Use of Analytical Models and Monte-Carlo Simulation for Quantification of Uncertainties Associated With Gas Production From Hydrate-Capped Gas Reservoirs

2008 ◽  
Author(s):  
Shahab Gerami ◽  
Mehran Pooladi-Darvish
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Gas Production ◽  
Analytical Models ◽  
Gas Reservoirs

Monte Carlo Simulation of Self-Absorption Effects in Elemental XRF Analysis of Atmospheric Particulates Collected on Filters

1975 ◽  
Vol 19 ◽  
pp. 323-337 ◽  
Author(s):  
A. R. Hawthorne ◽  
R. P. Gardner ◽  
T. G. Dzubay
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Size Range ◽  
Thick Layer ◽  
Thin Layers ◽  
Analytical Models ◽  
Enhancement Effect ◽  
X Rays ◽  
Atmospheric Particulates ◽  
Cylinders And Spheres

Monte Carlo simulation is used to determine the effects of selfabsorption for the low energy X-rays of light elements in the size range front 1 to 20 μm. Calculations are performed for a wide angle Fe-55 radioisotope-excited energy dispersive XRF system. Results are obtained for sulfur attenuation in thin layers, long cylinders, and spheres composed of various matrix materials. The enhancement effect is also treated for the transition region between thin and thick layer samples as well as in spheres of various sizes. Results are also comrpared to fixed angle analytical models.

Monte Carlo simulation of fluorescence correlation spectroscopy data

2011 ◽  
Vol 76 (3) ◽  
pp. 207-222 ◽  
Author(s):  
Peter Košovan ◽  
Filip Uhlík ◽  
Jitka Kuldová ◽  
Miroslav Štěpánek ◽  
Zuzana Limpouchová ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Diffusion Coefficients ◽  
Fluorescence Correlation Spectroscopy ◽  
Rotational Diffusion ◽  
Correlation Spectroscopy ◽  
Analytical Models ◽  
Spherical Particles ◽  
Fluorescence Signal ◽  
Fluorescence Correlation

We employed the Monte Carlo simulation methodology to emulate the diffusion of fluorescently labeled particles and understand the source of differences between values of diffusion coefficients (and consequently hydrodynamic radii) of fluorescently labeled nanoparticles measured by fluorescence correlation spectroscopy (FCS) and dynamic light scattering (DLS). We used the simulation program developed in our laboratory and studied the diffusion of spherical particles of different sizes, which are labeled on their surface. In this study, we focused on two complicating effects: (i) multiple labeling and (ii) rotational diffusion which affect the fluorescence signal from large particles and hinder the analysis of autocorrelation functions according to simple analytical models. We have shown that the fluorescence fluctuations can be well fitted using the analytical model for small point-like particles, but the obtained parameters deviate in some cases significantly from the real ones. It means that the current data treatment yields apparent values of diffusion coefficients and other parameters only and the interpretation of experimental results for systems of particles with sizes comparable to the size of the active illuminated volume requires great care and precaution.

Probabilistic Estimation of Shale Gas Reserves Implementing Fast Marching Method and Monte Carlo Simulation

2016 ◽  
Author(s):  
Jaejun Kim ◽  
Joe M. Kang ◽  
Yongjun Park ◽  
Seojin Lim ◽  
Changhyup Park ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Shale Gas ◽  
Computing Time ◽  
Gas Production ◽  
Full Scale ◽  
Fast Marching Method ◽  
Fast Marching ◽  
Gas Recovery ◽  
Marching Method

This paper evaluates the estimated ultimate recovery for 10-year operation at a shale gas reservoir, implementing FMM (Fast Marching Method) as a surrogate model of full-scale numerical simulation and Monte Carlo simulation as a tool for accessing the uncertainty of FMM-based proxy parameters. Sensitivity analysis shows the significant properties affecting the gas recovery that are enhanced permeability, matrix permeability, and porosity in sequence. Using the statistical distributions of these parameters, this study determines P10, P50, and P90 of the 10-year cumulative gas production and compares them with the values from full-physics simulations. The computing time based on the proxy model is much smaller than that of the full-scale simulations while the prediction accuracy is acceptable. FMM can forecast the production profiles reliably without time-consuming simulation and the integration of Monte-Carlo simulation is able to evaluate the uncertainty of gas recovery, quantitatively.

scholarly journals An integrated workflow to perform reservoir and completion parametric study on a shale gas reservoir

2020 ◽  
Vol 10 (4) ◽  
pp. 1497-1510
Author(s):  
Mohamed Mahmoud ◽  
Ahmed Aleid ◽  
Abdulwahab Ali ◽  
Muhammad Shahzad Kamal
Keyword(s):  
Shale Gas ◽  
Gas Production ◽  
Production Performance ◽  
Analytical Models ◽  
Well Performance ◽  
Short Term ◽  
Gas Reservoirs ◽  
Shale Gas Reservoirs ◽  
The Impact

AbstractThe main objectives of this paper are to assess the long-term and short-term production based on both reservoir parameters and completion parameters of shale gas reservoirs. The effects of the reservoir parameters (permeability and the initial reservoir pressure) and completion parameters (fracture geometry, stimulated reservoir volume, etc.) on the short-term and long-term production of shale gas reservoirs were investigated. The currently used approach relies mainly on the decline curve analysis or analogs from a similar shale play to forecast the gas production from shale gas reservoirs. Both these approaches are not satisfactory because they are calibrated on short production history and do not assess the impact of uncertainty in reservoir and well data. For the first time, this study integrates initial production analysis, probabilistic evaluation, and sensitivity analysis to develop a robust workflow that will help in designing a sustainable production from shale gas plays. The reservoir and completion parameters were collected from different available resources, and the probability distributions of gathered uncertain data were defined. Then analytical models were used to forecast the production. Two well evaluation results are presented in this paper. Based on the results, completion parameters affected the short-term and long-term production, while the reservoir parameters controlled the long-term production. Long-term well performance was mainly controlled by the fracture half-length and fracture height, whereas other completion and reservoir parameters have an insignificant effect. Stimulation treatment design defines the initial well performance, while well placement decision defines well long-term performance. The findings of this study would help in better understanding the production performance of shale gas reservoirs, maximizing production by selecting effective completion parameters and considering the governing reservoir parameters. Moreover, it would help in accomplishing more effective stimulation treatments and define the potentiality of the basin.

scholarly journals A Genetic Algorithm Integrated with Monte Carlo Simulation for the Field Layout Design Problem

2018 ◽  
Vol 73 ◽  
pp. 24 ◽  
Author(s):  
Leonardo de Pádua Agripa Sales ◽  
Anselmo Ramalho Pitombeira-Neto ◽  
Bruno de Athayde Prata
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Design Problem ◽  
Oil And Gas ◽  
Probabilistic Approach ◽  
Layout Design ◽  
Gas Production ◽  
Oil And Gas Production ◽  
Field Development

Oil and gas production is moving deeper and further offshore as energy companies seek new sources, making the field layout design problem even more important. Although many optimization models are presented in the revised literature, they do not properly consider the uncertainties in well deliverability. This paper aims at presenting a Monte Carlo simulation integrated with a genetic algorithm that addresses this stochastic nature of the problem. Based on the results obtained, we conclude that the probabilistic approach brings new important perspectives to the field development engineering.

Monte-Carlo Simulation of Generation- Recombination Noise in Amorphous Semiconductors

2002 ◽  
Vol 715 ◽  
Author(s):  
R.I. Badran ◽  
C. Main ◽  
S. Reynolds
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Noise Spectrum ◽  
Amorphous Semiconductors ◽  
Analytical Models ◽  
Release Time ◽  
Trapping Time ◽  
Extended States ◽  
Gap States ◽  
Analytical Approaches

AbstractWe compare the predictions of several analytical models for conductivity fluctuations in a homogeneous semiconductor containing discrete and distributed traps, using a Monte-Carlo simulation of the relevant multi – trapping (MT) transitions. The simulation directly embodies the statistical features associated with such processes, in a simple ‘model - independent’ approach, free of approximations and assumptions. We compare the results with those of several analytical approaches. In one, the noise spectrum is assumed to reflect separately, the characteristic individual release time constants of the various trapping centers in the material. In another, the trapping time into the ensemble of electron traps is taken to be the dominant time constant, and hence, in a material such as a-Si:H, where the trapping time into tail sates is of order 1ps, this is taken to imply that this component of the conductivity noise spectrum is unobservable in practice. Our own analytical approach, incorporates coupling (albeit weak) between traps, which necessarily communicate via the extended states. Preliminary results of the simulation support our thesis, and verify that the same information is contained in the real part of the modulated photoconductivity (MPC) spectrum. A ‘full Monte’ – Carlo simulation incorporating all gap states and spatial inhomogeneities is now a priority.

scholarly journals Sensitivity Analysis for Monte Carlo Simulation of Option Pricing

1995 ◽  
Vol 9 (3) ◽  
pp. 417-446 ◽  
Author(s):  
Michael C. Fu ◽  
Jian-Qlang Hu
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Option Pricing ◽  
Computational Effort ◽  
Analytical Models ◽  
Gradient Estimates ◽  
Option Value ◽  
Options Markets ◽  
Early Exercise

Monte Carlo simulation is one alternative for analyzing options markets when the assumptions of simpler analytical models are violated. We introduce techniques for the sensitivity analysis of option pricing, which can be efficiently carried out in the simulation. In particular, using these techniques, a single run of the simulation would often provide not only an estimate of the option value but also estimates of the sensitivities of the option value to various parameters of the model. Both European and American options are considered, starting with simple analytically tractable models to present the idea and proceeding to more complicated examples. We then propose an approach for the pricing of options with early exercise features by incorporating the gradient estimates in an iterative stochastic approximation algorithm. The procedure is illustrated in a simple example estimating the option value of an American call. Numerical results indicate that the additional computational effort required over that required to estimate a European option is relatively small.

Electron Scattering in Solids

1990 ◽  
Vol 48 (2) ◽  
pp. 4-5
Author(s):  
Ryuichi Shimizu ◽  
Ze-Jun Ding
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Angular Distribution ◽  
Elastic Scattering ◽  
Electron Scattering ◽  
Cross Sections ◽  
Expansion Method ◽  
Electron Excitation ◽  
Partial Wave Expansion ◽  
Backscattered Electrons

Monte Carlo simulation has been becoming most powerful tool to describe the electron scattering in solids, leading to more comprehensive understanding of the complicated mechanism of generation of various types of signals for microbeam analysis.The present paper proposes a practical model for the Monte Carlo simulation of scattering processes of a penetrating electron and the generation of the slow secondaries in solids. The model is based on the combined use of Gryzinski’s inner-shell electron excitation function and the dielectric function for taking into account the valence electron contribution in inelastic scattering processes, while the cross-sections derived by partial wave expansion method are used for describing elastic scattering processes. An improvement of the use of this elastic scattering cross-section can be seen in the success to describe the anisotropy of angular distribution of elastically backscattered electrons from Au in low energy region, shown in Fig.l. Fig.l(a) shows the elastic cross-sections of 600 eV electron for single Au-atom, clearly indicating that the angular distribution is no more smooth as expected from Rutherford scattering formula, but has the socalled lobes appearing at the large scattering angle.

Determination of Stoichiometry of GaAs Component in (Gaas)Ge Epitaxially Grown Thin Films by Electron Microprobe X-Ray Analysis

1990 ◽  
Vol 48 (2) ◽  
pp. 264-265
Author(s):  
D. R. Liu ◽  
S. S. Shinozaki ◽  
R. J. Baird
Keyword(s):  
Thin Film ◽  
Thin Films ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Compound Semiconductors ◽  
Energy Dispersive Analysis ◽  
X Ray ◽  
Metastable Alloys ◽  
Lower Voltage

The epitaxially grown (GaAs)Ge thin film has been arousing much interest because it is one of metastable alloys of III-V compound semiconductors with germanium and a possible candidate in optoelectronic applications. It is important to be able to accurately determine the composition of the film, particularly whether or not the GaAs component is in stoichiometry, but x-ray energy dispersive analysis (EDS) cannot meet this need. The thickness of the film is usually about 0.5-1.5 μm. If Kα peaks are used for quantification, the accelerating voltage must be more than 10 kV in order for these peaks to be excited. Under this voltage, the generation depth of x-ray photons approaches 1 μm, as evidenced by a Monte Carlo simulation and actual x-ray intensity measurement as discussed below. If a lower voltage is used to reduce the generation depth, their L peaks have to be used. But these L peaks actually are merged as one big hump simply because the atomic numbers of these three elements are relatively small and close together, and the EDS energy resolution is limited.

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