Relevant bibliographies by topics / Prediction of permeability

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Prediction of permeability'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Prediction of permeability"

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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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Dissertations / Theses on the topic "Prediction of permeability"

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Xie, Jiang. "Improved permeability prediction using multivariate analysis methods." [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-3223.

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Stenberg, Patric. "Computational models for the prediction of intestinal membrane permeability." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2001. http://publications.uu.se/theses/91-554-4934-4/.

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Florence, Francois-Andre. "Validation/enhancement of the "Jones-Owens" technique for the prediction of permeability in low permeability gas sands." Texas A&M University, 2003. http://hdl.handle.net/1969.1/5846.

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This work presents the validation and enhancement of existing correlations for estimating and predicting the permeability in low permeability gas sands. The "original" problem of predicting the corrected or "liquid equivalent" permeability has been under investigation since the early 1940s — in particular, using the application of "gas slippage" theory to petrophysics by Klinkenberg. In the first part of this work, the viability of the Jones-Owens and Sampath-Keighin correlations for estimating the Klinkenberg-corrected (absolute) permeability from single-point, steady-state measurements were investigated. We also provide an update to these correlations using modern petrophysical data. In the second part of this work we proposed and validated a new "microflow" model for the evaluation of an equivalent liquid permeability from gas flow measurements. This work was based on a more detailed application of similar concepts employed by Klinkenberg. In fact, we obtained the Klinkenberg result as an approximate form of this result. A theoretical "microflow" result was given as a rational polynomial (i.e., a polynomial divided by a polynomial) in terms of the Knudsen number (ratio of the mean free path of the gas molecules to the characteristic flow length (typically the radius of the capillary)), and this result can be applied as an explicit correlation device, or as an implicit prediction model (presuming the model is tuned to a particular data set). The following contributions are derived from this work: ● Validation and extension of the correlations proposed by Jones-Owens and Sampath-Keighin for low permeability samples. ● Development and validation of a new "microflow" model which correctly represents the flow of gases in low permeability core samples. This model is also applied as a correlation for prediction of the equivalent liquid permeability in much the same fashion as the Klinkenberg model, although the new model is substantially more theoretical (and robust) as compared to the Klinkenberg correction model.
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Wu, Tao. "Permeability prediction and drainage capillary pressure simulation in sandstone reservoirs." Texas A&M University, 2004. http://hdl.handle.net/1969.1/1496.

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Knowledge of reservoir porosity, permeability, and capillary pressure is essential to exploration and production of hydrocarbons. Although porosity can be interpreted fairly accurately from well logs, permeability and capillary pressure must be measured from core. Estimating permeability and capillary pressure from well logs would be valuable where cores are unavailable. This study is to correlate permeability with porosity to predict permeability and capillary pressures. Relationships between permeability to porosity can be complicated by diagenetic processes like compaction, cementation, dissolution, and occurrence of clay minerals. These diagenetic alterations can reduce total porosity, and more importantly, reduce effective porosity available for fluid flow. To better predict permeability, effective porosity needs to be estimated. A general equation is proposed to estimate effective porosity. Permeability is predicted from effective porosity by empirical and theoretical equations. A new capillary pressure model is proposed. It is based on previous study, and largely empirical. It is tested with over 200 samples covering a wide range of lithology (clean sandstone, shaly sandstone, and carbonates dominated by intergranular pores). Parameters in this model include: interfacial tension, contact angle, shape factor, porosity, permeability, irreducible water saturation, and displacement pressure. These parameters can be measured from routine core analysis, estimated from well log, and assumed. An empirical equation is proposed to calculate displacement pressure from porosity and permeability. The new capillary-pressure model is applied to evaluate sealing capacity of seals, calculate transition zone thickness and saturation above free water level in reservoirs. Good results are achieved through integration of well log data, production data, core, and geological concepts.
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Pereira, Janaina Luiza Lobato. "Permeability prediction from well log data using multiple regression analysis." Morgantown, W. Va. : [West Virginia University Libraries], 2004. https://etd.wvu.edu/etd/controller.jsp?moduleName=documentdata&jsp%5FetdId=3368.

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Thesis (M.S.)--West Virginia University, 2004.
Title from document title page. Document formatted into pages; contains xiii, 82 p. : ill. (some col.), maps. Vita. Includes abstract. Includes bibliographical references (p. 41).
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Hahn, Christoph [Verfasser]. "A Simulation Approach of Permeability Prediction for RTM Process Simulation / Christoph Hahn." München : Verlag Dr. Hut, 2015. http://d-nb.info/1067708391/34.

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Xu, Jianlong. "Prediction of gas permeability in composite laminates using three-dimensional finite elements." [Gainesville, Fla.] : University of Florida, 2007. http://purl.fcla.edu/fcla/etd/UFE0021260.

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Ball, Leslie Derek. "Permeability prediction in a fluvial reservoir : the PUC-B sandstone, Sirt Basin, Libya." Thesis, Heriot-Watt University, 1997. http://hdl.handle.net/10399/1293.

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Goswami, Tarun. "Sublingual drug delivery: In vitro characterization of barrier properties and prediction of permeability." Scholarly Commons, 2008. https://scholarlycommons.pacific.edu/uop_etds/2370.

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Sublingual administration of drugs offers advantages including avoidance of first pass metabolism and quick absorption into the systemic circulation. In spite of being one of the oldest routes of drug delivery, there is dearth of literature on characterization of the barrier properties of the sublingual mucosa. Therefore, the aim of this research was to gain an insight into the barrier properties of the porcine sublingual mucosa. The studies conducted in this dissertation research focused on an important aspect of sublingual permeation, the dependence of permeability on different physicochemical properties of the permeant such as the degree of ionization, distribution coefficient and molecular weight/size on drug transport across sublingual mucosa. Further the data from the sublingual permeation of model compounds was used in development of a predictive model which provided us with some understanding regarding the important descriptors required for sublingual drug delivery. A series of β-blockers were employed as the model drugs to study the dependence of permeability on lipophilicity across the sublingual mucosa. Eighth different β-blockers with log D (distribution coefficient) values ranging from -1.30 to 1.37 were used in this study. The most hydrophilic drug atenolol showed the lowest permeability (0.19 ± 0.04 x 10 -6 ) cm/sec and the most lipophilic drug propranolol showed the highest permeability (38.25 ± 4.30 x 10 -6 ) cm/sec. The log-log plot of permeability coefficient and the distribution coefficient showed a linear relationship. It was concluded that the increase in lipophilicity results in improved partitioning across the lipid bilayers of sublingual mucosa which results in increased permeation for the drugs. As the sublingual mucosa contains a significant amount of the polar lipids bonded with water molecules, therefore, it was hypothesized that the hydrophilic or ionized permeants will have significant permeation across the sublingual mucosa. The objective of this research was to study the effect of ionization on permeation across sublingual mucosa using a model drug nimesulide. Based on the relationship between the permeability coefficient and distribution coefficient of nimesulide at different pH, the lipoidal route was suggested as the dominant transport route for nimesulide across the sublingual mucosa. The contribution of individual ionic species of nimesulide to the total drug flux was quantitatively delineated. It was observed that the ionized species of nimesulide contributes significantly to the total flux across the sublingual mucosa. The contribution of the ionized species to total flux was almost (90%) at a pH where the drug was almost completely ionized. Polyethylene glycols (PEGs) were used as the model permeants to study the dependence of permeability on molecular weight. An inverse relationship between molecular weight and permeability coefficients was observed. This relationship was used to estimate the molecular weight cut off for the sublingual mucosa. The molecular weight cut off was estimated to be around 1675 daltons. Further, the Renkin function was used to estimate the theoretical pore size of the sublingual mucosa and the pore size of the sublingual mucosa was estimated to be around 30–53 Å based on two separate calculations using the radius of gyration and Stokes-Einstein radius for PEG molecules, respectively. No specific model is present in literature to predict the in vitro sublingual drug permeability. In this dissertation a specific model was developed and validated by performing permeation studies of 14 small molecules across the porcine sublingual mucosa. It was shown that the lipophilicity (logD 6.8 ) and the number of hydrogen bond donors (HBD) were the most significant descriptors affecting sublingual permeability. Research conducted in this dissertation provided an in-depth understanding about the barrier properties of the porcine sublingual mucosa and role of different physicochemical properties on sublingual transport. Such an understanding will hopefully expand the suitable lead candidates for sublingual delivery.
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Goswani, Tarun. "Sublingual drug delivery : in vitro characterization of barrier properties and prediction of permeability." Scholarly Commons, 2008. https://scholarlycommons.pacific.edu/uop_etds/708.

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Books on the topic "Prediction of permeability"

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Miller, Nadia C. Predicting flow characteristics of a lixiviant in a fractured crystalline rock mass. Washington, D.C: Bureau of Mines, United States Department of Interior, 1993.

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Vidstrand, Patrik. Hydrogeological scale effects in crystalline rocks: Comparison of field data from Äspö HRL with data from predictive upscaling methods. Göteborg, Sweden: Dept. of Geology, Chalmers University of Technology, 1999.

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Vepraskas, Michael J. Predicting contaminant transport along veins and fractures in saprolite above the water table. Raleigh, N.C: [Water Resources Research Institute of the University of North Carolina, 1995.

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A, Thomas M. D., Stanish K, United States. Federal Highway Administration. Office of Infrastructure Research and Development., Turner-Fairbank Highway Research Center, and University of Toronto. Dept. of Civil Engineering., eds. Prediction of chloride penetration in concrete. McLean, Va: U.S. Dept. of Transportation, Federal Highway Administration, Turner-Fairbank Highway Research Center, 2001.

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Kenny, Jerry F. Measurement and prediction of tillage effects on hydraulic and thermal properties of Palouse silt loam soil. 1990.

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Volume fracturing effect evaluation and productivity prediction of vertical wells in low-permeability tight oil reservoirs. ausasia science and technology press pty ltd, 2020. http://dx.doi.org/10.26804/2020112502.

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Turner, Neil, and Stewart Cameron. Proteinuria. Edited by Neil Turner. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199592548.003.0050.

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Excess protein in the urine almost always comes from the kidney. Proteinuria up to 150 mg/day in an adult (protein:creatinine ratio (PCR) up to 15 mg/mmol) is considered normal. Daily average excretion is 80 mg, of which about 30 mg is albumin that has been filtered and not reabsorbed. Other components comprise low-molecular-weight filtered proteins that have escaped reabsorption, and proteins secreted or lost into urine from cells of the nephron. Increased permeability of the glomerulus to high-molecular-weight proteins is the most common cause of the clinically detected proteinuria, and albumin is the major component of excess glomerular proteinuria. Even small amounts of proteinuria are associated with increased cardiovascular risk and long-term renal risk. In patients with renal disease, regardless of type, proteinuria is a strong predictor of loss of glomerular filtration rate and proteinuria at levels higher than an equivalent of 1 g/24 hours can be considered high renal risk. This limit should be lowered in young patients, and if microscopic haematuria is also present. For both cardiovascular and renal outcomes, risk is graded with severity of proteinuria. In routine clinical practice, ratios of albumin or total protein to creatinine level (ACR or PCR) in spot urine samples are usually more pragmatic and useful than 24-hour collections. ACR is more sensitive as a screening test (normal range up to 2.5 mg/mmol in men, 3.5 mg/mmol in women).
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Book chapters on the topic "Prediction of permeability"

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Ferreira, Ricardo J. "In Silico Prediction of Permeability Coefficients." In Methods in Molecular Biology, 243–61. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1468-6_14.

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Abbott, N. Joan, Andreas Reichel, Mansoor Chishty, Kevin D. Read, Janet A. Taylor, and David J. Begley. "Measurement and Prediction of Blood-Brain Barrier Permeability." In Blood—Brain Barrier, 27–44. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-0579-2_4.

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Raevsky, O. A., E. P. Trepalina, and S. V. Trepalin. "SLIPPER — A New Program for Water Solubility, Lipophilicity and Permeability Prediction." In Molecular Modeling and Prediction of Bioactivity, 489–90. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4141-7_131.

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Kansy, Manfred, Krystyna Kratzat, Isabelle Parrilla, Frank Senner, and Björn Wagner. "Physicochemical High Throughput Screening (pC-HTS): Determination of Membrane Permeability, Partitioning and Solubility." In Molecular Modeling and Prediction of Bioactivity, 237–43. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4141-7_28.

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Pape, H., J. Arnold, R. Pechnig, C. Clauser, E. Talnishnikh, S. Anferova, and B. Blümich. "Permeability Prediction for Low Porosity Rocks by Mobile NMR." In Rock Physics and Natural Hazards, 1125–63. Basel: Birkhäuser Basel, 2009. http://dx.doi.org/10.1007/978-3-0346-0122-1_18.

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Huang, Y., P. M. Wong, and T. D. Gedeon. "Permeability Prediction in Petroleum Reservoir using a Hybrid System." In Soft Computing in Industrial Applications, 437–46. London: Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0509-1_38.

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Karlén, Anders, Susanne Winiwarter, Nicholas Bonham, Hans Lennernäs, and Anders Hallberg. "Correlation of Intestinal Drug Permeability in Humans (In Vivo) with Experimentally and Theoretically Derived Parameters." In Molecular Modeling and Prediction of Bioactivity, 491–92. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4141-7_132.

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Ja’fari, Ahmad, and Rasoul Hamidzadeh Moghadam. "Integration of Fuzzy Systems and Genetic Algorithm in Permeability Prediction." In Advances in Computational Intelligence, 287–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38682-4_32.

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Kiauka, M., I. Kolodiazhnyj, and A. Borovkov. "Numerical Prediction of the Permeability Tensor Components for 2D Woven." In Proceedings of MEACM 2020, 331–43. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67958-3_35.

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Kudaikulov, Aziz, Christophe Josserand, and Aidarkhan Kaltayev. "Theoretical and Numerical Prediction of the Permeability of Fibrous Porous Media." In Communications in Computer and Information Science, 85–93. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25058-8_9.

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Conference papers on the topic "Prediction of permeability"

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Florence, Francois Andre, Jay Rushing, Kent Edward Newsham, and Thomas Alwin Blasingame. "Improved Permeability Prediction Relations for Low Permeability Sands." In Rocky Mountain Oil & Gas Technology Symposium. Society of Petroleum Engineers, 2007. http://dx.doi.org/10.2118/107954-ms.

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Bryant, Steven, and Christopher Cade. "Permeability Prediction from Geological Models." In ECMOR III - 3rd European Conference on the Mathematics of Oil Recovery. European Association of Geoscientists & Engineers, 1992. http://dx.doi.org/10.3997/2214-4609.201411076.

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M. Malik, Qamar, Brigida R.P. da Rocha, Robert Marsden, and Michael S. King. "Permeability Prediction from Electromagnetic Measurements." In 5th International Congress of the Brazilian Geophysical Society. European Association of Geoscientists & Engineers, 1997. http://dx.doi.org/10.3997/2214-4609-pdb.299.253.

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Dougherty, Elmer L., Fahmi Juber, and Jincai Chang. "Prediction and Analysis of Performance of Gas Wells Producing from Reservoirs Containing Several Noncommunicating Layers." In Low Permeability Reservoirs Symposium. Society of Petroleum Engineers, 1993. http://dx.doi.org/10.2118/25908-ms.

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Smith, L. K., D. B. MacGowan, and R. C. Surdam. "Scale Prediction During CO2 Huff 'n' Puff Enhanced Recovery, Crooks Gap Field, Wyoming." In Low Permeability Reservoirs Symposium. Society of Petroleum Engineers, 1991. http://dx.doi.org/10.2118/21838-ms.

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Advani, S. H., H. Khattab, and J. K. Lee. "Hydraulic Fracture Geometry Modeling, Prediction, and Comparisons." In SPE/DOE Low Permeability Gas Reservoirs Symposium. Society of Petroleum Engineers, 1985. http://dx.doi.org/10.2118/13863-ms.

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Susilo, Agus. "Permeability Prediction Based on Capillary Model." In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers, 2010. http://dx.doi.org/10.2118/141122-stu.

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Xu, S., and M. S. King. "Pore structure mapping and permeability prediction prediction from velocity measurements." In EAGE/SEG Research Workshop 1990. European Association of Geoscientists & Engineers, 1990. http://dx.doi.org/10.3997/2214-4609.201411898.

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Altunbay, M., D. Georgi, and H. M. Takezaki. "Permeability Prediction for Carbonates: Still a Challenge?" In Middle East Oil Show and Conference. Society of Petroleum Engineers, 1997. http://dx.doi.org/10.2118/37753-ms.

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Maslennikova, Yulia. "Permeability Prediction Using Hybrid Neural Network Modelling." In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers, 2013. http://dx.doi.org/10.2118/167640-stu.

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Reports on the topic "Prediction of permeability"

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Schlueter, E. M. Predicting the permeability of sedimentary rocks from microstructure. Office of Scientific and Technical Information (OSTI), January 1995. http://dx.doi.org/10.2172/70737.

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Smith, M. M., Y. Hao, L. H. Spangler, K. Lammers, and S. A. Carroll. Validation of a reactive transport model for predicting porosity and permeability evolution in carbonate core samples. Office of Scientific and Technical Information (OSTI), October 2018. http://dx.doi.org/10.2172/1579604.

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Reinaldo Gonzalez, Scott R. Reeves, and Eric Eslinger. Predicting Porosity and Permeability for the Canyon Formation, SACROC Unit (Kelly-Snyder Field), Using the Geologic Analysis via Maximum Likelihood System. Office of Scientific and Technical Information (OSTI), September 2007. http://dx.doi.org/10.2172/926646.

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