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In order to investigate the influence mechanism of temperature on the permeability of loose coal body in the process of spontaneous combustion of residual coal in the gob, the mathematical model of the permeability of coal body under different temperature conditions was established by considering the effects of temperature-induced adsorption deformation, thermal expansion deformation, thermal fission deformation and sliding effect, etc., and the parameters of the single influencing factors of the mathematical model of the permeability were controlled by the method of controlling variables to investigate the influence mechanism of the temperature change on the permeability of the loose coal body. Based on the principle of physical similarity, an experimental platform for loose coal seepage was designed and constructed, and anthracite coal from Chengzhuang Mine in Shanxi Province was selected as the experimental object, and air was used as the seepage gas to simulate the underground environment, so that gas transport experiments were carried out in the loose coal body under different temperature conditions (40-100 ℃), different injection pressures (0.3-0.5 MPa), and different permeability pressures (0.04-0.08 kPa). The mechanism of the influence of temperature change on the permeability of the loose coal body was investigated by mutual verification of the experimental and modeling results. The results show that: the permeability of loose coal body increases exponentially with the increase of gas pressure gradient; under the condition of the same pressure gradient, the permeability decreases gradually with the increase of temperature; the influence of the temperature on the permeability is small when it is from 40 ℃ to 60 ℃, and the change of the permeability is not obvious, and the permeability starts to decrease after 60 ℃, and the decrease of the permeability is more significant when it is from 90 ℃ to 100 ℃; under the influence of the slippage effect, the increase of the injection pressure inhibits the permeability of the coal body. Under different osmotic pressure conditions (0.04-0.08 kPa), the model values of the permeability of the loose coal body and the experimental values of the seepage flow are in high agreement. With the increase of temperature, adsorption deformation has an inhibitory effect on the permeability of the loose coal body, thermal cracking deformation and the slipping effect have a promotional effect on the permeability of the loose coal body, and the effect of the deformation of the thermal expansion on the permeability of the loose coal body is weak. Temperature affects the permeability of loose coal body through the combined effect of adsorption deformation, thermal cracking deformation and sliding effect.

Permeability is the most important petrophysical characteristic for determining how fluids pass through reservoir rocks. This study aims to develop and assess intelligent computer-based models for predicting permeability. The research focuses on three novel models—Decision Tree, Bagging Tree, and Extra Trees—while also investigating previously applied techniques such as random forest, support vector regressor (SVR), and multiple variable regression (MVR). The primary dataset consists of 197 data points from a heterogeneous petroleum reservoir in the Jeanne d'Arc Basin, including laboratory-derived permeability (K), oil saturation (SO), water saturation (SW), grain density (ρgr), porosity (φ), and depth. The most effective machine learning models are identified by a thorough analysis that makes use of a variety of statistical metrics, such as the coefficient of the determinant (R2), mean squared error (MSE), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), maximum error (maxE), and minimum error (minE). Additionally, core features are ranked based on their importance in permeability modeling. This study deviates from conventional approaches by proposing an efficient means of forecasting permeability, reducing reliance on labor-intensive and time-consuming laboratory work. The findings reveal that MVR is unsuitable for permeability prediction, with all developed models outperforming it. Extra Trees emerges as the most accurate model, with an R2 of 0.976, while random forest and bagging tree exhibit slightly lower R2 values of 0.961 and 0.964, respectively. The ranking of these algorithms based on performance criteria is as follows: extra trees, bagging tree, random forest, SVR, decision tree, and MVR. The study also presents a detailed analysis of the impact of input parameters, highlighting porosity (φ) and water saturation (SW) as the most influential, while grain density (ρgr), oil saturation (SO), and depth are considered less important. This study contributes to the petroleum industry's knowledge by showcasing the inadequacy of MVR and highlighting the superior performance of machine learning models, particularly Extra Trees. The proposed models employed in this study can help engineers and researchers determine reservoir permeability quickly and accurately by using a few core attributes, reducing the dependency on resource-intensive and time-consuming laboratory work.

For efficient permeability modeling and prediction in oil and gas reservoirs, the recorded well-log data and the discrete lithofacies are calibrated with the limited core data available and then used for the prediction in unobserved and unsampled well-bore sections. Like other machine learning models, the gradient boosting model (GBM) requires hyperparameter tuning to optimize their prediction performance. Two GBM configurations are evaluated that involve grid search (GS-GBM-HPO) and random search (RS-GBM-HPO) to optimize (tune) the model's hyperparameters settings and compare their prediction performances with default settings (GBM-DHP). These three GBM configurations are first trained and tested through cross-validation, in classification mode, to predict lithofacies, based on six well-log distributions, from a partially cored well drilled into an extensive carbonate oil and gas reservoir (Mishrif Formation, Iraq). The predicted discrete lithofacies distribution is then incorporated with the six well-log distributions to tune another set of GS-GBM-HPO, RS-GBM-HPO, and GBM-DHP models is trained and tested, in regression mode, to predict Mishrif reservoir permeability throughout the wellbore. Total-correct percentage (TCP) values assess the lithofacies classification accuracy of the GBM models. The highest TCP of 96.6% was achieved by the RS-GBM-HPO model applied to the lithofacies testing subset. Mean absolute error (MAE) and root mean square error (RMSE) assess permeability prediction accuracy. The lowest permeability prediction errors were achieved by the RS-GBM-HPO model, configured in regression mode, and applied to the testing subset (MAE = 6.85 mD; RMSE = 15.34 mD). The trained RS-GBM-HPO model was used to predict Mishrif permeability in a separate non-cored well (Well X) and these predictions closely matched permeability values derived from nuclear magnetic resonance (NMR) logging of Well X. The presented RS-GBM-HPO model can provide dependable permeability predictions in non-cored sections of the well used for GMB model training. Moreover, the trained GMB models can be generalized for application to other field wells lacking core data. [ABSTRACT FROM AUTHOR]

The injection of CO2-rich fluids in carbonate rocks results in an evolution of the pore space, with consequent changes in the hydraulic properties of the reservoir; how these properties evolve, particularly for parameters relevant to multiphase flow e.g. Pc(s), remains a topic of active research despite several decades of study. We have carried out an in situ synchrotron X-ray microtomography experiment to monitor pore structure evolution during dissolution of an Indiana Limestone core; the experiment involved flowing CO2-saturated water through the core for 36 h and resulted in 10 volumes corresponding to different temporal stages of the dissolution process. The injection parameters corresponded to the flow velocities expected near the well-bore region of a shallow aqueous CO2 injection; fast flow rates with high reactant availability. Analysis of the tomographic data shows flow-enhanced dissolution i.e. channeling, and provides a time-resolved map of pore space alteration. Using the resulting 4D pore space volume, we modeled the evolution of capillary-pressure curves; this exercise demonstrates how pore structure evolution could impact the invasion and remobilization of non-wetting fluids, dramatically decreasing the entry pressure and the PC in some parts of the sample. The modeling of permeability, using a Stokes solver approach, quantified the relationship of porosity vs. permeability; we found that a modest increase in porosity, especially when the channeling system is more developed, greatly affects permeability. These results demonstrate how movement of CO2 saturated brine near injected plumes might alter drainage dynamics near the plume boundary, thus leading to mobilization across subtle capillary barriers.

The injection of CO2-rich fluids in carbonate rocks results in an evolution of the pore space, with consequent changes in the hydraulic properties of the reservoir; how these properties evolve, particularly for parameters relevant to multiphase flow e.g. Pc(s), remains a topic of active research despite several decades of study. We have carried out an in situ synchrotron X-ray microtomography experiment to monitor pore structure evolution during dissolution of an Indiana Limestone core; the experiment involved flowing CO2-saturated water through the core for 36 h and resulted in 10 volumes corresponding to different temporal stages of the dissolution process. The injection parameters corresponded to the flow velocities expected near the well-bore region of a shallow aqueous CO2 injection; fast flow rates with high reactant availability. Analysis of the tomographic data shows flow-enhanced dissolution i.e. channeling, and provides a time-resolved map of pore space alteration. Using the resulting 4D pore space volume, we modeled the evolution of capillary-pressure curves; this exercise demonstrates how pore structure evolution could impact the invasion and remobilization of non-wetting fluids, dramatically decreasing the entry pressure and the PC in some parts of the sample. The modeling of permeability, using a Stokes solver approach, quantified the relationship of porosity vs. permeability; we found that a modest increase in porosity, especially when the channeling system is more developed, greatly affects permeability. These results demonstrate how movement of CO2 saturated brine near injected plumes might alter drainage dynamics near the plume boundary, thus leading to mobilization across subtle capillary barriers.

This work highlights the application of Artificial Neural Networks optimized by Cuckoo optimization algorithm for predictions of NMR log parameters including porosity and permeability by using field log data. The NMR logging data have some highly vital privileges over conventional ones. The measured porosity is independent from bearer pore fluid and is effective porosity not total. Moreover, the permeability achieved by exact measurement and calculation considering clay content and pore fluid type. Therefore availability of the NMR data brings a great leverage in understanding the reservoir properties and also perfectly modelling the reservoir. Therefore, achieving NMR logging data by a model fed by a far inferior and less costly conventional logging data is a great privilege. The input parameters of model were neutron porosity (NPHI), sonic transit time (DT), bulk density (RHOB) and electrical resistivity (RT). The outputs of model were also permeability and porosity values. The structure developed model was build and trained by using train data. Graphical and statistical validation of results showed that the developed model is effective in prediction of field NMR log data. Outcomes show great possibility of using conventional logging data be used in order to reach the precious NMR logging data without any unnecessary costly tests for a reservoir. Moreover, the considerable accuracy of newly ANN-Cuckoo method also demonstrated. This study can be an illuminator in areas of reservoir engineering and modelling studies were presence of accurate data must be essential.

Abstract Modeling reservoir permeability is one of the crucial tasks in reservoir simulation studies. Traditionally, it is done by kriging-based methods. More rigorous modeling of the permeability results in more reliable outputs of the reservoir models. Recently, a new category of geostatistical methods has been used for this purpose, namely multiple point statistics (MPS). By this new category of permeability modeling methods, one is able to predict the heterogeneity of the reservoir permeability as a continuous variable. These methods consider the direction of property variation in addition to the distances of known locations of the property. In this study, the reservoir performance of a modified version of the SPE 10 solution project as a pioneer case is used for investigating the efficiency of these methods and paralleling them with the kriging-based one. In this way, the permeability texture concept is introduced by applying some MPS methods. This study is accomplished in the conditions of real reservoir dimensions and velocities for the whole reservoir life. A continuous training image is used as the input of calculation for the permeability modeling. The results show that the detailed permeability of the reservoir as a continuous variable makes the reservoir simulation show the same fluid front movement and flooding behavior of the reservoir similar to the reference case with the same permeability heterogeneity. Some MPS methods enable the reservoir simulation to reproduce the fluid flow complexities such as bypassing and oil trapping during water flooding similar to the reference case. Accordingly, total oil production is predicted with higher accuracy and lower uncertainty. All studied cases are identical except for the permeability texture. Even histograms and variograms of permeabilities for the studied reservoir are quite similar, but the performance of the reservoir shows that kriging-based method results have slightly less accuracy than some MPS methods. Meanwhile, it results in lower uncertainty in outputs for this water flooding case performance.

Permeability is the most important petrophysical characteristic for determining how fluids pass through reservoir rocks. This study aims to develop and assess intelligent computer-based models for predicting permeability. The research focuses on three novel models-Decision Tree, Bagging Tree, and Extra Trees-while also investigating previously applied techniques such as random forest, support vector regressor (SVR), and multiple variable regression (MVR). The primary dataset consists of 197 data points from a heterogeneous petroleum reservoir in the Jeanne d'Arc Basin, including laboratory-derived permeability ( K ), oil saturation ( S O ), water saturation ( S W ), grain density ( ρ g r ), porosity (φ), and depth. The most effective machine learning models are identified by a thorough analysis that makes use of a variety of statistical metrics, such as the coefficient of the determinant (R 2 ), mean squared error (MSE), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), maximum error (maxE), and minimum error (minE). Additionally, core features are ranked based on their importance in permeability modeling. This study deviates from conventional approaches by proposing an efficient means of forecasting permeability, reducing reliance on labor-intensive and time-consuming laboratory work. The findings reveal that MVR is unsuitable for permeability prediction, with all developed models outperforming it. Extra Trees emerges as the most accurate model, with an R 2 of 0.976, while random forest and bagging tree exhibit slightly lower R 2 values of 0.961 and 0.964, respectively. The ranking of these algorithms based on performance criteria is as follows: extra trees, bagging tree, random forest, SVR, decision tree, and MVR. The study also presents a detailed analysis of the impact of input parameters, highlighting porosity ( φ ) and water saturation ( S W ) as the most influential, while grain density ( ρ g r ), oil saturation ( S O ), and depth are considered less important. This study contributes to the petroleum industry's knowledge by showcasing the inadequacy of MVR and highlighting the superior performance of machine learning models, particularly Extra Trees. The proposed models employed in this study can help engineers and researchers determine reservoir permeability quickly and accurately by using a few core attributes, reducing the dependency on resource-intensive and time-consuming laboratory work., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

Modeling reservoir permeability is one of the crucial tasks in reservoir simulation studies. Traditionally, it is done by kriging-based methods. More rigorous modeling of the permeability results in more reliable outputs of the reservoir models. Recently, a new category of geostatistical methods has been used for this purpose, namely multiple point statistics (MPS). By this new category of permeability modeling methods, one is able to predict the heterogeneity of the reservoir permeability as a continuous variable. These methods consider the direction of property variation in addition to the distances of known locations of the property. In this study, the reservoir performance of a modified version of the SPE 10 solution project as a pioneer case is used for investigating the efficiency of these methods and paralleling them with the kriging-based one. In this way, the permeability texture concept is introduced by applying some MPS methods. This study is accomplished in the conditions of real reservoir dimensions and velocities for the whole reservoir life. A continuous training image is used as the input of calculation for the permeability modeling. The results show that the detailed permeability of the reservoir as a continuous variable makes the reservoir simulation show the same fluid front movement and flooding behavior of the reservoir similar to the reference case with the same permeability heterogeneity. Some MPS methods enable the reservoir simulation to reproduce the fluid flow complexities such as bypassing and oil trapping during water flooding similar to the reference case. Accordingly, total oil production is predicted with higher accuracy and lower uncertainty. All studied cases are identical except for the permeability texture. Even histograms and variograms of permeabilities for the studied reservoir are quite similar, but the performance of the reservoir shows that kriging-based method results have slightly less accuracy than some MPS methods. Meanwhile, it results in lower uncertainty in outputs for this water flooding case performance. [ABSTRACT FROM AUTHOR]

Abstract In this paper, an integrated procedure was adopted to obtain accurate lithofacies classification to be incorporated with well log interpretations for a precise core permeability modeling. Probabilistic neural networks (PNNs) were employed to model lithofacies sequences as a function of well logging data in order to predict discrete lithofacies distribution at missing intervals. Then, the generalized boosted regression model (GBM) was used as to build a nonlinear relationship between core permeability, well logging data, and lithofacies. The well log interpretations that were considered for lithofacies classification and permeability modeling are neutron porosity, shale volume, and water saturation as a function of depth; however, the measured discrete lithofacies types are sand, shaly sand, and shale. Accurate lithofacies classification was achieved by the PNN as the total percent correct of the predicted discrete lithofacies was 95.81%. In GBM results, root-mean-square prediction error and adjusted R-square have incredible positive values, as there was an excellent matching between the measured and predicted core permeability. Additionally, the GBM model led to overcome the multicollinearity that was available between one pair of the predictors. The efficiency of boosted regression was demonstrated by the prediction matching of core permeability in comparison with the conventional multiple linear regression (MLR). GBM led to much more accurate permeability prediction than the MLR.

Integrating discrete distribution of lithofacies into the petrophysical property modeling is essential to preserve reservoir heterogeneity and improve flow modeling. Specifically, various studies have been implemented to model the permeability as a function of well logging data without taking into account the effect of lithofacies, which is rational to produce distinct regression lines given each facies type. In this paper, advanced statistical learning approaches were adopted as an integrated workflow to model the core permeability given well logging records and discrete lithofacies classification for a well in the South Rumaila oil field, located in Iraq. In particular, the probabilistic neural networks was first applied for modeling and predicting the discrete lithofacies classification given the well logging records: neutron porosity, shale volume, and water saturation. Next, smooth generalized additive model (SGAM) was used to model the core permeability as a function of the same well logging records. In addition, the predicted lithofacies was included as a discrete independent variable in the core permeability modeling to provide different regression lines given each lithotype. The SGAM was also modeled for three subset data given each separate lithofacies to verify the efficiency of SGAM and to provide more accurate prediction of permeability. The same procedure of SGAM was completely repeated by the generalized linear model (GLM) to prove the higher effectiveness of SGAM for permeability modeling and prediction. The root mean square prediction error in SGAM was lower than in GLM in all the combined and separate lithofacies models. In addition, the SGAM model overcame the multicollinearity between shale volume and water saturation variables by using the smoothed terms. Finally, making accurate permeability prediction for all wells in the field should ensure capturing the spatial variation and correlation between the data and then preserving the reservoir heterogeneity. [ABSTRACT FROM AUTHOR]

In this research, Bayesian model averaging (BMA) and least absolute shrinkage and selection operator regression (LASSO) algorithms were adopted for the core permeability modeling as a function of well log and core measurements. More specifically, the core permeability (dependent factor) was modeled given the well log and core data (independent variables) and then predicted in non-cored intervals of a well in a sandstone formation. The BMA is a stochastic linear modeling and a Bayesian parameter selection. Among 50 linear models generated as a function of the independent variables and depicted in Occam's window, the best model of the highest posterior probability is determined. The best model has the optimal subset parameters that most influences the response factor. The core permeability modeling was again conducted using the LASSO algorithm, which adopts a completely different way of modeling and subset selection by using the penalized least-squared equation. Both BMA and LASSO resulted in a very accurate prediction of the core permeability by achieving perfect measured and calculated permeability matching. Matches for the two algorithms were quantified by computing the adjusted R-squared and root-mean-squared prediction error. In addition, results of the BMA and LASSO were compared to the conventional multiple linear regression (LM). The LASSO algorithm led to an accurate matching similar to LM, but slightly better than BMA. Therefore, both BMA and LASSO represent integrated procedures for accurate permeability estimation similar to conventional regression analysis. [ABSTRACT FROM AUTHOR]

That permeability is a critical parameter dictating the performance of naturally fractured reservoirs, like coalbed methane (CBM), is evident form the available field, experimental and permeability modeling information in the literature. Although modeling is often achieved at the expense of several input parameters, the exercise is typically unable to match sudden increases in coal permeability, encountered in deep coals after significant depletion. This paper is aimed at coupling stress and permeability in order to reduce the number of parameters required for modeling the permeability variation. Stresses in the reservoir are translated to invariants and stress path of coal is established in octahedral effective stress plane. Based on a detailed analysis of the stress path of three different coal types, a permeability model is presented in terms of stresses alone, that is applicable for elastic as well as inelastic deformations of coal. The model is validated using pressure-dependent-permeability experimental data for three coal types along with the geomechanical testing data used to develop the failure envelope. The primary implication of the study is improved capability to predict permeability of deep coal deposits, given that they are likely to undergo inelastic deformation or shear failure with continued depletion, using one parameter only. Finally, realistic constraints on the values of the parameter are provided to enable operators with the necessary tools to use the model for field applications, particularly in the new and upcoming CBM fields. [ABSTRACT FROM AUTHOR]

In this paper, an integrated procedure was adopted to obtain accurate lithofacies classification to be incorporated with well log interpretations for a precise core permeability modeling. Probabilistic neural networks (PNNs) were employed to model lithofacies sequences as a function of well logging data in order to predict discrete lithofacies distribution at missing intervals. Then, the generalized boosted regression model (GBM) was used as to build a nonlinear relationship between core permeability, well logging data, and lithofacies. The well log interpretations that were considered for lithofacies classification and permeability modeling are neutron porosity, shale volume, and water saturation as a function of depth; however, the measured discrete lithofacies types are sand, shaly sand, and shale. Accurate lithofacies classification was achieved by the PNN as the total percent correct of the predicted discrete lithofacies was 95.81%. In GBM results, root-mean-square prediction error and adjusted R-square have incredible positive values, as there was an excellent matching between the measured and predicted core permeability. Additionally, the GBM model led to overcome the multicollinearity that was available between one pair of the predictors. The efficiency of boosted regression was demonstrated by the prediction matching of core permeability in comparison with the conventional multiple linear regression (MLR). GBM led to much more accurate permeability prediction than the MLR. [ABSTRACT FROM AUTHOR]

The influence of barrier coating defectiveness on the overall permeating flux through a bilayer film is considered with particular attention to the effect of spatial defect distribution. To that aim, the diffusion equations for the permeating species have been solved in a large number of 3D geometries, built to simulate a bilayer structure with defects randomly distributed on the coating surface. A numerical approach based on finite volume method was used and, for each value of defectiveness considered, ranging from 0.25 to 10%, a minimum number of 200 different geometries were analyzed, in order to obtain statistically meaningful results. The numerical simulations results, which for regular arrays of defects also compares rather well with data available in the open literature, showed that the average flux obtained for each different defectiveness is not a fixed value, but rather shows substantial variations due to the randomness of the defect distribution. In particular, when few defects are present on the surface, differences higher than 10% have been observed in the flux calculated, while deviation lower than 3% with respect to the average value were obtained when the defects fraction on the surface was about 10%. Interestingly, the random distribution of defects on the surface gives average flux which are always smaller than the corresponding ordered geometry and does not result in a Gaussian distribution of the permeate flux. Distributions close to the normal one were indeed observed only for higher defectiveness while, for lower defect fractions, flux distributions with a clear tail towards the lower permeation rates were obtained. [ABSTRACT FROM AUTHOR]

Unsaturated permeability in soil is useful in assessment of water consumption by roots and suction distribution in slopes, which in turn, helps in design for the stability of slopes/covers. Stress factor is important in understanding the behavior of the unsaturated soils because it has a significant effect on its permeability as it brings micropores and minipores changes in the soil structure. Past studies have often neglected to measure its effect on the permeability property of the soil. The present study will introduce an optimization framework of genetic programming (GP) in developing the explicit relation of the permeability and the stress of the unsaturated clay. Experimental validation of the GP model will be done using the metrics such as the coefficient of determination, the root mean square error and the mean absolute percentage error. 2-D analysis of the model will be useful for experts to monitor the permeability property unsaturated clay. [ABSTRACT FROM AUTHOR]

This article demonstrates how to model permeability in different levels of a reservoir, integrating petrophysical and seismic data. In the first part of the present study seismic data is interpreted, including horizon and fault interpretations. Based on this, a reliable structural model of the prospect is built. In the second part, three common geostatistical techniques (including kriging, sequential Gaussian simulation, and collocated co-simulation) are employed to distribute permeability over the studied area such that it realistically preserves the reservoir heterogeneity and matches the well data. Validity of the results of each technique is checked by statistical methods like histogram analysis and cross validation approach. The proposed methodology is presented with an application to field information of a carbonate oil reservoir with a relatively high level of heterogeneity, located in the Persian Gulf, Iran. The results indicate that constructed 3D geological models provide realistic descriptions of reservoir permeability considering available geological interpretations of studied area. [ABSTRACT FROM PUBLISHER]

The influence of stress, which is one of the constitutive variables that governs unsaturated soil behavior, on the permeability has been recognized by various researchers. Stress factor is essential to study as it drastically alters the soil matrix which includes macropores, minipores and micropores and thus affecting the ability of soils to retain water and also permeability. An evolutionary approach of multi-gene genetic programming (MGGP), which automatically evolves model structure and coefficients can also be applied. However, the effective functioning of MGGP may be affected by the formulation of robust-multi-gene model and the poor selection of the best model. Therefore, a new evolutionary approach of MGGP (E-MGGP) is proposed by incorporating the stepwise and the classification strategies and applied to formulate the functional relationship between the permeability and input variables (stress and initial void ratio). The results reveal that the E-MGGP model outperformed the other three models (MGGP, support vector regression and artificial neural network). Further, the parametric and sensitivity analysis conducted validates the robustness of our proposed model by unveiling dominant input process variables and hidden nonlinear relationships. [ABSTRACT FROM AUTHOR]

The present research aimed to investigate the influence of layer arrangement and composition ratio on tensile and transport properties in bi-constituent electrospun nanofibrous membranes composed of polyurethane nanofibers with a hydrophobic nature and poly(2-acrylamido-2-methylpropanesulfonic acid) with a hydrophilic nature. Different hybrid electrospun nanofibrous membranes were produced based on different layer arrangement and composition ratios. To evaluate the membrane performance, their tensile strength, wind, and waterproof performance were measured. By adding 50% poly(2-acrylamido-2-methylpropanesulfonic acid) to polyurethane, the pore size of the hybrid electrospun nanofibrous membranes decreased by 47.64%. As a result, compared with pristine polyurethane membranes, hybrid electrospun nanofibrous membranes showed a good (9.6 mm s−1) windproof performance. Adding poly(2-acrylamido-2-methylpropanesulfonic acid) nanofibers to the polyurethane resulted in a decrease in mechanical properties and waterproof performance. The results show that the volume fraction of the poly(2-acrylamido-2-methylpropanesulfonic acid) and the electrospinning scenarios have a great influence on the mechanical properties of the samples. The results also show that the mechanical properties of hybrid electrospun nanofibrous membranes can be predicted based on the geometrical properties of each component. Computational fluid dynamics were used to simulate air flow through a virtual medium and the results of simulation were compared with the experimental measurement and predicted permeability; then, the best models for predicting air permeability were determined. In addition, based on different types of use, duration of use, and relative humidity level, a bi-functional membrane can be obtained by regulating layer arrangement and composition ratios to suit various applications, for example, in medical disposable clothing, wound dressing, filtering industries, and protective clothing. [ABSTRACT FROM AUTHOR]

The effects of the impregnation of three types of inorganic fillers into polyvinylidene fluoride (PVDF) polymer membranes on the gas permeability and selectivity of these membranes were studied theoretically and experimentally. Permeabilities of He, CO2, O2, and N2 through three types of mixed matrix membranes (MMMs) based on PVDF, that is, PVDF/SiO2, PVDF/MCM-41, and PVDF/4A MMMs, were experimentally measured and theoretically predicted using Maxwell, Higuchi, Bruggeman, and Bottcher-Landauer models. Theoretical permeabilities of the PVDF/SiO2 MMMs using the above four models predicted the results in the following order: Maxwell model>Bruggeman model>Bottcher model>Higuchi model. However, this sequence was reversed for PVDF/MCM-41 MMMs. The nonporous SiO2, mesoporous MCM-41 and zeolite 4A inorganic fillers had effects on the permeabilities of the challenge gases for the PVDF/SiO2, PVDF/MCM-41, and PVDF/4A MMMs but had no effects on the selectivities of the MMMs. The experimental permeabilities of the MMMs showed that there were no significant differences among the three types of MMMs despite that the inorganic fillers, that is, SiO2, MCM-41, and zeolite 4A, had distinct dissimilar properties such as pore structures and particle sizes. Density measurements indicated that some voids were present in the polymer/particle interfaces. Based on the density measurement results, the void volume fractions of the resulting MMMs were calculated. An equation is derived to determine the void thickness of the MMM in terms of its physical properties and hence this proposed equation can substitute the difficult task of measuring such void thickness through any microscopy techniques. The Maxwell, Higuchi, Bruggeman, and Bottcher-Landauer models could not predict the actual gas permeabilities of the PVDF MMMs. By taking the effects of crystallinity and immobilization factor on gas permeability into consideration, the extended modified Maxwell model showed good agreement with the experimental gas permeabilities of the resulting MMMs, indicating that the model did capture the essence of the gas transport behaviors through the MMMs. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4715-4726, 2013 [ABSTRACT FROM AUTHOR]

Abstract: Permeability increase in coalbed methane (CBM) reservoirs during primary depletion, particularly in the San Juan Basin, is a well accepted phenomenon. It is complex since it is influenced by stress conditions and coal matrix shrinkage associated with gas desorption. Understanding the variations in coal permeability is critical in order to reliably project future gas production, or consider other gas migration issues in the reservoir. Since sorption-induced strain plays a critical role in changing the permeability, typically observed, the theoretical strain model should be incorporated into the permeability prediction models. An effort is made in this paper to couple the recently developed Liu and Harpalani sorption-induced strain model with various permeability models. The model first calculates the theoretical coal matrix shrinkage strain and, using the calculated strain, various commonly used permeability models are applied to two sets of field data. The results of the coupled models show that the agreement between the predicted permeability and that observed in the field is very good. The merit of the coupled models is that it can theoretically predict the permeability with less experimental work, making it a more time efficient and economical technique compared to models used in the past. [Copyright &y& Elsevier]

Abstract: Fibrous media as aerosol filtration devices are widely used in many industrial applications and present a wide variety of features like the fiber composition, the solid volume fraction, the fiber diameter distribution and the fiber orientation. Among them are nonwovens materials, which are mostly composed of fibers blends. The two main characteristics of these filters are the filtration efficiency and the pressure drop. These properties depend on the filter thickness, the face velocity, the fluid viscosity, etc. Recently, predictive expressions for bimodal media have been developed based on the combination of unimodal equivalent radius models and dimensionless permeability. The main contribution of this work is to propose combinations for a wide range of realistic fibrous filters in addition to experimental validation. The building of a universal model to predict the permeability of bimodal fibrous media for a wide range of solid volume fraction, and fiber diameter ratio, R is envisaged. Therefore our approach has been to test different analytical correlations for and . Based on well-known permeability models, combinations between dimensionless permeability models given by or and unimodal equivalent radius from , or were compared with numerical results obtained with GeoDict code. Even if the building of a universal model is far from being completed due to the large number of variables and their dependence, this comparison categorizes previous models in a unique way, which made it easy for readers to use them in different applications. [Copyright &y& Elsevier]

Abstract: In this paper, second of the two-part series, pressure-permeability trend established in the laboratory was compared with modeled results using two analytical models, Shi and Durucan and Palmer et al., developed to predict changes in permeability of coalbed methane reservoirs with continued production. The experimental results did not match the modeled results very well for either of the two models. Also, there appeared to be several uncertainties, primarily the constancy of cleat compressibility and estimation of certain input parameters. A modification of the Shi and Durucan model was then proposed to increase the impact of the shrinkage effect on permeability. With this modification, the measured and modeled results matched perfectly, with the modeled permeability of coal increasing continuously, just as observed in the laboratory and supported by field data, and the rate of increase accelerating at low pressures. The phenomenon was explained by analyzing the permeability as a function of reservoir pressure. [Copyright &y& Elsevier]

Abstract: Pressure drop is one of the most important characteristics of a fibrous media. While numerous analytical, numerical, and experimental published works are available for predicting the permeability of media made up of fibers with a unimodal fiber diameter distribution (referred to as unimodal media here), there are almost no easy-to-use expressions available for media with a bimodal fiber diameter distribution (referred to as bimodal media). In the present work, the permeability of bimodal media is calculated by solving the Stokes flow governing equations in a series of 3-D virtual geometries that mimic the microstructure of fibrous materials. These simulations are designed to establish a unimodal equivalent diameter for the bimodal media thereby taking advantage of the existing expressions of unimodal materials for permeability prediction. We evaluated eight different methods of defining an equivalent diameter for bimodal media and concluded that the area-weighted average diameter of Brown and Thorpe [2001. Glass-fiber filters with bimodal fiber size distributions. Powder Technology 118, 3–9], volume-weighted resistivity model of Clague and Phillips [1997. A numerical calculation of the hydraulic permeability of three dimensional disordered fibrous media. Physics of Fluids 9 (6), 1562–1572], and the cube root relation of the current paper offer the best predictions for the entire range of mass (number) fractions, , with fiber diameter ratios, , and solidities, . [Copyright &y& Elsevier]

In permeability modeling of fault damage zones affecting porous sandstones, deformation bands are commonly considered low-permeability structures that influence fluid flow. Permeability in damage zones of such rocks exhibits and is influenced by complex networks of deformation bands separated by volumes of seemingly undeformed host rock. This study used deformation band frequency data to delimit and model the fault damage zone subdomains and fault facies in the hanging wall of the Malta Fault segment in the Rio do Peixe Basin, Brazil. We then integrate in situ permeability data collected in deformation bands and interband spaces (deformed host rock located between adjacent deformation bands) with damage zones and fault facies models to estimate the equivalent permeability perpendicular and parallel to the main fault. Our results indicated a logarithmic decrease in deformation band frequency towards the protolith with local variations controlled by minor faults in the damage zone. We identified three damage zone subdomains (inner and outer damage zones and transitional zone), totalizing roughly 200 m of width. Permeabilities of the deformation bands and interband spaces increase towards the protolith non-uniformly due to the difference in deformation intensity. The deformation bands and interband spaces in the inner and outer damage zones, which comprise fault facies representing the highest deformation intensities, exhibit permeability reductions of 1–3 orders of magnitude compared to the undeformed rocks, whereas 0–2.5 orders of magnitude reductions were observed in the transitional zone between the damage zone and the protolith. This study provides new perspectives about the permeability in deformation bands and interband spaces by different subdomains of damage zones and fault facies. This information proved is essential for modeling equivalent permeabilities along damage zones and assessing its implications to siliciclastic reservoirs affected by fault zones, where deformation bands are pervasive. [Display omitted] • We study the hanging wall damage zone of rift fault in Rio do Peixe Basin, Brazil. • Deformation band frequency shows a logarithmic decrease from the fault core. • Fault facies are distributed differently in damage zones subdomains. • Deformation bands and interband space permeabilities vary in fault facies and damage zones. • Parallel and perpendicular equivalent permeability tensors vary along damage zones. [ABSTRACT FROM AUTHOR]

The barrier property of polypropylene (PP)/polyamide (PA) blend film produced by the extrusion casting film process was studied by investigating the effects of rheological properties of each components, compatibilizer content, screw rpm, and the absorbed moisture, and by performing morphological analysis and permeability modeling. The oxygen barrier property of blend film with ellipsoidal structure of PA dispersed phase was significantly improved by a factor of 5.4 with the addition of only 20 wt% PA, when compatibilizer was added by the content of 7 phr and the PP matrix resin and process condition was selected to induce lower viscosity ratio less than unity. Using Fricke’s model with the value of 19.6 of aspect ratio of the ellipsoid, the permeabilities of blend films as a function of PA content could be predicted and exhibited a good agreement with the experimental results. The absorbed moisture had a significant influence on the barrier property of the PP/PA blend system, suggesting that the hygroscopic PA resin should be incorporated with the high water barrier PP resin by the amount below about 20 wt% to minimize the deterioration of the barrier property in the humid atmosphere. [ABSTRACT FROM AUTHOR]

An integrated multidisciplinary workflow of machine learning and data analytics was conducted for the multivariate geostatistical characterization of clastic reservoirs. This workflow was adopted for the estimation of lithofacies and permeability distributions in non-core intervals and wells on data from the upper shale member/Zubair formation in Luhais oil field, southern Iraq. Specifically, the workflow includes field and reservoir description, review of univariate, bivariate, and multivariate statistics for facies and petrophysical property distribution. In univariate and bivariate statistical analysis, the correction of core and log scale was adopted in order to have core permeability corrected to log scale. The discrete lithofacies distribution has been obtained for a few wells to be later incorporated in the permeability modeling and prediction. At first, the measured discrete lithofacies distributions were incorporated into classification algorithms to model the facies given the well logging data (shale volume, neutron porosity and water saturation) in order to predict the discrete facies distribution at other wells of missing records. The Multinomial Logistic Regression (Multinom), Logistic Boosting Regression (LogitBoost), and Extreme Gradient Boosting (XGBoost) were all comparatively adopted for lithofacies classification. After that, the resulting most accurate discrete facies distribution by LogitBoost were included along with the well logging interpretations into the multivariate permeability modeling through advanced machine learning approaches to model and predict the corrected core permeability given well logging interpretations for all wells in the reservoir. More specifically, the Multivariate Adaptive Regression Splines (MARS) and Smooth Generalized Additive Models (SGAM) were comparatively adopted to model and predict the core permeability at all wells. From this workflow, it was indicated that LogitBoost and MARS were the best machine learning algorithms to produce the most realistic lithofacies classification and permeability modeling, respectively. • Lithofacies classification and permeability modeling were adopted on a clastic reservoir in Luhais oil field. • Prior to permeability modelling, the core permeability was corrected to log scale. • The lithofacies and permeability were conducted given the well logging interpretations and lithofacies. • The Multinomial Logistic Regression and Logistic Boosting Regression were adopted for lithofacies classification. • The Multivariate Adaptive Regression Splines and Smooth Generalized Additive Models were for permeability modelling. [ABSTRACT FROM AUTHOR]

Multiple point statistics (MPS) methods are good tools to recreate the spatial heterogeneity of permeability in porous media. On the other hand, a reliable forecast of reservoir performance necessitates a large number of realistic permeability realizations which could be achieved by MPS techniques. However, the cost of numerical simulation on a large number of realizations, especially for screening, optimization or history matching purposes, is completely prohibitive. Emerging techniques in deep learning such as Long Short-Term Memory (LSTM) networks can provide robust proxy models replacing the numerical simulation with reasonable run-time and acceptable predictability. We train LSTM networks using some of realizations as input with a novel implementation. The LSTM network accepts the permeability values in a state of art manner, i.e. accepting the permeability values row by row. The network is trained with reservoir simulation outputs of corresponding MPS-generated realizations. In fact, the output values for training (the labels) are the reservoir simulation outputs for each realization which are obtained by an MPS method. In this way, MPS can be coupled with deep learning to find the realizations having the best match with a reference case. The architecture of the trained LSTM network is illustrated with details. The results show that the trained network is able to find realizations resulting in an adequate match with the reference case. These realizations not only exhibit fairly good prediction in future times of simulation due to MPS-derived permeability heterogeneity within but have an acceptable match with the historical reservoir performance. Our hybrid MPS-LSTM approach allows reducing the uncertainty in reservoir performance prediction. The reason is both meticulous heterogeneity modeling and deep-learning-assisted approach of realization screening. The proposed workflow could be a promising basis for history matching and uncertainty quantification calculation of reservoir. [ABSTRACT FROM AUTHOR]

Different sources of data are used to construct a reliable model of reservoir for oil/gas production. This model ought to be matched with the production history of reservoir and also show reliable predictions for future performance. To this end, permeability modeling (characterization of heterogeneity) is crucially important which is proved to be done by Multiple Point Statistics (MPS) recently. Furthermore, deep learning methods are massively used as a promising tool for regression applications. In this study, one MPS method is employed for generating the reservoir realizations. Realizations, alongside their simulation outputs, are utilized for training a convolutional deep network. In this manner, MPS is joined with deep learning to find the most appropriate realization(s) of the reservoir based on the fluid flow simulation. Moreover, unseen MPS realizations as well as another MPS realizations are used to verify the selection ability of trained network. The detailed architecture of convolutional network is illustrated in this study. The purpose of training this network and combination with MPS is to generate the matched realization(s) in history period that also show acceptable reservoir behavior in the future times of reservoir simulation. After training, the actual production data of selected realizations are obtained by simulation the reservoir for history and also future times. The results show that selected realizations efficiently capture the trend of reference behavior. Although these realizations lack identical permeability values, they have same texture of permeability (permeability heterogeneity). Meanwhile, they show acceptable match in reservoir simulation outputs. By proposed workflow, the uncertainty of permeability modeling is considered more exhaustively. It is done by selecting the realizations from enormous possible realizations dataset and providing a deep learning tool which is capable for screening quite large number of realizations. Interesting finding is satisfactory behavior of realization(s) in both history and future periods of reservoir performance. Image 1 • Novel implementation of MPS method with deep learning for reservoir simulation. • Finding realizations containing both heterogeneity and accurate outputs. • Introducing an efficient workflow for reservoir engineering issues. [ABSTRACT FROM AUTHOR]

In drug development, estimating fraction absorbed ( F a ) in man for permeability-limited compounds is important but challenging. To model F a of such compounds from apparent permeabilities ( P app ) across filter-grown Caco-2 cell monolayers, it is central to elucidate the intestinal permeation mechanism(s) of the compound. The present study aims to refine a computational permeability model to investigate the relative contribution of paracellular and transcellular routes to the P app across Caco-2 monolayers of the permeability-limited compound acamprosate having a bioavailability of ∼11%. The P app values of acamprosate and of several paracellular marker molecules were measured. These P app values were used to refine system-specific parameters of the Caco-2 monolayers, that is, paracellular pore radius, pore capacity, and potential drop. The refined parameters were subsequently used as an input in modeling the permeability ( P modeled ) of the tested compounds using mathematical models collected from two published permeability models. The experimental data show that acamprosate P app across Caco-2 monolayers is low and similar in both transport directions. The obtained acamprosate P app , 1.56 ± 0.28 × 10 -7 cm·s -1 , is similar to the P app of molecular markers for paracellular permeability, namely, mannitol (2.72 ± 0.24 × 10 -7 cm·s -1 ), lucifer yellow (1.80 ± 0.35 × 10 -7 cm·s -1 ), and fluorescein (2.10 ± 0.28 × 10 -7 cm·s -1 ), and lower than that of atenolol (7.32 ± 0.60 × 10 -7 cm·s -1 ; mean ± SEM, n = 3-6), while the end-point amount of acamprosate internalized by the cell monolayer, Q monolayer , was lower than that of mannitol. Acamprosate did not influence the barrier function of the monolayers since it altered neither the P app of the three paracellular markers nor the transepithelial electrical resistance (TEER) of the cell monolayer. The P modeled for all the paracellular markers and acamprosate was dominated by the P para component and matched the experimentally obtained P app . Furthermore, acamprosate did not inhibit the uptake of probe substrates for solute carriers PEPT1, TAUT, PAT1, EAAT1, B 0,+ AT/rBAT, OATP2B1, and ASBT expressed in Caco-2 cells. Thus, the P modeled estimated well P para , and the paracellular route appears to be the predominant mechanism for acamprosate P app across Caco-2 monolayers, while the alternative transcellular routes, mediated by passive diffusion or carriers, are suggested to only play insignificant roles.