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Reservoir stochastic modeling Constrained by Quantitative Geological Conceptual Patterns

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Petroleum Science 2006年第1期3卷 27-33页

作者： Wu Shenghe Zhang Yiwei Jan Einar Ringas Faculty of Natural Resource ＆ Information Technology China University of Petroleum Beijing 102249 China Statoil Trondheim Norway

This paper discusses the principles of geologic constraints on reservoir stochastic modeling. By using the system science theory, two kinds of uncertainties, including random uncertainty and fuzzy uncertainty, are recognized. In order to improve the precision of stochastic modeling and reduce the uncertainty in realization, the fuzzy uncertainty should be stressed, and the ＂geological genesis-controlled modeling＂ is conducted under the guidance of a quantitative geological pattern. An example of the Pingqiao horizontal-well division of the Ansai Oilfield in the Ordos Basin is taken to expound the method of stochastic modeling.

关键词： Reservoir stochastic modeling geological constraints sedimentary facies

stochastic modeling for starting-time of phase evolution of random seismic ground motions

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Theoretical & Applied Mechanics Letters 2014年第1期4卷 63-67页

作者： Yongbo Peng Jie Li State Key Laboratory of Disaster Reduction in Civil Engineering Tongji University Shanghai Institute of Disaster Prevention and Relief Tongji University School of Civil Engineering Tongji University

In response to the challenge inherent in classical high-dimensional models of random ground motions, a family of simulation methods for nonstationary seismic ground motions was developed previously through employing a wave-group propagation formulation with phase spectrum model built up on the frequency components’ starting-time of phase evolution. The present paper aims at extending the formulation to the simulation of non-stationary random seismic ground motions. The ground motion records associated with N–S component of Northridge Earthquake at the type-II site are investigated. The frequency components’ starting-time of phase evolution of is identified from the ground motion records, and is proved to admit the Gamma distribution through data fitting. Numerical results indicate that the simulated random ground motion features zeromean, non-stationary, and non-Gaussian behaviors, and the phase spectrum model with only a few starting-times of phase evolution could come up with a sound contribution to the simulation.

关键词： stochastic modeling starting-time phase spectrum Gamma distribution nonstationary Northridge Earthquake

A Discussion on Two stochastic Elliptic modeling Strategies

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Communications in Computational Physics 2012年第3期11卷 775-796页

作者： Xiaoliang Wan Department of Mathematics and Center for Computation and Technology Louisiana State UniversityBaton RougeLA 70803USA

Based on the study of two commonly used stochastic elliptic models:I:−∇·(a(x,w)·∇u(x,w))=f(x)and II:−∇·(a(x,w)⋄∇u(x,w))=f(x),we constructed a new stochastic elliptic model III:−∇·(a^(−1))^(⋄(−1))⋄∇u(x,w)=f(x),in[20].The difference between models I and II is twofold:a scaling factor induced by the way of applying theWick product and the regularization induced by theWick product ***[20],we showed that model III has the same scaling factor as model *** this paper we present a detailed discussion about the difference between models I and III with respect to the two characteristic parameters of the random coefficient,i.e.,the standard deviation s and the correlation length *** results are presented for both one-and twodimensional cases.

关键词： Wiener chaos stochastic PDE stochastic modeling stochastic finite element method

modeling stochastic noise in gene regulatory systems

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Frontiers of Electrical and Electronic Engineering in China 2014年第1期9卷 1-29页

作者： Arwen Meister Chao Du Ye Henry Li Wing Hung Wong Computational Biology Lab Bio-X Program Stanford University Stanford CA 94305 USA

The Master equation is considered the gold standard for modeling the stochastic mechanisms of gene regulation in molecular detail, but it is too complex to solve exactly in most cases, so approximation and simulation methods are essential. However, there is still a lack of consensus about the best way to carry these out. To help clarify the situation, we review Master equation models of gene regulation, theoretical approximations based on an expansion method due to N.G. van Kampen and R. Kubo, and simulation algorithms due to D.T. Gillespie and P. Langevin. Expansion of the Master equation shows that for systems with a single stable steady-state, the stochastic model reduces to a deterministic model in a first-order approximation. Additional theory, also due to van Kampen, describes the asymptotic behavior of multistable systems. To support and illustrate the theory and provide further insight into the complex behavior of multistable systems, we perform a detailed simulation study comparing the various approximation and simulation methods applied to synthetic gene regulatory systems with various qualitative characteristics. The simulation studies show that for large stochastic systems with a single steady-state, deterministic models are quite accurate, since the probability distribution of the solution has a single peak tracking the deterministic trajectory whose variance is inversely proportional to the system size. In multistable stochastic systems, large fluctuations can cause individual trajectories to escape from the domain of attraction of one steady-state and be attracted to another, so the system eventually reaches a multimodal probability distribution in which all stable steady- states are represented proportional to their relative stability. However, since the escape time scales exponentially with system size, this process can take a very long time in large systems.

关键词： gene regulation stochastic modeling simulation Master equation Gillespie algorithm Langevin equation

stochastic Epidemic Model of Covid-19 via the Reservoir-People Transmission Network

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Computers, Materials & Continua 2022年第7期72卷 1495-1514页

作者： Kazem Nouri Milad Fahimi Leila Torkzadeh Dumitru Baleanu Department of Mathematics Faculty of MathematicsStatistics and Computer SciencesSemnan UniversityP.O.Box 35195-363SemnanIran Department of Mathematics Faculty of Arts and SciencesCankaya UniversityAnkaraTurkey Institute of Space Sciences Magurele-BucharestRomania

The novel Coronavirus COVID-19 emerged in Wuhan,China in December ***-19 has rapidly spread among human populations and other *** outbreak of COVID-19 has become a global *** models of epidemiological systems enable studying and predicting the potential spread of *** and predicting the evolution of COVID-19 epidemics in near real-time is a scientific challenge,this requires a deep understanding of the dynamics of pandemics and the possibility that the diffusion process can be completely *** this paper,we develop and analyze a model to simulate the Coronavirus transmission dynamics based on Reservoir-People transmission *** faced with a potential outbreak,decision-makers need to be able to trust mathematical models for their decision-making *** of the most considerable characteristics of COVID-19 is its different behaviors in various countries and regions,or even in different individuals,which can be a sign of uncertain and accidental behavior in the disease *** trait reflects the existence of the capacity of transmitting perturbations across its *** construct a stochastic environment because of parameters random essence and introduce a stochastic version of *** we prove the uniqueness and existence of the solution on the stochastic ***,the equilibria of the system are ***,we establish the extinction of the disease under some suitable ***,some numerical simulation and comparison are carried out to validate the theoretical results and the possibility of comparability of the stochastic model with the deterministic model.

关键词： Coronavirus infectious diseases stochastic modeling brownian motion reservoir-people model transmission simulation stochastic differential equation

Reservoir lithology stochastic simulation based on Markov random fields

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Journal of Central South University 2014年第9期21卷 3610-3616页

作者：梁玉汝王志忠郭建华 School of Mathematics and Statistics Central South University School of Earth Sciences and Physical Information Central South University

Markov random fields(MRF) have potential for predicting and simulating petroleum reservoir facies more accurately from sample data such as logging, core data and seismic data because they can incorporate interclass relationships. While, many relative studies were based on Markov chain, not MRF, and using Markov chain model for 3D reservoir stochastic simulation has always been the difficulty in reservoir stochastic simulation. MRF was proposed to simulate type variables(for example lithofacies) in this work. Firstly, a Gibbs distribution was proposed to characterize reservoir heterogeneity for building 3-D(three-dimensional) MRF. Secondly, maximum likelihood approaches of model parameters on well data and training image were considered. Compared with the simulation results of MC(Markov chain), the MRF can better reflect the spatial distribution characteristics of sand body.

关键词： stochastic modeling Markov random fields training image Monte Carlo simulation

stochastic particle based models for suspended particle movement in surface flows

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International Journal of Sediment Research 2014年第2期29卷 195-207页

作者： Christina W.TSAI Chuanjian MAN Jungsun OH Department of Civil Structural and Environmental EngineeringState University of New York at BuffaloBuffaloNY 14051USA Assoc.Prof. Department of Civil and Environmental Engineering National Taiwan University

modeling of suspended sediment particle movement in surface water can be achieved by stochastic particle tracking model *** this paper,different mathematical forms of particle tracking models are introduced to describe particle movement under various flow conditions,i.e.,the stochastic diffusion process,stochastic jump process,and stochastic jump diffusion *** the stochastic diffusion process can be used to represent the stochastic movement of suspended particles in turbulent flows,the stochastic jump and the stochastic jump diffusion processes can be used to describe suspended particle movement in the occurrences of a sequence of extreme *** extreme flow herein is defined as a hydrologic flow event or a hydrodynamic flow phenomenon with a low probability of occurrence and a high impact on its ambient flow *** this paper,the suspended sediment particle is assumed to immediately follow the extreme flows in the jump process(*** time lag between the flow particle and the sediment particle in extreme flows is considered negligible).In the proposed particle tracking models,a random term mainly caused by fluid eddy motions is modeled as a Wiener process,while the random occurrences of a sequence of extreme flows can be modeled as a Poisson *** frequency of occurrence of the extreme flows in the proposed particle tracking model can be explicitly accounted for by the Poisson process when evaluating particle *** ensemble mean and variance of particle trajectory can be obtained from the proposed stochastic models via *** ensemble mean and variance of particle velocity are verified with available *** of the proposed stochastic particle tracking models for sediment transport modeling is also discussed.

关键词： Suspended sediment Particle tracking model stochastic modeling Diffusion process Poisson jump process Jump diffusion process Extreme flows

Hierarchical modeling of stochastic manufacturing and service systems

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Frontiers of Engineering Management 2017年第3期4卷 295-303页

作者： Zhe George ZHANG Xiaoling YIN Department of Decision Sciences Western Washington University Bellingham Beedie School of Business Simon Fraser University Burnaby School of Management Lanzhou University

This paper presents a review of methodologies for analyzing stochastic manufacturing and service systems. On the basis of the scale and level of details of operations, we can study stochastic systems using micro-,meso-, and macro-scopic models. Such a classification unifies stochastic modeling theory. For each model type,we highlight the advantages and disadvantages and the applicable situations. Micro-scopic models are based on quasi-birth-and-death process because of the phase-type distributed service times and/or Markov arrival *** models are appropriate for modeling the detailed operations of a manufacturing system with relatively small number of servers(production facilities). By contrast,meso-scopic and macro-scopic models are based on the functional central limit theorem(FCLT) and functional strong law of large numbers(FSLLN), respectively, under heavy-traffic regimes. These high-level models are appropriate for modeling large-scale service systems with many servers, such as call centers or large service networks. This review will help practitioners select the appropriate level of modeling to enhance their understanding of the dynamic behavior of manufacturing or service systems. Enhanced understanding will ensure that optimal policies can be designed to improve system performance. Researchers in operation analytics and optimization of manufacturing and logistics also benefit from such a review.

关键词： stochastic modeling QBD process PH distribution heavy traffic limits diffusion process

stochastic simulation of ground motions based on NGA-West2 strong motion records

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Earthquake Science 2019年第3期32卷 115-124页

作者： Peng Tian Xiaodan Sun Xin Li Keyu Wan Departement of Civil Engineering Southwest Jiaotong UniversityChengdu 610031China

stochastic modeling of ground motion is a simple tool to predict ground shaking level for future earthquake and less time consuming than physics-based deterministic *** this paper,a record-based stochastic method that considers the time-and frequency-evolution of ground motion is used to estimate ground motion for scenario earthquakes in tectonic active *** stochastic method employs a time-domain modulation function to describe the temporal nonstationarity and a filter impulse response function that describe the evolution of frequency *** characterizing the modulation function and the filter impulse function,six parameters(Ia,D5-95,tmid,ωmid,ω',ξf)are defined,and 2,571 pairs of ground motion recording in the NGA-west2 database are selected to identify the six *** density function is assigned to each of the parameter by fitting the frequency distribution *** parameters are then transformed into standard normal space where regression analysis is performed by considering each parameter as function of moment magnitude,rupture distance,vS30(The time-averaged shear wave velocity of the top 30 m of soil).The prediction equations are used to generate ground motions for several scenario earthquakes and compared to NGA-West2 GMPEs.

关键词： stochastic modeling nonstationarity scenario earthquake NGA-West2