Reservoir Volume Estimation in Exploration Phase by Monte Carlo Simulation

2013 ◽  
Vol 859 ◽  
pp. 248-252
Author(s):  
Lei Zhao ◽  
Bing Li ◽  
Peng Xiang Diwu
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Civil Engineering ◽  
Resource Assessment ◽  
Monte Carlo Sampling ◽  
Volume Estimation ◽  
Volume Calculation ◽  
Reservoir Volume ◽  
Few Data ◽  
Exploration Phase

The STOIIP determines the scale of civil engineering in the oilfield, so the accurate calculation STOIIP has a very important significance on civil engineering, especially in the exploration phase few data are available in oilfield, traditional volume calculation method is hardly to provide a reasonable result. The mathematical statistics method, namely Monte Carlo simulation is introduced to calculate reservoir volumes for hydrocarbons in place (STOIIP or GIIP). This method can provide several volume results by monte carlo sampling. making the resource assessment results a probability distribution rather than a single valuation, which greatly improve the credibility and usefulness of evaluation results. The S oilfield in Malaysia are evaluated and the results show the P50 STOIIP is 4.82 MMbbl.

scholarly journals Monte Carlo sampling processes and incentive compatible allocations in large economies

2020 ◽  
Author(s):  
Peter J. Hammond ◽  
Lei Qiao ◽  
Yeneng Sun
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Incentive Compatibility ◽  
Random Variables ◽  
Almost Sure Convergence ◽  
Econ Theory ◽  
Monte Carlo Sampling ◽  
Link Type ◽  
Stochastic Framework ◽  
Almost All

Abstract Monte Carlo simulation is used in Hammond and Sun (Econ Theory 36:303–325, 2008. 10.1007/s00199-007-0279-7) to characterize a standard stochastic framework involving a continuum of random variables that are conditionally independent given macro shocks. This paper presents some general properties of such Monte Carlo sampling processes, including their one-way Fubini extension and regular conditional independence. In addition to the almost sure convergence of Monte Carlo simulation considered in Hammond and Sun (2008), here we also consider norm convergence when the random variables are square integrable. This leads to a necessary and sufficient condition for the classical law of large numbers to hold in a general Hilbert space. Applying this analysis to large economies with asymmetric information shows that the conflict between incentive compatibility and Pareto efficiency is resolved asymptotically for almost all sampling economies, following some similar results in McLean and Postlewaite (Econometrica 70:2421–2453, 2002) and Sun and Yannelis (J Econ Theory 134:175–194, 2007. 10.1016/j.jet.2006.03.001).

scholarly journals Monte Carlo Simulation in Prediction of Periodical Sales

2018 ◽  
Author(s):  
Andysah Putera Utama Siahaan
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Sampling Technique ◽  
Monte Carlo Sampling ◽  
Potential Consumer ◽  
The Monte Carlo Method ◽  
Sales Prediction ◽  
Sample Data ◽  
To Come ◽  
A Company

Sales prediction is a budget that contains estimates about the activities of a company within a specified period to come and contains estimates of the state or financial position of the company in the future. The outcome of a sales prediction is a statement or assessment that is quantified against future conditions regarding sales as a technical projection of potential consumer demand for a certain period. However, the estimated results obtained may not be the same as the plan. The Monte Carlo simulation is also known as the Sampling Simulation or the Monte Carlo Sampling technique. Sampling Simulation illustrates the possibility of using sample data in the Monte Carlo method and its probability can be known or estimated. This simulation uses valid data in previous periods. The data is used to predict the possible results that will occur in the coming period. The purpose of this simulation is to estimate the profitability of a company to sell.

scholarly journals Penetration of Vancomycin into Epithelial Lining Fluid in Healthy Volunteers

2011 ◽  
Vol 55 (12) ◽  
pp. 5507-5511 ◽  
Author(s):  
Thomas P. Lodise ◽  
George L. Drusano ◽  
Jill M. Butterfield ◽  
Joshua Scoville ◽  
Mark Gotfried ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Healthy Volunteers ◽  
Compartment Model ◽  
Epithelial Lining ◽  
Time Curve ◽  
Epithelial Lining Fluid ◽  
Content Type ◽  
Concentration Time ◽  
Few Data

ABSTRACTAlthough vancomycin is often regarded as an agent that concentrates poorly in the lower respiratory tract, as determined from concentrations in epithelial lining fluid (ELF), few data are available. This study sought to determine the profile of vancomycin exposure in the ELF relative to plasma. Population modeling and Monte Carlo simulation were employed to estimate the penetration of vancomycin into ELF. Plasma and ELF pharmacokinetic (PK) data were obtained from 10 healthy volunteers. Concentration-time profiles in plasma and ELF were simultaneously modeled using a three-compartment model with zero-order infusion and first-order elimination and transfer using the big nonparametric adaptive grid (BigNPAG) program. Monte Carlo simulation with 9,999 subjects was performed to calculate the ELF/plasma penetration ratios by estimating the area under the concentration-time curve (AUC) in ELF (AUCELF) and plasma (AUCplasma) after a single simulated 1,000-mg dose. The mean (standard deviation) AUCELF/AUCplasmapenetration ratio was 0.675 (0.677), and the 25th, 50th, and 75th percentile penetration ratios were 0.265, 0.474, and 0.842, respectively. Our results indicate that vancomycin penetrates ELF at approximately 50% of plasma levels. To properly judge the adequacy of current doses and schedules employed in practice, future studies are needed to delineate the PK/PD (pharmacodynamics) target for vancomycin in ELF. If the PK/PD target in ELF is found to be consistent with the currently proposed target of an AUC/MIC of ≥400, suboptimal probability of target attainment would be expected when vancomycin is utilized for pneumonias due to MRSA (methicillin-resistantStaphylococcus aureus) with MICs in excess of 1 mg/liter.

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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