Journal articles: 'Prediction of permeability'

1

Swery, Elinor E., Tom Allen, and Piaras Kelly. "Capturing the influence of geometric variations on permeability using a numerical permeability prediction tool." Journal of Reinforced Plastics and Composites 35, no. 24 (October 1, 2016): 1802–13. http://dx.doi.org/10.1177/0731684416669249.

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An automated tool has been developed for generation of permeability predictions for multi-layered unit cells utilising textile modelling techniques. This tool has been used to predict the permeability tensor of a woven textile. Single-layer predictions were carried out and the predicted permeabilities obtained were in close agreement to the permeability behaviour captured experimentally. The tool was used to capture the effects of textile variability on its permeability, isolating the influence of individual parameters. A complete textile sample was also analysed, predicting its permeability map. The concept of estimating the permeability of a textile with variability using an average single unit cell was explored. The prediction tool was also used to study the effect of preform structure on its permeability, including consideration of the number of layers, ply shift and applied compaction.

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Egan, William J., and Georgio Lauri. "Prediction of intestinal permeability." Advanced Drug Delivery Reviews 54, no. 3 (March 2002): 273–89. http://dx.doi.org/10.1016/s0169-409x(02)00004-2.

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Alam, M. Monzurul, Ida Lykke Fabricius, and Manika Prasad. "Permeability prediction in chalks." AAPG Bulletin 95, no. 11 (November 2011): 1991–2014. http://dx.doi.org/10.1306/03011110172.

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Sugano, Kiyohiko, Yoshiaki Nabuchi, Minoru Machida, and Yoshinori Aso. "Prediction of human intestinal permeability using artificial membrane permeability." International Journal of Pharmaceutics 257, no. 1-2 (May 2003): 245–51. http://dx.doi.org/10.1016/s0378-5173(03)00161-3.

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Robitaille, F., A. C. Long, and C. D. Rudd. "Permeability prediction for industrial preforms." Plastics, Rubber and Composites 31, no. 6 (June 2002): 238–48. http://dx.doi.org/10.1179/146580102225004992.

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Ji, Jinlan, and Guisheng Fan. "Prediction of the permeability-reducing effect of cement infiltration into sandy soils." Water Supply 17, no. 3 (November 15, 2016): 851–58. http://dx.doi.org/10.2166/ws.2016.183.

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Univariate analysis on the permeability-reducing effects of cement infiltration into sandy soil was carried out using a series of experiments on sandy soil infiltrated by adding fine cement grains. The SPSS statistical analysis software was used on these experimental data to construct multivariate prediction models on the permeability-reducing effects of cement infiltration into sandy soils. The results indicate that it is possible to predict permeability-reducing effects using transfer functions. Relatively satisfactory predictions were achieved by inputting the postponed time of water supply, soil dry density, quantity of added cement, water pressure head of cement infiltration, physical clay-silt particle content of soil, and other factors as independent variables. A comparison between the multivariate linear and non-linear models showed that the two models had similar accuracy. The multivariate linear model is relatively simple, and hence can be used to predict permeability-reducing effects. The development of the models has scientific implications for soil modification by altering soil permeability through cement infiltration. It also has practical significance in predictive research on reducing the migration of ground surface pollutants into groundwater.

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LEI, G., P. C. DONG, S. Y. MO, S. H. GAI, and Z. S. WU. "A NOVEL FRACTAL MODEL FOR TWO-PHASE RELATIVE PERMEABILITY IN POROUS MEDIA." Fractals 23, no. 02 (May 28, 2015): 1550017. http://dx.doi.org/10.1142/s0218348x15500176.

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Multiphase flow in porous media is very important in various scientific and engineering fields. It has been shown that relative permeability plays an important role in determination of flow characteristics for multiphase flow. The accurate prediction of multiphase flow in porous media is hence highly important. In this work, a novel predictive model for relative permeability in porous media is developed based on the fractal theory. The predictions of two-phase relative permeability by the current mathematical models have been validated by comparing with available experimental data. The predictions by the proposed model show the same variation trend with the available experimental data and are in good agreement with the existing experiments. Every parameter in the proposed model has clear physical meaning. The proposed relative permeability is expressed as a function of the immobile liquid film thickness, pore structural parameters (pore fractal dimension Dfand tortuosity fractal dimension DT) and fluid viscosity ratio. The effects of these parameters on relative permeability of porous media are discussed in detail.

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Delli, Mohana L., and Jocelyn L. H. Grozic. "Prediction Performance of Permeability Models in Gas-Hydrate-Bearing Sands." SPE Journal 18, no. 02 (March 27, 2013): 274–84. http://dx.doi.org/10.2118/149508-pa.

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Summary Permeability variation in the presence of gas hydrates (GH) is a major unknown in modeling hydrate dissociation in gas-hydrate-bearing sediment. Reduction of permeability in porous media occurs as a result of decreased porosity because of hydrate formation within pore spaces. In the absence of reliable experimental data, theoretical and empirical models have been proposed to establish the relationship between gas-hydrate saturation and permeability. The effectiveness of a particular permeability model in fitting the measured data has largely been qualitative through graphical analysis. In contrast, this paper introduces a quantitative performance measure to evaluate the effectiveness of an individual model in predicting the measured permeability. Second, a hybrid approach based on the weighted combination of existing permeability models is proposed. Permeability measurements from experimental and field studies were used to assess the prediction performance of various permeability models and the proposed hybrid approach.

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Andrew, Matthew. "Permeability Prediction using multivariant structural regression." E3S Web of Conferences 146 (2020): 04001. http://dx.doi.org/10.1051/e3sconf/202014604001.

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A novel method for permeability prediction is presented using multivariant structural regression. A machine learning based model is trained using a large number (2,190, extrapolated to 219,000) of synthetic datasets constructed using a variety of object-based techniques. Permeability, calculated on each of these networks using traditional digital rock approaches, was used as a target function for a multivariant description of the pore network structure, created from the statistics of a discrete description of grains, pores and throats, generated through image analysis. A regression model was created using an Extra-Trees method with an error of <4% on the target set. This model was then validated using a composite series of data created both from proprietary datasets of carbonate and sandstone samples and open source data available from the Digital Rocks Portal (www.digitalrocksporta.org) with a Root Mean Square Fractional Error of <25%. Such an approach has wide applicability to problems of heterogeneity and scale in pore scale analysis of porous media, particularly as it has the potential of being applicable on 2D as well as 3D data.

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Xu, S., and R. White. "Permeability prediction in anisotropic shaly formations." Geological Society, London, Special Publications 136, no. 1 (1998): 225–36. http://dx.doi.org/10.1144/gsl.sp.1998.136.01.19.

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11

Hossain, Delwar. "Prediction of permeability of fissured tills." Quarterly Journal of Engineering Geology and Hydrogeology 25, no. 4 (November 1992): 331–42. http://dx.doi.org/10.1144/gsl.qjeg.1992.025.04.07.

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Meng, Fancui, and Weiren Xu. "Drug permeability prediction using PMF method." Journal of Molecular Modeling 19, no. 3 (October 27, 2012): 991–97. http://dx.doi.org/10.1007/s00894-012-1655-1.

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13

Nazir, Ahsan, Tanveer Hussain, Ali Afzal, Sajid Faheem, Waseem Ibrahim, and Muhammad Bilal. "Prediction and Correlation of Air Permeability and Light Transmission Properties of Woven Cotton Fabrics." Autex Research Journal 17, no. 1 (March 1, 2017): 61–66. http://dx.doi.org/10.1515/aut-2015-0053.

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Abstract The aim of this study was to develop statistical models for predicting the air permeability and light transmission properties of woven cotton fabrics and determine the level of correlation between the two parameters. Plain woven fabrics were developed with different warp and weft linear densities, ends per inch and picks per inch. After desizing, scouring, bleaching, drying and conditioning, the air permeability and light transmission properties of the fabric samples were determined. Regression analysis results showed statistically significant effect of the fabric ends, picks and warp linear density on both the fabric air permeability and light transmission. Correlation analysis was performed to analyze the relation between the fabric air permeability and light transmission. A linear equation was also formulated to find the fabric air permeability through transmission of light intensity. A fitted line plot between the air permeability and light transmission exhibited significant correlation with R-sq. value of 96.4%. The statistical models for the prediction of fabric air permeability and light transmittance were developed with an average prediction error of less than 7%.

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Wang, Qiannan, Guoshuai Zhang, Yunyun Tong, and Chunping Gu. "Prediction on Permeability of Engineered Cementitious Composites." Crystals 11, no. 5 (May 10, 2021): 526. http://dx.doi.org/10.3390/cryst11050526.

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Permeability of concrete is regarded as a basic indicator of its durability. This paper proposed a simple model to predict the permeability of engineered cementitious composites (ECC), which are fiber reinforced cementitious composites with extremely high ductility and toughness. The permeability of cement paste in ECC was firstly determined based on the general effective media theory. The needed microstructure information of cement paste was obtained from a simulated microstructure. Porosity of the interfacial transition zone (ITZ) was obtained with an ITZ porosity model, and then used to calculate the permeability of ITZ. The permeability of the matrix was determined according to the general self-consistent scheme, and the influence of fiber was simplified with its volume fraction. The calculated permeability of ECC was verified with results from water permeability tests and the accuracy of the model was acceptable for cement-based materials.

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Gholami, R., A. R. Shahraki, and M. Jamali Paghaleh. "Prediction of Hydrocarbon Reservoirs Permeability Using Support Vector Machine." Mathematical Problems in Engineering 2012 (2012): 1–18. http://dx.doi.org/10.1155/2012/670723.

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Permeability is a key parameter associated with the characterization of any hydrocarbon reservoir. In fact, it is not possible to have accurate solutions to many petroleum engineering problems without having accurate permeability value. The conventional methods for permeability determination are core analysis and well test techniques. These methods are very expensive and time consuming. Therefore, attempts have usually been carried out to use artificial neural network for identification of the relationship between the well log data and core permeability. In this way, recent works on artificial intelligence techniques have led to introduce a robust machine learning methodology called support vector machine. This paper aims to utilize the SVM for predicting the permeability of three gas wells in the Southern Pars field. Obtained results of SVM showed that the correlation coefficient between core and predicted permeability is 0.97 for testing dataset. Comparing the result of SVM with that of a general regression neural network (GRNN) revealed that the SVM approach is faster and more accurate than the GRNN in prediction of hydrocarbon reservoirs permeability.

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Zhao, Bochao, Ram Ratnakar, Birol Dindoruk, and Kishore Mohanty. "A Hybrid Approach for the Prediction of Relative Permeability Using Machine Learning of Experimental and Numerical Proxy SCAL Data." SPE Journal 25, no. 05 (March 5, 2020): 2749–64. http://dx.doi.org/10.2118/196022-pa.

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Summary Accurate estimation of relative permeability is one of the key parameters for decision making in upstream applications from project appraisal to field development and evaluation of various field development options. In this study, we identify Euler number (Arns et al. 2001) (a quantitative measure of fluid connectivity/distribution) and saturation as being the first-order predictors of relative permeability and develop a reliable correlation between them using machine learning of experimental special core analysis (SCAL) data and pore network simulation results. In order to achieve our objective, first, we developed a machine-learning model based on the random forest algorithm (Breiman 2001) to analyze specific SCAL data that indicates a key missing feature in the traditional saturation-based relative permeability prediction. We identified this missing feature and proposed the Euler characteristic as a potential first-order predictor of relative permeability in combination with in-situ fluid saturations. We generated “artificial” relative permeability data using pore network simulation (Valvatne and Blunt 2004) by systematically varying a set of key parameters such as pore geometry, wettability, and saturation history. Subsequently, we used machine learning to rank the importance of each parameter and identify possible correlative responses to those selected variables. At a fixed saturation (zero-dimensional volumetric abundance) and Euler number coordinates, the relative permeability is very consistent and varies insignificantly across different cases, suggesting these two parameters as first-order predictors. Euler number characterizes the fluid connectivity/distribution, while saturation represents the net volumetric fluid quantity. We believe that Euler number could be the missing first-order predictor in traditional saturation-based predictive relative permeability models, especially for connected pathway dominated flow regime. Finally, we identified the quantitative relationship between relative permeability and Euler characteristic, and present a reliable correlation to determine the relative permeability on the basis of Euler number and saturation.

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17

HELMY, TAREK, and ANIFOWOSE FATAI. "HYBRID COMPUTATIONAL INTELLIGENCE MODELS FOR POROSITY AND PERMEABILITY PREDICTION OF PETROLEUM RESERVOIRS." International Journal of Computational Intelligence and Applications 09, no. 04 (December 2010): 313–37. http://dx.doi.org/10.1142/s1469026810002902.

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The hybridization of two or more Computational Intelligence (CI) techniques to build a single model has increased in popularity over the recent years. Such models that combine the best properties of different Artificial Intelligence (AI) techniques in a single package are very much required in the process of reservoir characterization in petroleum engineering, where a high degree of prediction accuracy is essential for efficient exploration, and management of oil and gas resources. In this paper, we have successfully predicted, with higher accuracy, the porosity and permeability of oil and gas reservoirs through the hybridization of Type-2 Fuzzy Logic (FL), Support Vector Machines (SVM) and Functional Networks (FN), using several real-life well log data. While utilizing the capabilities of data mining and CI, two hybrid models (FFS and FSF) were built. In both models, FN, using its functional approximation capability with least-square fitting algorithm, was used to select the best of the predictor variables from the input data. In the FFS model, the selected predictor variables were passed to Type-2 FL to remove uncertainties in the data (if any), and then to SVM for training and making final predictions. In the FSF model, the best predictor variables from FN were passed to SVM to transform them to a feature space, and then passed to Type-2 FL to remove uncertainties (if any), extract inference rules and make final predictions. The results showed that the hybrid models, with their higher correlation coefficients, performed better than the individual techniques when used separately with the same datasets. An extended study, used as a benchmark, showed that the hybrid models also performed better than a hybridization of only two of the techniques viz. Type-2 FL and SVM, both in terms of higher correlation coefficients and lower execution times. This was attributed to the role of FN in selecting the best variables and reducing the dimensionality of input data in the FFS and FSF models.

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Izadi, Mohammad, and Ali Ghalambor. "A New Approach in Permeability and Hydraulic-Flow-Unit Determination." SPE Reservoir Evaluation & Engineering 16, no. 03 (July 4, 2013): 257–64. http://dx.doi.org/10.2118/151576-pa.

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Summary Building an integrated subsurface model is one of the main goals of major oil and gas operators to guide the field-development plans. All field-data acquisitions from seismic, well logging, production, and geomechanical monitoring to enhanced-oil-recovery (EOR) operations can be affected by the accurate details incorporated in the subsurface model. Therefore, building a realistic integrated subsurface model of the field and associated operations is essential for a successful implementation of such projects. Furthermore, using a more reliable model can, in turn, provide the basis in the decision-making process for control and remediation of formation damage. One of the key identifiers of the subsurface model is accurately predicting the hydraulic-flow units (HFUs). There are several models currently used in the prediction of these units on the basis of the type of data available. The predictions that used these models differ significantly because of the assumptions made in the derivations. Most of these assumptions do not adequately reflect realistic subsurface conditions, thus increasing the need for better models. A new approach has been developed in this study for predicting the petrophysical properties and improving the reservoir characterization. The Poiseuille flow equation and Darcy equation were coupled, taking into consideration the irreducible water saturation in the pore network. The porous medium was introduced as a domain containing a bundle of tortuous capillary tubes with irreducible water lining the pore wall. A series of routine and special core analysis was performed on 17 Berea sandstone samples, and the petrophysical properties were measured and X-ray diffraction (XRD) analysis was conducted. In building the petrophysical model, it was initially necessary to assume an ideal reservoir with 17 different layers, each layer representing one Berea sample. Afterward, by the iteration and calibration of the laboratory data, the number of HFUs was determined by use of the common HFU model and the proposed model accordingly. A comparative study shows that the new model provides a better distribution of HFUs and prediction of the petrophysical properties. The new model provides a better match with the experimental data collected than the models currently used in the prediction of such parameters. The good agreement observed for the Berea sandstone experimental data and the model predictions by use of the new permeability model shows a wider range of applicability for various reservoir conditions. In addition, the model has been applied to a series of core-analysis data on low-permeability Medina sandstone, Appalachian basin, northwest Pennsylvania. The flow-unit distribution by use of the proposed model shows a better flow-zone distinction, and the permeability/ porosity relationship has a higher confidence coefficient.

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Chen, Zhiming, Shidong Pan, Zhengong Zhou, Tao Lei, Baofeng Dong, and Peifei Xu. "The Effect of Shear Deformation on Permeability of 2.5D Woven Preform." Materials 12, no. 21 (October 31, 2019): 3594. http://dx.doi.org/10.3390/ma12213594.

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The accurate prediction of the permeability is the key to optimizing the molding process of fiber reinforced composites, thus to improve the composite quality, and reduce the material and labor costs in the manufacturing process. In this paper, the permeability of 2.5D woven preform with shear deformation was studied by experiments and numerical simulations. The permeabilities of the samples under various shear angles were measured by the radial flow method. An RVE (representative volume element) model based on the fabric microstructure and shear deformation is developed to predict the permeability of preform and the simulation results are compared with experiments value to verify the effectiveness of this model. Using this model, the effect of the fiber volume fraction on the permeability of the 2.5D woven preform was determined. Based on the structural characteristics, experimental and simulation results of the 2.5D woven preform, an empirical equation for predicting its permeability under shear deformation was formulated. The prediction accuracy of the equation was evaluated, and the equation was used to determine the change of permeability with shear deformation for the 2.5D woven preform.

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Chi, Cheng-Ting, Ming-Han Lee, Ching-Feng Weng, and Max K. Leong. "In Silico Prediction of PAMPA Effective Permeability Using a Two-QSAR Approach." International Journal of Molecular Sciences 20, no. 13 (June 28, 2019): 3170. http://dx.doi.org/10.3390/ijms20133170.

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Oral administration is the preferred and predominant route of choice for medication. As such, drug absorption is one of critical drug metabolism and pharmacokinetics (DM/PK) parameters that should be taken into consideration in the process of drug discovery and development. The cell-free in vitro parallel artificial membrane permeability assay (PAMPA) has been adopted as the primary screening to assess the passive diffusion of compounds in the practical applications. A classical quantitative structure–activity relationship (QSAR) model and a machine learning (ML)-based QSAR model were derived using the partial least square (PLS) scheme and hierarchical support vector regression (HSVR) scheme to elucidate the underlying passive diffusion mechanism and to predict the PAMPA effective permeability, respectively, in this study. It was observed that HSVR executed better than PLS as manifested by the predictions of the samples in the training set, test set, and outlier set as well as various statistical assessments. When applied to the mock test, which was designated to mimic real challenges, HSVR also showed better predictive performance. PLS, conversely, cannot cover some mechanistically interpretable relationships between descriptors and permeability. Accordingly, the synergy of predictive HSVR and interpretable PLS models can be greatly useful in facilitating drug discovery and development by predicting passive diffusion.

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Peng, Yan, Jishan Liu, Wancheng Zhu, Zhejun Pan, and Luke Connell. "Benchmark assessment of coal permeability models on the accuracy of permeability prediction." Fuel 132 (September 2014): 194–203. http://dx.doi.org/10.1016/j.fuel.2014.04.078.

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Ecay, Lionel, David Grégoire, and Gilles Pijaudier-Cabot. "On the prediction of permeability and relative permeability from pore size distributions." Cement and Concrete Research 133 (July 2020): 106074. http://dx.doi.org/10.1016/j.cemconres.2020.106074.

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23

Fleury, Marc, Françoise Deflandre, and Sophie Godefroy. "Validity of permeability prediction from NMR measurements." Comptes Rendus de l'Académie des Sciences - Series IIC - Chemistry 4, no. 11 (November 2001): 869–72. http://dx.doi.org/10.1016/s1387-1609(01)01343-3.

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24

Wang, Ling. "Permeability Prediction for Monofilament Plain Woven Fabric." Advanced Science Letters 10, no. 1 (May 15, 2012): 573–76. http://dx.doi.org/10.1166/asl.2012.3396.

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25

Shilova, T. V., L. A. Rybalkin, and A. V. Yablokov. "Prediction of In-Situ Cleaved Coal Permeability." Journal of Mining Science 56, no. 2 (March 2020): 226–35. http://dx.doi.org/10.1134/s1062739120026686.

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26

Katz, A. J., and A. H. Thompson. "Quantitative prediction of permeability in porous rock." Physical Review B 34, no. 11 (December 1, 1986): 8179–81. http://dx.doi.org/10.1103/physrevb.34.8179.

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Segarra, V., M. López, H. Ryder, and J. M. Palacios. "Prediction of Drug Permeability Based onGrid Calculations." Quantitative Structure-Activity Relationships 18, no. 5 (November 1999): 474–81. http://dx.doi.org/10.1002/(sici)1521-3838(199911)18:5<474::aid-qsar474>3.0.co;2-n.

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Lanevskij, Kiril, Pranas Japertas, Remigijus Didziapetris, and Alanas Petrauskas. "Ionization-Specific Prediction of Blood–Brain Permeability." Journal of Pharmaceutical Sciences 98, no. 1 (January 2009): 122–34. http://dx.doi.org/10.1002/jps.21405.

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Guan, Kang, Laifei Cheng, Qingfeng Zeng, Hui Li, Shanhua Liu, Jianping Li, and Litong Zhang. "Prediction of Permeability for Chemical Vapor Infiltration." Journal of the American Ceramic Society 96, no. 8 (June 17, 2013): 2445–53. http://dx.doi.org/10.1111/jace.12456.

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Jones, C., J. M. Somerville, B. G. D. Smart, O. Kirstetter, S. A. Hamilton, and K. P. Edlmann. "Permeability prediction using stress sensitive petrophysical properties." Petroleum Geoscience 7, no. 2 (June 2001): 211–19. http://dx.doi.org/10.1144/petgeo.7.2.211.

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Xu, Guangbiao, and Fumei Wang. "Prediction of the Permeability of Woven Fabrics." Journal of Industrial Textiles 34, no. 4 (April 2005): 243–54. http://dx.doi.org/10.1177/1528083705051455.

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Weller, Andreas, Mohamed A. Kassab, Wolfgang Debschütz, and Carl-Diedrich Sattler. "Permeability prediction of four Egyptian sandstone formations." Arabian Journal of Geosciences 7, no. 12 (November 27, 2013): 5171–83. http://dx.doi.org/10.1007/s12517-013-1188-7.

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Mose, Douglas G., George W. Mushrush, and Charles E. Chrosniak. "Soil radon, permeability, and indoor radon prediction." Environmental Geology and Water Sciences 19, no. 2 (March 1992): 91–96. http://dx.doi.org/10.1007/bf01797437.

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Kakarash, Tariq, and Qays M. Sadeq. "Development Permeability prediction for Bai Hassan Cretaceous Carbonate Reservoir." UHD Journal of Science and Technology 2, no. 1 (May 25, 2018): 8. http://dx.doi.org/10.21928/uhdjst.v2n1y2018.pp8-18.

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Permeability and porosity are the most difficult parameters to estimate in the oil reservoir because they are varying significantly over the reservoir, especially in the carbonate formation. Porosity and permeability can only be sampled at the well location. However, porosity is easy to estimate directly from well log data, permeability is not. In addition, permeability measurements from core samples are very expensive. Carbonate reservoirs are very difficult to characterize because of their tendency to be tight and heterogeneous due to deposition and diagenetic processes. Therefore, many engineers and geologists try to establish methods to get the best characterization for the carbonate reservoir. In this study, available routine core data from three wells are used to develop permeability model based on hydraulic flow unit method (HFUM) (RQI/FZI) for cretaceous carbonate middle reservoirs of Bai Hassan oil field. The results show that the HFUM is work perfectly to characterize and predict permeability for uncored wells because R2 ≥ 0.9. It is indicating that permeability can be accurately predicted from porosity if rock type is known.

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Mathew Nkurlu, Baraka, Chuanbo Shen, Solomon Asante-Okyere, Alvin K. Mulashani, Jacqueline Chungu, and Liang Wang. "Prediction of Permeability Using Group Method of Data Handling (GMDH) Neural Network from Well Log Data." Energies 13, no. 3 (January 23, 2020): 551. http://dx.doi.org/10.3390/en13030551.

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Permeability is an important petrophysical parameter that controls the fluid flow within the reservoir. Estimating permeability presents several challenges due to the conventional approach of core analysis or well testing, which are expensive and time-consuming. On the contrary, artificial intelligence has been adopted in recent years in predicting reliable permeability data. Despite its shortcomings of overfitting and low convergence speed, artificial neural network (ANN) has been the widely used artificial intelligent method. Based on this, the present study conducted permeability prediction using the group method of data handling (GMDH) neural network from well log data of the West arm of the East African Rift Valley. Comparative analysis of GMDH permeability model and ANN methods of the back propagation neural network (BPNN) and radial basis function neural network (RBFNN) were further explored. The results of the study showed that the proposed GMDH model outperformed BPNN and RBFNN as it achieved R/root mean square error (RMSE) value of 0.989/0.0241 for training and 0.868/0.204 for predicting, respectively. Sensitivity analysis carried out revealed that shale volume, standard resolution formation density, and thermal neutron porosity were the most influential well log parameters when developing the GMDH permeability model.

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Abdull Yamin, Elsa Syuhada, and Nor Azwadi Che Sidik. "Prediction of Fluid Flow in Artificial Cancellous Bone." Applied Mechanics and Materials 695 (November 2014): 393–97. http://dx.doi.org/10.4028/www.scientific.net/amm.695.393.

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The permeability of the blood in the artificial cancellous are affected by certain morphological aspects that include pore diameter, pore size, porosity and the bone surface area. In this study, computational fluid dynamics method is used to study the fluid flow through the cancellous structure. Result of the present work show that geometries with the same porosity and overall volume can have different permeability due to the differences in bone surface area. The hexahedron geometry has the highest permeability under stimulated blood flow conditions, where the cylindrical geometry has the lowest. Linear relationship is found between permeability and the two physical properties, bone surface area and the pore size.

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Zhu, Guocheng, Yuan Fang, Lianying Zhao, Jinfeng Wang, and Weilai Chen. "Prediction of structural parameters and air permeability of cotton woven fabric." Textile Research Journal 88, no. 14 (April 25, 2017): 1650–59. http://dx.doi.org/10.1177/0040517517705632.

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Air permeability is a very important property influencing the performance of clothing comfort and technical textiles particularly in applications for protective products, including airbags, parachutes, and tents. Several analytical models for predicting air permeability have been made by considering porosity and pore diameter or porous area. However, the connection between fabric structure and air permeability with analytical models has not been well reported as yet. In this work, the diameter of cotton yarn was predicted by considering yarn count, twist, and packing density. Subsequently, the pore area and equivalent pore diameter of fabric were predicted after finding the warp and the weft densities of fabric. The predicted values had very good agreement with the experimental results in yarn diameter and other structural parameters of fabric. The air permeability of fabrics was measured and several well-known analytical models for predicting air permeability were compared. The results revealed that the Hagen–Poiseuille equation had much better prediction than other models and also had good agreement with the experimental results, especially when it was applied for tight fabrics at low pressure drop (≤60 Pa). The Hagen–Poiseuille equation could be improved by considering the Reynolds number, interfiber interstices, and the deformation of pores under higher pressure drop.

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Akande, Kabiru O., Taoreed O. Owolabi, Sunday O. Olatunji, and AbdulAzeez Abdulraheem. "A Novel Homogenous Hybridization Scheme for Performance Improvement of Support Vector Machines Regression in Reservoir Characterization." Applied Computational Intelligence and Soft Computing 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/2580169.

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Hybrid computational intelligence is defined as a combination of multiple intelligent algorithms such that the resulting model has superior performance to the individual algorithms. Therefore, the importance of fusing two or more intelligent algorithms to achieve better performance cannot be overemphasized. In this work, a novel homogenous hybridization scheme is proposed for the improvement of the generalization and predictive ability of support vector machines regression (SVR). The proposed and developed hybrid SVR (HSVR) works by considering the initial SVR prediction as a feature extraction process and then employs the SVR output, which is the extracted feature, as its sole descriptor. The developed hybrid model is applied to the prediction of reservoir permeability and the predicted permeability is compared to core permeability which is regarded as standard in petroleum industry. The results show that the proposed hybrid scheme (HSVR) performed better than the existing SVR in both generalization and prediction ability. The outcome of this research will assist petroleum engineers to effectively predict permeability of carbonate reservoirs with higher degree of accuracy and will invariably lead to better reservoir. Furthermore, the encouraging performance of this hybrid will serve as impetus for further exploring homogenous hybrid system.

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Di, Jianwei, and Jerry L. Jensen. "A New Approach for Permeability Prediction With NMR Measurements in Tight Formations." SPE Reservoir Evaluation & Engineering 19, no. 03 (April 13, 2016): 481–93. http://dx.doi.org/10.2118/180921-pa.

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Summary Prediction of extremely low permeability in tight reservoirs poses major challenges with traditional methods. Several studies have proposed nuclear magnetic resonance (NMR) permeability predictors, but these often give large errors when applied in tight formations. In this report, we describe a new method with NMR well-log measurements that decomposes the T2 spectrum into, at most, three Gaussian components. On the basis of parameters from the decomposition, we build a pore-size-based lithofacies model to predict whole-core horizontal permeability. With these parameters, we also modify the empirical Timur-Coates equation (TIM) to predict permeability. The NMR decomposition allows us to predict proportions of shale and silt. Applied to the tight Cardium formation, the parameters correlate strongly with core image and X-Ray-diffraction (XRD) results. In addition to Cardium data, we apply our approach to published data sets with good results, showing that the model gives accurate lithofacies-proportion estimates. To calibrate the model, Cardium probe permeameter data are used to identify facies permeabilities. Arithmetic-averaged permeability with the NMR-based model was calculated to compare with whole-core horizontal permeability. Monte Carlo analysis confirms the agreement between the model and core-permeability values. Our model provides a “bridge” to relate permeability between the probe scale (<1 cm laminations) and core size (>15 cm thin beds). Without the NMR well-log decomposition, Cardium TIM permeability predictions are in error by more than one order of magnitude in most intervals. The major challenge with the TIM model is obtaining an accurate T2 cutoff value. Compared with core-measured bound-water saturations, the default 33 ms value is too large for our tight samples. Our NMR decomposition, however, shows good correlation with measured bound-water saturations. With several core samples and NMR parameters, we modified the TIM model and found that it provides very good permeability predictions.

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Saxena, Deeksha, Anju Sharma, Mohammed H. Siddiqui, and Rajnish Kumar. "Blood Brain Barrier Permeability Prediction Using Machine Learning Techniques: An Update." Current Pharmaceutical Biotechnology 20, no. 14 (November 15, 2019): 1163–71. http://dx.doi.org/10.2174/1389201020666190821145346.

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Blood Brain Barrier (BBB) is the collection of vessels of blood with special properties of permeability that allow a limited range of drug and compounds to pass through it. The BBB plays a vital role in maintaining balance between intracellular and extracellular environment for brain. Brain Capillary Endothelial Cells (BECs) act as vehicle for transport and the transport mechanisms across BBB involve active and passive diffusion of compounds. Efficient prediction models of BBB permeability can be vital at the preliminary stages of drug development. There have been persistent efforts in identifying the prediction of BBB permeability of compounds employing multiple machine learning methods in an attempt to minimize the attrition rate of drug candidates taking up preclinical and clinical trials. However, there is an urgent need to review the progress of such machine learning derived prediction models in the prediction of BBB permeability. In the current article, we have analyzed the recently developed prediction model for BBB permeability using machine learning.

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Pape, Hansgeorg, Christoph Clauser, and Joachim Iffland. "Permeability prediction based on fractal pore‐space geometry." GEOPHYSICS 64, no. 5 (September 1999): 1447–60. http://dx.doi.org/10.1190/1.1444649.

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Estimating permeability from grain‐size distributions or from well logs is attractive but difficult. In this paper we present a new, generally applicable, and relatively inexpensive approach which yields permeability information on the scale of core samples and boreholes. The approach is theoretically based on a fractal model for the internal structure of a porous medium. It yields a general and petrophysically justified relation linking porosity to permeability, which may be calculated either from porosity or from the pore‐radius distribution. This general relation can be tuned to the entire spectrum of sandstones, ranging from clean to shaly. The resulting expressions for the different rock types are calibrated to a comprehensive data set of petrophysical and petrographical rock properties measured on 640 sandstone core samples of the Rotliegend Series (Lower Permian) in northeastern Germany. With few modifications, this new straightforward and petrophysically motivated approach can also be applied to metamorphic and igneous rocks. Permeability calculated with this procedure from industry porosity logs compares very well with permeability measured on sedimentary and metamorphic rock samples.

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Song, Hongqing, Shuyi Du, Ruifei Wang, Jiulong Wang, Yuhe Wang, Chenji Wei, and Qipeng Liu. "Potential for Vertical Heterogeneity Prediction in Reservoir Basing on Machine Learning Methods." Geofluids 2020 (August 12, 2020): 1–12. http://dx.doi.org/10.1155/2020/3713525.

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With the rapid development of computer technology, some machine learning methods have begun to gradually integrate into the petroleum industry and have achieved some achievements, whether in conventional or unconventional reservoirs. This paper presents an alternative method to predict vertical heterogeneity of the reservoir utilizing various deep neural networks basing on dynamic production data. A numerical simulation technique was adopted to obtain the required dataset, which contains dynamic production data calculated under different heterogeneous reservoir conditions. Machine learning models were established through deep neural networks, which learn and capture the characteristics better between dynamic production data and reservoir heterogeneity, so as to invert the vertical permeability. On the basis of model validation, the results show that machine learning methods have excellent performance in predicting heterogeneity with the RMSE of 12.71 mD, which effectively estimated the permeability of the entire reservoir. Moreover, the overall AARD of the predictive result obtained by the CNN method was controlled at 11.51%, revealing the highest accuracy compared with BP and LSTM neural networks. And the permeability contrast, an important parameter to characterize heterogeneity, can be predicted precisely as well, with a derivation of below 10%. This study proposed a potential for vertical heterogeneity prediction in reservoir basing on machine learning methods.

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Jung, Seo Jeong, Sun Ok Choi, So Young Um, Joo Il Kim, Hae Young Park Choo, Su Young Choi, and Soo Youn Chung. "Prediction of the permeability of drugs through study on quantitative structure–permeability relationship." Journal of Pharmaceutical and Biomedical Analysis 41, no. 2 (May 2006): 469–75. http://dx.doi.org/10.1016/j.jpba.2005.12.020.

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Clothier, Richard, Gemma Starzec, Lionel Pradel, Victoria Baxter, Melanie Jones, Helen Cox, and Linda Noble. "The Prediction of Human Skin Responses by Using the Combined In Vitro Fluorescein Leakage/Alamar Blue (Resazurin) Assay." Alternatives to Laboratory Animals 30, no. 5 (September 2002): 493–504. http://dx.doi.org/10.1177/026119290203000503.

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A range of cosmetics formulations with human patch-test data were supplied in a coded form, for the examination of the use of a combined in vitro permeability barrier assay and cell viability assay to generate, and then test, a prediction model for assessing potential human skin patch-test results. The target cells employed were of the Madin Darby canine kidney cell line, which establish tight junctions and adherens junctions able to restrict the permeability of sodium fluorescein across the barrier of the confluent cell layer. The prediction model for interpretation of the in vitro assay results included initial effects and the recovery profile over 72 hours. A set of the hand-wash, surfactant-based formulations were tested to generate the prediction model, and then six others were evaluated. The model system was then also evaluated with powder laundry detergents and hand moisturisers: their effects were predicted by the in vitro test system. The model was under-predictive for two of the ten hand-wash products. It was over-predictive for the moisturisers, (two out of six) and eight out of ten laundry powders. However, the in vivo human patch test data were variable, and 19 of the 26 predictions were correct or within 0.5 on the 0–4.0 scale used for the in vivo scores, i.e. within the same variable range reported for the repeat-test hand-wash in vivo data.

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Vilar, Santiago, Eduardo Sobarzo-Sanchez, Lourdes Santana, and Eugenio Uriarte. "Ligand and Structure-based Modeling of Passive Diffusion through the Blood-Brain Barrier." Current Medicinal Chemistry 25, no. 9 (March 29, 2018): 1073–89. http://dx.doi.org/10.2174/0929867324666171106163742.

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Background: Blood-brain barrier transport is an important process to be considered in drug candidates. The blood-brain barrier protects the brain from toxicological agents and, therefore, also establishes a restrictive mechanism for the delivery of drugs into the brain. Although there are different and complex mechanisms implicated in drug transport, in this review we focused on the prediction of passive diffusion through the blood-brain barrier. Methods: We elaborated on ligand-based and structure-based models that have been described to predict the blood-brain barrier permeability. Results: Multiple 2D and 3D QSPR/QSAR models and integrative approaches have been published to establish quantitative and qualitative relationships with the blood-brain barrier permeability. We explained different types of descriptors that correlate with passive diffusion along with data analysis methods. Moreover, we discussed the applicability of other types of molecular structure-based simulations, such as molecular dynamics, and their implications in the prediction of passive diffusion. Challenges and limitations of experimental measurements of permeability and in silico predictive methods were also described. Conclusion: Improvements in the prediction of blood-brain barrier permeability from different types of in silico models are crucial to optimize the process of Central Nervous System drug discovery and development.

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LEI, GANG, NAI CAO, and QINGZHI WEN. "PERMEABILITY PREDICTION IN ROUGHENED FRACTURES UNDER STRESS CONDITION USING FRACTAL MODEL." Fractals 27, no. 03 (May 2019): 1950030. http://dx.doi.org/10.1142/s0218348x19500300.

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The prediction of permeability in rough fracture under stress condition presents ever more of a challenge in various scientific and engineering fields. However, up to now, the essential controls on stress-dependent permeability of rough fracture are not determined. In order to find a relationship between the microstructure and the permeability of rough fracture, an analytical method for the permeability of roughened fracture under stress condition is proposed based on the fractal model. The validity of the proposed model is obtained by the good agreement between the simulated results and the experimental data. Compared with the previous models, our model takes into account more factors, including the influence of the microstructural parameters of rough fracture and rock lithology. This paper presents that (1) the rock with soft lithology can yield smaller normalized permeability, (2) normalized permeability decreases with the increases of percent of smaller rough elements. The fractal permeability model can reveal more mechanisms that affect the coupled flow deformation behavior in the fractured porous media.

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Jing, Wen, Luo Wei, Yin Qingguo, Wang Hongying, He Yongming, and Zhao Yarui. "PRODUCTIVITY PREDICTION OF FRACTURED HORIZONTAL WELLS WITH LOW PERMEABILITY FLOW CHARACTERISTICS." Journal of Environmental Engineering and Landscape Management 27, no. 2 (April 29, 2019): 82–92. http://dx.doi.org/10.3846/jeelm.2019.9803.

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Horizontal well and large-scale fracturing are revolutionary technologies in petroleum industry. The technologies bring obvious economic benefits to exploiting unconventional oil and gas reservoirs with low permeability, ultra-low permeability and shale gas. With the increasingly extensive application of these technologies, other correlated technologies have also gained great development. However, low-permeability reservoirs exhibit complicated features and horizontal well fractures have complex shape. The existing methods for the productivity prediction of fractured horizontal well in low-permeability reservoirs rarely consider the influencing factors in a comprehensive manner. In this paper, a horizontal well seepage model of casing fracturing completion was established according to the superposition principle of low-permeability reservoir and the relationship between potential and pressure, by which model the seepage characteristics of low-permeability reservoirs could be fully described. Based on the established new seepage model, a new targeted model with coupling seepage and wellbore flow was established for the productivity prediction of low-permeability fractured horizontal well. Finally, the new targeted model was verified through field experiment. The experimental results confirmed the reliability of productivity prediction by the proposed model. Sensitivity analysis was then performed on the parameters in the proposed model.

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Kirchner, Lisa A., Richard P. Moody, Edward Doyle, Ranjan Bose, Jamie Jeffery, and Ih Chu. "The Prediction of Skin Permeability by Using Physicochemical Data." Alternatives to Laboratory Animals 25, no. 3 (May 1997): 359–70. http://dx.doi.org/10.1177/026119299702500319.

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A database on physicochemical properties and skin permeation compiled by Health Canada was analysed by using linear regression analysis. The correlation between permeability coefficient (Kp) and the octanol–water partition coefficient (Kow) has been improved by grouping the compounds according to their respective molar volumes. Linear regression analysis of the individual groups has demonstrated a positive correlation for the majority of the groups, with the compounds in the lowest molar volume range (≤ 75Å3) having the best correlation (r2 = 0.86), and the compounds in the highest molar volume range (≥ 30lÅ3) being the least well-correlated (r2 = 0.55). Due to the diversity of the chemicals used in this analysis, and the statistically significant correlations obtained, this model could permit the prediction of skin permeation of a wide variety of chemical compounds. Although of a simplistic nature, and not yet experimentally validated, this quantitative structure-activity relationship may be useful for predicting human skin permeability coefficients for compounds that fall within the constraints of this data set.

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Lu, Zhen, Aimin Sha, and Wentong Wang. "Permeability Evaluation of Clay-quartz Mixtures Based on Low-Field NMR and Fractal Analysis." Applied Sciences 10, no. 5 (February 27, 2020): 1585. http://dx.doi.org/10.3390/app10051585.

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Nuclear magnetic resonance (NMR) technology has been widely used for predicting permeability coefficients of porous medium, such as shales, sandstones, and coals. However, there have been limited studies on the prediction model of clay-quartz mixtures based on NMR technology. In this study, evaporation tests at 40 °C and NMR tests were simultaneously performed on eight clay-quartz mixtures with different mineral compositions. The results show that during the evaporation process, the decay rate of T2 total signal amplitudes was constant at first, and then decreased to 0 after a period of time. Based on the decay rate, the evaporation process was divided into two stages: the constant rate stage and the falling rate stage. Based on the two stages, the T2 cut-offs of eight mixtures were determined. The water in the mixture was divided into two parts by the T2 cut-off: the free water and the bound water. The prediction model of permeability coefficients of clay-quartz mixtures was established based on the Timur-Coates model. In order to simplify the process of predicting the permeability coefficient, fractal analysis was used to develop the relationship between the T2 cut-off and fractal dimension of the T2 spectrum of saturated mixture. A simplified method for predicting permeability coefficients of clay-quartz mixtures based on NMR technology without centrifugal and evaporation experiments was also proposed.

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Shokir, Eissa M. "A Novel Model for Permeability Prediction in Uncored Wells." SPE Reservoir Evaluation & Engineering 9, no. 03 (June 1, 2006): 266–73. http://dx.doi.org/10.2118/87038-pa.

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Summary A fuzzy model is applied for permeability estimation in heterogeneous sandstone oil reservoirs using core porosity and gamma ray logs. The basic concepts of a fuzzy model are described, and we explain how to use the constructed model to analyze and interpret the results. The fuzzy-logic approach is used to represent a nonlinear relationship as a smooth concatenation of local linear submodels. The partitioning of the input space into fuzzy regions, represented by the individual rules, is obtained through fuzzy clustering. The results from the fuzzy model show that it is not only accurate but also provides some insight into the nonlinear relationship represented by the model. Furthermore, the results of the blind test developed a good agreement between the measured core permeability and the output of the fuzzy model. Introduction Many oil reservoirs have heterogeneity in rock properties. Understanding the form and spatial distribution of these heterogeneities is fundamental to the successful characterization of these reservoirs. Permeability is one of the fundamental rock properties, which reflects the ability to flow when subjected to applied pressure gradients. While this property is so important in reservoir engineering, there is no well log for permeability, and its determination from conventional log analysis is often unsatisfactory (Mohaghegh et al. 1997; Malki et al. 1996). Estimation of permeability in a heterogeneous reservoir is a very complex task; a poorly estimated permeability will make the model inaccurate and unreliable, thus affecting the degree of success of many oil and gas operations that are based on such models. Major efforts have been made by many researchers to establish a complex mathematical function that relates permeability to other reservoir characteristics. These studies have helped in understanding the factors controlling permeability but have not provided an accurate estimation of permeability. The internal processes of a reservoir correspond to complex physical phenomena where many factors are interacting. Definition of an exact expression for each of these factors as a function of others is an impossible task. The best that can be done is approximate methods that somehow give a hint about the permeability distribution in the reservoir (Berg 1970; Timur 1968). One of the first practices was finding correlations between permeability and other reservoir characteristics such as porosity, or well logs. Samples extracted from cored wells were used in the laboratory to find values of permeability and porosity; likewise, logs were taken in the same wells. Correlations were obtained from permeability vs. porosity plots or from functional transformation on the well logs wherever enough information existed. These correlations were extrapolated to wells in which little or no information was available. For this method to work, a high amount of reservoir-representative samples was required, something expensive to achieve. Besides, when heterogeneity of a well is high, these correlations become unreliable (Yao and Holditch 1993). Statistical multivariate techniques arise as a better choice, providing a potential solution through regression analysis. These techniques offer appealing solutions; however, their main drawback is the need to exhaustively identify all the factors affecting permeability and then establish a linear or nonlinear model that best represents interactions among such factors. Because permeability is controlled by both depositional characteristics (such as grain size and sorting) and digenetic features, a precise model should take into account the fundamentals of geology and physics of flow in porous media (Abbaszadeh et al. 1996). Relationships between core-derived pore-throat parameters and log-derived macroscopic petrophysical attributes can be established (Soto B. et al. 1999). Fuzzy logic uses the benefits of approximate reasoning. Under this type of reasoning, decisions are made on the basis of fuzzy linguistic variables such as "low," "good," and "high," with fuzzy set operators such as "and" or "or." This process simulates the human expert's reasoning process much more realistically than do conventional expert systems. Fuzzy-set theory is an efficient tool for modeling the kind of uncertainty associated with vagueness, imprecision, and/or a lack of information regarding a particular element of the problem at hand (Soto B. et al. 2001). In this paper, the fuzzy model was applied for permeability estimation in heterogeneous oil reservoirs using core porosity and gamma ray log. Also, the basic concepts of the fuzzy model are described. Finally, a method is presented for using the constructed models to analyze and interpret the results.

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Journal articles on the topic 'Prediction of permeability'

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