Agreement of the proposed model is assessed against experimental results and discussed in relation to optimal management of existing RC constructions with a view towards sustainability.

Modelling of the impact of stress concentration on permeability in porous medium based on machine learning method

The behavior of stress-dependent permeability has been an important research topic for oil/gas production. The majority of permeability models for porous media have been proposed based on porelasticity theory. The matrix is assumed to be thoroughly separated by pores in the models and the pore compressibility is used to represent the stress-dependent behavior of permeability. However, matrix could not be separated by pores thoroughly and the impact of stress concentration around pores on pore deformation and permeability should be considered. In this study, the impact of stress concentration on permeability was illustrated by numerical simulation. In addition, the mechanism of stress-dependent behavior of permeability was analyzed. Since it is difficult to establish theoretical permeability models involving stress concentration effect caused by the complex pore structure, machine learning was applied in this paper. One of the four capable machine learning methods was selected and the corresponding machine learning model was validated through both numerical and experimental data. Moreover, the different performance of permeability prediction between the conventional model and the proposed one was discussed. The results indicate that the stress-dependent behavior of permeability results from stress concentration rather than the pore bulk modulus. Therefore, the stress-dependent permeability model with the impact of stress concentration is more accurate, compared with the numerical results and experimental data. In addition, the stress concentration increases the pore deformation and induces strong stress-dependent behavior of permeability, which is sensitive to pore shapes and related to the pore shape complexity. Specifically, the impacts of pores with different shapes on permeability are similar if the complexity index of pore shape is under 0.6 or over 0.9 and distinct for the value in between. Furthermore, the alteration in the magnitude and orientation of the stress affects stress dependency of permeability, which increases with the pore shape complexity. If the complexity index of pore shape exceeds 0.96, the alteration of permeability induced by change in stress orientation can be over 64%.

Permeability prediction using logging data in a heterogeneous carbonate reservoir: A new self-adaptive predictor

Accurately predicting permeability is critical for oil deposit exploitation and high-quality reservoir identification. However, the substantial heterogeneity in carbonate reservoirs has significantly challenged the accurate permeability prediction. In this study, 325 core samples with logging data from the Sinian carbonate reservoirs of the second member of the Dengying Formation were collected to establish a reliable predictor for permeability prediction in the Gaoshiti-Moxi block, Sichuan Basin. Three typical machine learning algorithms and three population-based optimization algorithms were applied to the core samples and logging data to evaluate the applicability and prediction performance of different methods. Two types of objective functions are well designed to obtain a more satisfactory result for permeability prediction. By comparison, the multi-objective mayfly algorithm (MMA) combined with gradient boosting decision tree (LGB) among all the algorithms had a more vital ability to predict permeability. Therefore, a new self-adaptive predictor was developed by combining the MMA-LGB algorithm with the low-pass filters that were applied as a noise filtering method from the original geophysical well logs with Fourier transform (FT). Filtered logs were tested by using Fractal statistics. Through comparisons, the self-adaptive predictors significantly improved the prediction accuracy with the lowest MSE of 0.239 and the highest R2 of 0.831, well demonstrating that combining the machine learning algorithm with low-pass filters could mitigate the adverse effects of heterogeneity on permeability prediction. The excellent prediction performance of the proposed self-adaptive predictor lays a sound theoretical foundation for logging interpretation and identifying high-quality carbonate reservoirs in the target field.

An Apparent Permeability Model in Organic Shales: Coupling Multiple Flow Mechanisms and Factors.

It is of great significance to study shale apparent permeability under the action of multiple flow mechanisms and factors because shale reservoirs possess complex pore structures and flow mechanisms. In this study, the confinement effect was considered, with the thermodynamic properties of gas being modified, and the law relating to the conservation of energy adopted to characterize bulk gas transport velocity. On this basis, the dynamic change of pore size was assessed, from which shale apparent permeability model was derived. The new model was verified by three steps: experimental and molecular simulation results of rarefied gas transport, shale laboratory data, and comparison with different models. The results revealed that, under the conditions of low pressure and small pore size, the microscale effects became obvious, which significantly improved gas permeability. Through comparisons, the effects of surface diffusion and matrix shrinkage, including the real gas effect, were obvious in the smaller pore sizes; nevertheless, the stress sensitivity effect was stronger in larger pore sizes. In addition, shale apparent permeability and pore size decreased with an increase in permeability material constant and increased with increasing porosity material constant, including internal swelling coefficient. The permeability material constant had the greatest effect on gas transport behavior in nanopores, followed by the porosity material constant; however, the internal swelling coefficient had the least effect. The results of this paper will be important for the prediction and numerical simulation of apparent permeability relating to shale reservoirs.

Permeability modeling of Upper Cretaceous Bahariya Formation rocks, Abu Sennan field, Western Desert, Egypt

The present work aims to describe in detail the petrophysical properties of the Bahariya Formation (Abu Sennan field) through processing the core sample data and finding out the possible relations between permeability and other reservoir parameters such as porosity, specific internal surface, formation factor, and nuclear magnetic resonance to obtain reliable permeability prediction model of the Upper Cretaceous formation. The core samples have a length range of 2–4 cm with a diameter of 2.54 cm. Many petrophysical reservoir parameters were measured, such as permeability, porosity, density, formation factor, and special measurements such as spectral induced polarization, nuclear magnetic resonance, and specific internal surface. The samples cover a wide range of porosity (11.82–23.68 %) and permeability (0.06–133.65 mD). The porosity-permeability relationship indicates the increase of the permeability with porosity increase for the studied samples. The reservoir quality index ranges from 0.02 to 0.78 μm. The flow zone indicator ranges from 0.09 to 3.39 μm where pore throat radius (R35) ranges from 0.10 to 7.40 μm. The reservoir quality index (RQI) and flow zone indicator (FZI) cross-plot show a better understanding of the formation evaluation. The hydraulic flow units are (0.09 μm < FZI ≤ 3.39 μm) and RQI < (1 μm). NMR longitudinal relaxation time T1 range is 1.21–175 ms, NMR transversal relaxation time T2(100) range is 0.70–91.77 ms, and T2(600) range is 0.74–75.86 ms. Models that incorporate porosity and NMR relaxation times will be more accurate and suitable for permeability prediction. Using multiple regression to determine the exponents of the power-law equation and replacing the porosity with the formation factor can improve the permeability prediction for the samples under investigation. The utilizing of NMR in predicting the permeability through a robust model considers an additional value for the Upper Cretaceous rocks in Egypt.

Improving multiwell petrophysical interpretation from well logs via machine learning and statistical models

Well-log interpretation estimates in situ rock properties along well trajectory, such as porosity, water saturation, and permeability, to support reserve-volume estimation, production forecasts, and decision making in reservoir development. However, due to measurement errors, variability of well logs caused by multiple measurement vendors, different borehole tools, and nonuniform drilling/borehole conditions, estimations of rock properties with original well logs without proper preprocessing may not be accurate, especially in the context of multiwell estimation. Well-log normalization techniques such as two-point scaling and mean-variance normalization are commonly used to improve the robustness of multiwell rock-property estimation. However, these techniques do not consider the correlation between well logs and require subjective knowledge for their effective implementation. To reduce uncertainties and processing time associated with multiwell rock-property estimation from well logs, we develop discriminative adversarial (DA) and linear constraint models for well-log normalization and rock-property estimation. The DA neural network model developed for well-log normalization and interpretation can perform linear and nonlinear well-log normalization while considering the joint distribution of each well log and rock properties. However, the linear constraint model uses an ensemble of predictions from linear models to constrain well-log normalization and rock-property estimation. We also develop a divergence-based type well identification method to select type (training) wells for a test well based on the statistical similarity of associated well-log distributions instead of the interwell distance. We apply the DA model to perform well-log normalization and prediction of permeability for the Seminole San Andres Unit carbonate reservoir. Compared with the permeability predicted with the classical machine learning model without well-log normalization and models with two-point scaling normalization, the DA model yields the most accurate permeability prediction by decreasing the mean-squared error of permeability prediction by 20%–50%.

Prediction of permeability from well logs using a new hybrid machine learning algorithm

Permeability is a measure of fluid transmissibility in the rock and is a crucial concept in the evaluation of formations and the production of hydrocarbon from the reservoirs. Various techniques such as intelligent methods have been introduced to estimate the permeability from other petrophysical features. The efficiency and convergence issues associated with artificial neural networks have motivated researchers to use hybrid techniques for the optimization of the networks, where the artificial neural network is combined with heuristic algorithms. This research combines Social Ski-Driver (SSD) algorithm with the multilayer perception (MLP) neural network and presents a new hybrid algorithm to predict the value of rock permeability. The performance of this novel technique is compared with two previously used hybrid methods (Genetic Algorithm-MLP and Particle Swarm Optimization-MLP) to examine the effectiveness of these hybrid methods in predicting the permeability of the rock. The results indicate that the hybrid models can predict rock permeability with excellent accuracy. MLP-SSD method yields the highest coefficient of determination (0.9928) among all other methods in predicting the permeability values of the test data set, followed by MLP-PSO and MLP-GA, respectively. However, the MLP-GA converged faster than the other two methods and is computationally less expensive.

Iterative geostatistical seismic inversion with rock-physics constraints for permeability prediction

Accurate prediction of the spatial distribution of subsurface permeability is a fundamental task in reservoir characterization and monitoring studies for hydrocarbon production and CO2geologic storage. Predicting permeability over large areas is challenging, due to their high variability and spatial anisotropy. Common approaches for modeling permeability generally involve deterministic calculations from porosity using precalibrated rock-physics models (RPMs) or geostatistical cosimulation methods that reproduce observed experimental porosity-permeability relationships. Instead, we have predicted permeability from seismic data using an iterative geostatistical seismic inversion method that combines the advantages of rock-physics and geostatistical modeling methods. First, we simulate facies through 1D vertical Markov chain simulations. Then, permeability, porosity, and acoustic impedance are sequentially generated and conditioned to the previously simulated facies model. An RPM is used to evaluate the misfit between the permeability predictions obtained from geostatistical cosimulation at the well locations and well-log values computed from the acoustic impedance. The residuals of the misfit function are used as conditioning constraints in the stochastic update of the models in the subsequent iteration. The outcome of our methodology is a set of multiple geostatistical realizations of facies, permeability, porosity, and acoustic impedance conditioned to seismic data and constrained by an RPM. We first illustrate the method on a synthetic 1D example and compare it to a traditional geostatistical inversion approach. We then apply our inversion to a 3D real data set to assess the methodology performance with scarce conditioning data and in the presence of noise.

Prediction of gas–water relative permeability in tight rock from movable fluid distribution with nuclear magnetic resonance

The measurement of the relative permeability in tight rock is challenging due to its ultralow permeability and the time-consuming nature of the experiments. Studying the movable and unmovable fluid distribution and establishing a reliable relative permeability prediction model is an urgent problem to be solved. This paper used nuclear magnetic resonance (NMR) to investigate movable and unmovable water distribution in tight sandstone under different centrifugal forces. A new method for predicting gas–water relative permeability in tight rock is established based on movable fluid distribution using the capillary bundle model. The results show that the distribution of movable and unmovable fluids is strongly influenced by the tight rock's pore size distribution and structure. The unmovable fluid saturation increases as the tight rock's permeability and median radius decrease. The nonlinear correlation between the NMR relaxation time and the pore throat size obtained from high-pressure mercury intrusion can be used to derive the pore size of the fluid distribution in tight rocks. The ratio of the movable fluid thickness to pore throat size increases near linearly with the logarithm of the pore throat size. The proposed mathematical model for the prediction of gas-water relative permeability based on movable fluid distribution is verified by comparing with the normalized relative permeability curve measured from experiments. This new model offers an alternative method of estimating the gas–water relative permeability when measurement is unavailable due to the ultralow permeability of the core samples.

A surrogate model for the prediction of permeabilities and flow through porous media

AbstractIn this contribution we present an approach to generate a data driven surrogate model for the prediction of permeabilities and flow through two dimensional random micro‐heterogeneous materials. The laminar flow is well described by Darcy's law. In order to achieve an efficient computational tool for the generation of the database (up to 103 realizations), needed for the training of the neural networks, we apply a stochastic model based on the Brownian motion. The stationary state of the resulting stochastic model solves the Darcy equation and can be iteratively solved by a Monte Carlo approach applied to a particle simulation. Improved numerical efficiency can be yield by usage of the related transition matrix.

Permeability prediction of considering organic matter distribution based on deep learning

At present, researchers predict permeability through core experiments that require specific experimental conditions and methods, which are difficult and time-consuming. Conventional simulation methods for predicting permeability require considerable computational resources. Therefore, deep learning can be used as a pore-scale simulation prediction method. In this study, we established a workflow for directly predicting permeability from images. Considering that the mineral properties of the nanopore wall of shale oil have a large influence on the flow, a core dataset with organic distribution was constructed with random circles, and the slip influence of organic pores was considered. From our dataset, we found that the average permeability with organic distribution was 32.3% higher than that without organic distribution. Therefore, to simulate the microscopic flow and predict the permeability of shale oil, considering the differences in the pore flow mechanisms of different minerals is necessary. We designed a convolutional network for the dataset, adopted the structure of SE-ResNet, added the squeeze-and-excitation (SE) module to the double-layer residual module of ResNet18, and combined the characteristics of the SE block with the attention mechanism and ResNet to effectively obtain the information between channels and avoid the problem of gradient disappearance or explosion. Using SE-ResNet for directly predicting the apparent permeability from images, the accuracy of the test set reached 88.5%. The model had strong generalization ability, and the SE-ResNet could map the image of the core to the apparent permeability, which was approximately 100 times faster than the direct flow simulation.

Multimodal Evolution of Coal Permeability due to Interior Deformations within a Triple Porosity System

Summary Measurements of coal permeability are normally analyzed without considering the interaction among microfracture and pore size distributions within the sample (control volume). Without this inclusion, nearly all permeability predictions are monomodal as reported in the literature. However, experimental observations are multimodal for most cases. In this study, we hypothesize that these discrepancies or mismatches between measurements and analytical predictions are due to the exclusion of the interaction among microfracture and pore size distributions within the sample (control volume). We report a first experimental study of triple-porosity interactions on a prismatic sample containing millimeter-scale fractures (Ⅰ) and micron- (Ⅱ) through nanometer-scale (Ⅲ) pores. Migration speeds of sorbing (e.g., CH4) gases are conditioned by the strain field, which is in turn conditioned by effective stresses and swelling strains. These distinct pore populations exhibit characteristic times for a time-staged equilibration of the strain field as multiple plateaus. This time-staged evolution of strain in turn delimits the evolving fracture permeability into a series of stages. The relatively high permeability of fractures and micropores defines a brief intermediate equilibrium permeability, after which the nanopore system controls the final permeability evolution. Our results indicate that the multimodal evolution of coal fracture permeability can be explained by the time-staged evolution of strain due to multiporosity interactions and could be defined by a time-staged equilibration of the strain fields as multiple plateaus.

Efficient large-scale mechanism-based computation of skin permeability

A drug’s skin permeability is a key quantity within dermatological risk assessment as it quantifies the maximal rate of dermal absorption, under steady state conditions, per unit concentration difference across the skin of a topically applied drug. Given descriptors of the permeant, the vehicle, and skin conditions, this paper adopts a systematic approach to efficiently calculate estimates of a topically applied chemical’s skin permeability based on mechanistic knowledge of permeability across the elements of a fine spatial discretization of the dermal strata. The permeability estimates are obtained by solving a system of linear equations constructed using an electrical resistor network analogy. Being mechanism based, these estimates can account for skin conditions, heterogenous dermal penetration pathways, and the chemical-dependence and spatial dependence of partitioning and diffusivity.The contribution of this work can be viewed as a mechanistic, numerical, extension of the Potts-Guy relation that augments an open-source dermal PBPK model. Moreover, rather than requiring model simulations of steady state conditions, the approach centers on a direct calculation of permeability based on the mechanistic descriptors of the permeation scenario. The calculation method may therefore be directly integrated into parameter identification, optimization, and sensitivity analysis algorithms. We demonstrate the validity of the method by comparing its permeability predictions with previously reported in silico estimates and in vitro measurements. We additionally illustrate the utility of the method towards the analysis of dermal PBPK models using a minimal example that relates overall skin permeability to the interaction between multiple permeation pathways.

Prediction of air permeability and effective thermal conductivity of multifilament polyester yarn by finite element analysis

The effective thermal conductivity and air permeability of a multifilament polyester yarn used in sports T-shirts was investigated by computer modeling using finite element analysis. It has been shown that the number of fibers, the porosity of the yarn and the proportion of fibers in the volume fraction of the yarn have a direct effect on the effective thermal conductivity and air permeability of the multifilament yarn. It was found that with the increase in the number of fibers, the porosity of the yarn decreases linearly, while the volume fraction of the fibers increases, and thus the effective thermal conductivity increases. In addition, air permeability decreases exponentially.