Modeling of Injection-Induced Particle Movement Leading to the Development of Thief Zones

2021 ◽  
Author(s):  
Qianru Qi ◽  
Iraj Ershaghi
Keyword(s):  
Grain Size ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Size Exclusion ◽  
Published Data ◽  
Particle Movement ◽  
Grain Sizes ◽  
Modeling Approach

Abstract This paper is a contribution to failure prediction of unconsolidated intervals that could have a negative impact on injection efficiency because of susceptibility to structural changes under fluid injection processes. In unconsolidated formations, formation fines may be subjected to drag forces by injected water because of poor cementation. This results in small grain moments, and continuation can result in a gradual increase in permeability and eventual development of washed-out or thief zones. This paper presents a new modeling approach using information from profile surveys and grain and pore size distribution to model the process of injection and the induced particle movement. The motivation came from field observations and realization of permeability increase from profile surveys and substantial fines movement, leading to an increase in rock permeability. A series of case studies based on realistic published data on pore and grain size distribution are included to demonstrate the estimated increases in formation permeability. In our modeling approach, once we establish the range of grain sizes that fits the criterion for particle movement, a probabilistic algorithm, developed for the study, is applied to track changes in porosity and associated variations in permeability. This algorithm, presented for the first time, considers a stochastic approach to monitor the reservoir particle movements, pore size exclusion by particle accumulation and their resultant changes in rock properties. For this methodology, we ignored potential effects of wettability and clay swelling, and considered perfect spheres to represent the various grain sizes. Predictions made using various realizations of channel formation and petrophysical alterations show the significance of having access to three sources of information; pore size distribution, grain size distribution, and profile surveys. Through inverse modeling using these pieces of information for a particular formation, we demonstrate how we can predict realistic changes and map rock transport properties.

Error propagation in the estimation of glomerular pressure from macromolecule sieving data

1995 ◽  
Vol 268 (4) ◽  
pp. F736-F745 ◽  
Author(s):  
A. Edwards ◽  
W. M. Deen
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Experimental Studies ◽  
Systematic Errors ◽  
Published Data ◽  
Hydraulic Pressure ◽  
Random Errors ◽  
True Value ◽  
Experimental Errors

The theoretical effects of the glomerular transmural hydraulic pressure difference (delta P) on the sieving coefficients (theta i) of macromolecules of varying size have led to a number of attempts to use sieving curves to estimate delta P noninvasively, with inconsistent results. The objective of this study was to determine the extent to which experimental errors and imperfections in the theoretical models limit the ability to obtain reliable estimates of delta P using this method. Our approach was to generate many sets of synthetic “experimental data” using computer simulations of glomerular sieving and to compute values of delta P by fitting models to those data in the presence of various types and magnitudes of errors. Unbiased experimental errors were simulated by adding random amounts to individual values of theta i, and systematic errors were investigated by using a model based on one type of pore-size distribution to fit “data” generated using a model of a different type. We found that with random errors in theta i only, the estimate of delta P was accurate to within +/- 4 mmHg nearly all of the time, provided that the standard deviation, sigma i, was < or = 5% of theta i. When there were also systematic errors arising from the use of an “incorrect” form of pore-size distribution, a useful predictor of success was the probability P that the residuals, the differences between the measured and predicted sieving coefficients, were randomly distributed. A value of P > 0.2, as calculated from the algebraic signs of the residuals, indicated a high likelihood that the pressure estimate was accurate, provided that the random errors were sufficiently small. When P > 0.2, the fitted value of delta P was within +/- 4 mmHg of the true value in about 90%, 80%, and 70% of the cases examined when sigma i was < or = 2%, 5%, or 10% of theta i, respectively. An analysis of published data from a number of experimental studies indicated, however, that the favorable conditions of small sigma i and large P are extremely difficult to achieve, making it unlikely that an accurate group-mean value of delta P will be estimated from any given set of sieving data. Significant experimental and theoretical advances will be needed to make this a reliable method for estimating glomerular pressure.

scholarly journals Modified Gompertz sigmoidal model removing fine-ending of grain-size distribution

2019 ◽  
Vol 11 (1) ◽  
pp. 29-36 ◽  
Author(s):  
Rui Yuan ◽  
Bo Yang ◽  
Yingfei Liu ◽  
Lingyu Huang
Keyword(s):  
Grain Size ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Cumulative Probability ◽  
Second Stage ◽  
Grain Sizes ◽  
Third Stage ◽  
Moments Method ◽  
Sigmoidal Model ◽  
Parameters Calculation

Abstract Because of the laboratory operating, the fineending of grain-size distribution (GSD) are simply combined as one point, which results in the information loss of the fine and very-fine clastic particles, and affects the geological parameters calculation of GSD. To remove the fine-endings, a modified Gompertz sigmoidal model is proposed in this paper. The first stage is establishing and solving the modified Gompertz sigmoidal model; the second stage is fitting and evaluating the cumulative probability and frequency of GSD; the third stage is calculating the geological parameters. Taking 113 samples for example, coefficients of determination (COD) between measured and fitted individual cumulative probability and frequency are bigger than 0.98980 and 0.97000 respectively, which proves the goodness of fitting results. By moments method using frequency data, the COD between fitted and measured mean is 0.97578, while CODs of sorting, skewness and kurtosis are in low values, which suggest that the fine-endings has little influence on the average grain-sizes of GSD and large influence on its geometry. Besides, modified Gompertz sigmoidal model offers another quick numerical way to calculate median, mean and sorting of GSD by graphical method using cumulative probability data. The proposed method is useful to remove the fine-endings and contribute to calculate the geological parameters of GDS.

Effects of Processing Factors on Microstructure and Diffusivity of Diethylbenzene Dehydrogenation Catalyst

2010 ◽  
Vol 297-301 ◽  
pp. 233-238
Author(s):  
Mohammad Ebrahim Zeynali ◽  
I. Soltani
Keyword(s):  
Grain Size ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Bulk Diffusion ◽  
Effective Diffusivity ◽  
Catalyst Pellet ◽  
Different Types ◽  
Effective Diffusivities ◽  
Processing Factors

In this study, different mechanisms of diffusion such as Knudsen and bulk were investigated for diethylbenzene diffusion into a catalyst and it was concluded that the pore sizes should be in the range that permit transitional diffusion (both Knudsen and bulk diffusion). The catalyst grain size can be controlled and varied by different parameters such as speed and time of mixing, type of alkali, temperature and pH. Particle size distribution experiments were conducted for different types of alkali and speed of mixing to characterize the catalyst. The effects of grain size formed during coprecipitation on pore size distribution of the catalyst pellet which affect the effective diffusivity were discussed. Pore size distribution of the model catalyst was obtained and the effective diffusivities were calculated by numerical integration of Johanson-Stewart equation.

The Inverse Hall-Petch Effect—Fact or Artifact?

2000 ◽  
Vol 634 ◽  
Author(s):  
Carl C. Koch ◽  
J. Narayan
Keyword(s):  
Grain Size ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Computer Simulations ◽  
Nanocrystalline Materials ◽  
Grain Sizes ◽  
Average Grain Size

ABSTRACTThis paper critically reviews the data in the literature which gives softening—the inverse Hall-Petch effect—at the finest nanoscale grain sizes. The difficulties with obtaining artifactfree samples of nanocrystalline materials will be discussed along with the problems of measurement of the average grain size distribution. Computer simulations which predict the inverse Hall-Petch effect are also noted as well as the models which have been proposed for the effect. It is concluded that while only a few of the experiments which have reported the inverse Hall-Petch effect are free from obvious or possible artifacts, these few along with the predictions of computer simulations suggest it is real. However, it seems that it should only be observed for grain sizes less than about 10 nm.

Topology Based Growth Law and New Analytical Grain Size Distribution Function of 3D Grain Growth

2007 ◽  
Vol 558-559 ◽  
pp. 1183-1188 ◽  
Author(s):  
Peter Streitenberger ◽  
Dana Zöllner
Keyword(s):  
Grain Size ◽  
Distribution Function ◽  
Size Distribution ◽  
Grain Growth ◽  
Grain Size Distribution ◽  
Three Dimensional ◽  
Size Distribution Function ◽  
Change Rate ◽  
Grain Sizes ◽  
Self Similar

Based on topological considerations and results of Monte Carlo Potts model simulations of three-dimensional normal grain growth it is shown that, contrary to Hillert’s assumption, the average self-similar volume change rate is a non-linear function of the relative grain size, which in the range of observed grain sizes can be approximated by a quadratic polynomial. In particular, based on an adequate modification of the effective growth law, a new analytical grain size distribution function is derived, which yields an excellent representation of the simulated grain size distribution.

The Influence of Microstructures on the Thermal Conductivity of Polycrystalline UO2

2016 ◽  
Author(s):  
Enze Jin ◽  
Chen Liu ◽  
Heming He
Keyword(s):  
Thermal Conductivity ◽  
Finite Element Method ◽  
Grain Size ◽  
Finite Element ◽  
Pore Size ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Structural Changes ◽  
Property Changes ◽  
Design Guidance

The thermal conductivity is one of the most important properties for UO2. The influences of microstructure are especially important for UO2 due to the severe structural changes under irradiation conditions. In this study, we have investigated the thermal conductivity of UO2 with different microstructures using Finite Element Method. The thermal conductivity increases with increasing grain size. The grain size distribution has obvious influence on the thermal conductivity especially when there are pores in the polycrystal. The influences of porosity and pore size are very sensitive to the position of the pores. The results obtained in this study are useful for prediction of property changes of UO2 fuel in pile and important to gain some design guidance to tune the properties through the control of the microstructure.

Statistical analysis of the parameters and grain size distribution functions of single-phase polycrystalline materials

2020 ◽  
Vol 86 (4) ◽  
pp. 39-45
Author(s):  
S. I. Arkhangelskiy ◽  
D. M. Levin
Keyword(s):  
Grain Size ◽  
Distribution Function ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Single Phase ◽  
Distribution Functions ◽  
Grain Structure ◽  
Polycrystalline Materials ◽  
Grain Sizes ◽  
Average Grain Size

A statistical analysis of the grain size distribution is important both for developing theories of the grain growth and microstructure formation, and for describing the size dependences of various characteristics of the physical and mechanical properties of polycrystalline materials. The grain size distribution is also an important characteristic of the structure uniformity and, therefore, stability of the properties of the products during operation. Statistical Monte Carlo modeling of single-phase and equiaxed polycrystalline microstructures was carried out to determine the type of statistically valid distribution function and reliable estimates of the average grain size. Statistical parameters (mean values, variances, variation coefficient) and distribution functions of the characteristics of the grain microstructure were obtained. It is shown that the distribution function of the effective grain sizes for the studied polycrystal model is most adequately described by γ-distribution, which is recommended to be used in analysis of the experimental distribution functions of grain sizes of single-phase polycrystalline materials with equiaxed grains. The general average (mathematical expectation) of the effective grain size (projection diameter) with γ-distribution function (parameters of the distribution function are to be previously determined in analysis of the grain structure of polycrystalline materials) should be taken as a statistically valid and reliable estimate of the average grain size. The results of statistical modeling are proved by the experimental data of metallographic study of the microstructures of single-phase model and industrial materials with different degree of the grain structure heterogeneity.

A Realistic Method to Describe the Role of Diffusion in Catalyst Design

2009 ◽  
Vol 294 ◽  
pp. 65-76
Author(s):  
Mohammad Ebrahim Zeynali
Keyword(s):  
Grain Size ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Nitrate Solution ◽  
Effective Diffusivity ◽  
Catalyst Design ◽  
Internal Surface Area ◽  
Co Precipitation

The dehydrogenation of diethylbenzene to divinylbenzene is a catalytic reaction. The catalyst for the dehydrogenation was prepared by co-precipitation of iron and chromium hydroxide from nitrate solution, followed by doping with potassium carbonate and drying. To make available the internal surface area of the catalyst for the reactant, the pores must be of the proper sizes to allow the reactant to diffuse and penetrate inside the catalyst pellets. The prepared catalyst was considered as a model for investigating the role of diffusion in catalyst design. In this study, different mechanisms of diffusion, such as Knudsen and bulk, were investigated for the case of diethylbenzene diffusion into the catalyst and it was concluded that the pore sizes should be in a range that permits transitional diffusion (both Knudsen and bulk diffusion). The catalyst grain size can be controlled and varied by acting on parameters such as the speed and time of mixing, type of alkali, temperature and pH. Particle size distribution experiments were conducted for different types of alkali and speeds of mixing in order to characterize the catalyst. The effects of the grain size, formed during co-precipitation, upon the pore size distribution of the catalyst pellet which affects the effective diffusivity were discussed. The pore size distribution of the model catalyst was obtained and the effective diffusivities were calculated by numerical integration of the Johanson-Stewart equation.

On the Grain Size Distribution of MnO Doped ZnO Ceramics

2007 ◽  
Vol 336-338 ◽  
pp. 2361-2362
Author(s):  
Shu Ai Li ◽  
Da Nian Liu ◽  
Jiang Hong Gong
Keyword(s):  
Grain Size ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Sintering Temperature ◽  
Grain Morphology ◽  
Holding Time ◽  
Grain Sizes ◽  
Different Temperatures ◽  
Doped Zno ◽  
Zno Ceramics

A series of MnO-doped ZnO with different grain sizes and grain morphologies were prepared by sintering the samples at different temperatures for different holding times. The grain size distribution for each sample was determined. It was found that, although the grain size increases and the grain morphology varies with the sintering temperature and/or the holding time, the normalized grain size distribution keeps invariable.

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