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1.
The prediction of magnitude (M) of reservoir induced earthquake is an important task in earthquake engineering. In this article, we employ a Support Vector Machine (SVM) and Gaussian Process Regression (GPR) for prediction of reservoir induced earthquake M based on reservoir parameters. Comprehensive parameter (E) and maximum reservoir depth (H) are considered as inputs to the SVM and GPR. We give an equation for determination of reservoir induced earthquake M. The developed SVM and GPR have been compared with the Artificial Neural Network (ANN) method. The results show that the developed SVM and GPR are efficient tools for prediction of reservoir induced earthquake M. 相似文献
2.
碳酸盐岩、致密砂岩和页岩等储层具有孔隙类型多样、孔隙结构复杂和非均质性强等特征,属于典型的多重孔隙储层,孔隙结构表征是多重孔隙储层预测和流体识别的关键.现有的孔隙结构表征方法大多利用孔隙纵横比或者构建一种新参数来描述孔隙结构.岩石临界孔隙度模型是一种常用的岩石物理模型,具有一定的物理意义和地质含义.本文推导了岩石临界孔隙度与岩石孔隙结构(孔隙纵横比)之间的关系,进而利用极化(形状)因子建立临界孔隙度与弹性参数之间的关系,构建了能够包含多种孔隙类型的多孔可变临界孔隙度模型.利用多孔可变临界孔隙度模型由储层的弹性参数反演不同孔隙类型的体积含量.实验室测量数据和实际测井数据表明,多孔可变临界孔隙度模型能够适用于多重孔隙储层岩石物理建模和孔隙结构表征. 相似文献
3.
In this article we present the modelling of uncertainty in strong-motion studies for engineering applications, particularly
for the assessment of earthquake hazard. We examine and quantify the sources of uncertainty in the basic variables involved
in ground motion estimation equations, including those associated with the seismological parameters, which we derive from
a considerable number of strong-motion records. Models derived from regression analysis result in ground motion equations
with uncertain parameters, which are directly related to the selected basic variables thus providing an uncertainty measure
for the derivative variable. These uncertainties are exemplified and quantified. An alternative approach is presented which
is based on theoretical modelling defining a functional relationship on a set of independent basic variables. Uncertainty
in the derivative variable is then readily obtained when the uncertainties of the basic variables have been defined. In order
to simplify the presentation, only the case of shallow strike-slip earthquakes is presented. We conclude that the uncertainty
is approximately the same as given by the residuals typical for regression modelling. This implies that uncertainty in ground
motion modelling cannot be reduced below certain limits, which is in accordance with findings reported in the literature.
Finally we discuss the implications of the presented methodology in hazard analyses, which is sensitive to the truncation
of the internal error term, commonly given as an integral part of ground motion estimation equations. The presented methodology
does not suffer from this shortcoming; it does not require truncation of the error term and yields realistic hazard estimates.
This revised version was published online in July 2006 with corrections to the Cover Date. 相似文献
4.
肯吉亚克油田石炭系油藏属持低孔渗、异常高压碳酸盐岩油藏，它除了具有埋深大，非均质性强，油气成藏控制因素复杂等特点外，其上还覆盖巨厚盐丘，造成盐下地震反射时间和振幅畸变严重，地震成像差、信噪比低和分辨率低，给储层预测工作带来极大困难。如何正确预测油藏高产带分布规律是高效开发这类油藏的关键，本文研究从分析形成碳酸盐岩油藏高产带的主控因素入手，通过井震标定，优选反映碳酸盐岩岩相、岩溶、物性和裂缝的地震属性，结合地震、地质、测井、油藏工程和钻井资料，把盐下特低孔渗碳酸盐岩油藏高产带预测问题分解成构造解释、岩相预测、岩溶预测、物性预测、裂缝预测和综合评价等六个环节。宏观上，通过建立断裂、岩相、岩溶模式，定性预测储层分布有利区带；微观上，通过多参数储层特征反演和多属性综合分析，定量、半定量预测有利储层分布，有效解决盐下碳酸盐岩油藏高产带预测难题，基本搞清本区碳酸盐岩油藏高产带分布规律，为优选有利勘探和开发目标提供依据。文中提出的方法和技术对解决国内外碳酸盐岩油藏高产带预测和其他复杂储层预测问题有借鉴作用。 相似文献
5.
The uncertainty in the seismic demand of a structure (referred to as the engineering demand parameter, EDP) needs to be properly characterized in performance‐based earthquake engineering. Uncertainties in the ground motion and in structural properties are responsible for EDP uncertainty. In this study, sensitivity of EDPs to major uncertain variables is investigated using the first‐order second‐moment method for a case study building. This method is shown to be simple and efficient for estimating the sensitivity of seismic demand. The EDP uncertainty induced by each uncertain variable is used to determine which variables are most significant. Results show that the uncertainties in ground motion are more significant for global EDPs, namely peak roof acceleration and displacement, and maximum inter‐storey drift ratio, than those in structural properties. Uncertainty in the intensity measure (IM) of ground motion is the dominant variable for uncertainties in local EDPs such as the curvature demand at critical cross‐sections. Conditional sensitivity of global and local EDPs given IM is also estimated. It is observed that the combined effect of uncertainties in structural properties is more significant than uncertainty in ground motion profile at lower IM levels, while the opposite is true at higher IM levels. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
6.
J. T. Shiau 《Stochastic Environmental Research and Risk Assessment (SERRA)》2003,17(1-2):42-57
Extreme hydrological events are inevitable and stochastic in nature. Characterized by multiple properties, the multivariate
distribution is a better approach to represent this complex phenomenon than the univariate frequency analysis. However, it
requires considerably more data and more sophisticated mathematical analysis. Therefore, a bivariate distribution is the most
common method for modeling these extreme events. The return periods for a bivariate distribution can be defined using either
separate single random variables or two joint random variables. In the latter case, the return periods can be defined using
one random variable equaling or exceeding a certain magnitude and/or another random variable equaling or exceeding another
magnitude or the conditional return periods of one random variable given another random variable equaling or exceeding a certain
magnitude. In this study, the bivariate extreme value distribution with the Gumbel marginal distributions is used to model
extreme flood events characterized by flood volume and flood peak. The proposed methodology is applied to the recorded daily
streamflow from Ichu of the Pachang River located in Southern Taiwan. The results show a good agreement between the theoretical
models and observed flood data.
The author wishes to thank the two anonymous reviewers for their constructive comments that improving the quality of this
work. 相似文献
7.
J. A. Vargas-Guzmán 《Stochastic Environmental Research and Risk Assessment (SERRA)》2012,26(6):765-776
Forecasting of extreme events and phenomena that respond to non-Gaussian heavy-tailed distributions (e.g., extreme environmental events, rock permeability, rock fracture intensity, earthquake magnitudes) is essential to environmental and geoscience risk analysis. In this paper, new parametric heavy-tailed distributions are devised starting from the exponential power probability density function (pdf) which is modified by explicitly including higher-order “cumulant parameters” into the pdf. Instead of dealing with whole power random variables, novel “residual” random variables are proposed to reconstruct the cumulant generating function. The expected value of a residual random variable with the corresponding pdf for order G, gives the input higher-order cumulant parameter. Thus, each parametric pdf is used to simulate a random variable containing residuals that yield, in average, the expected cumulant parameter. The cumulant parameters allow the formulation of heavy-tailed skewed pdfs beyond the lognormal to handle extreme events. Monte Carlo simulation of heavy-tailed distributions with higher-order parameters is demonstrated with a simple example for permeability. 相似文献
8.
Compressional-wave (Vp) data are key information for estimation of rock physical properties and formation evaluation in hydrocarbon reservoirs. However, the absence of Vp will significantly delay the application of specific risk-assessment approaches for reservoir exploration and development procedures. Since Vp is affected by several factors such as lithology, porosity, density, and etc., it is difficult to model their non-linear relationships using conventional approaches. In addition, currently available techniques are not efficient for Vp prediction, especially in carbonates. There is a growing interest in incorporating advanced technologies for an accurate prediction of lacking data in wells. The objectives of this study, therefore, are to analyze and predict Vp as a function of some conventional well logs by two approaches; Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR). Also, the significant impact of selected input parameters on response variable will be investigated. A total of 2156 data points from a giant Middle Eastern carbonate reservoir, derived from conventional well logs and Dipole Sonic Imager (DSI) log were utilized in this study. The quality of the prediction was quantified in terms of the mean squared error (MSE), correlation coefficient (R-square), and prediction efficiency error (PEE). Results show that the ANFIS outperforms MLR with MSE of 0.0552, R-square of 0.964, and PEE of 2%. It is posited that porosity has a significant impact in predicting Vp in the investigated carbonate reservoir. 相似文献
9.
数字岩心微观孔隙结构十分复杂,有限元模拟物性参数与弹性参数之间关系是非线性的,直接反演其物性参数准确度低、稳定性差.本文发展了一种数字岩石物理逆建模方法,实现了基于数字岩心的储层参数有效预测.从数字岩心基函数的构建出发,基于有限元方法,计算了一系列具有等间距物性参数值(孔隙度、泥质含量和含水饱和度)的数字岩心弹性参数(体积模量、剪切模量和密度),通过插值算法建立了数字岩心弹性参数三维数据集,从而实现了弹性模量的有限元数值解的快速构建;然后搜索弹性参数的单值等值面,通过等值面的空间交会得到交点,完成储层参数预测.测试结果表明:基于数字岩心逆建模理论的储层参数预测结果与实际模型一致,具有可行性,并且可以通过增加插值点数目提高预测的准确性;孔隙度和泥质含量预测结果稳定性很好,而含水饱和度对噪声的加入较为敏感. 相似文献
10.
Accounting for the uncertainty in the local mean in spatial prediction by Bayesian Maximum Entropy 总被引：2，自引：2，他引：2
T. G. Orton R. M. Lark 《Stochastic Environmental Research and Risk Assessment (SERRA)》2007,21(6):773-784
Bayesian Maximum Entropy (BME) has been successfully used in geostatistics to calculate predictions of spatial variables given
some general knowledge base and sets of hard (precise) and soft (imprecise) data. This general knowledge base commonly consists
of the means at each of the locations considered in the analysis, and the covariances between these locations. When the means
are not known, the standard practice is to estimate them from the data; this is done by either generalized least squares or
maximum likelihood. The BME prediction then treats these estimates as the general knowledge means, and ignores their uncertainty.
In this paper we develop a prediction that is based on the BME method that can be used when the general knowledge consists
of the covariance model only. This prediction incorporates the uncertainty in the estimated local mean. We show that in some
special cases our prediction is equal to results from classical geostatistics. We investigate the differences between our
approach and the standard approach for predicting in this common practical situation. 相似文献
11.
Accurate prediction of the water level in a reservoir is crucial to optimizing the management of water resources. A neuro-fuzzy hybrid approach was used to construct a water level forecasting system during flood periods. In particular, we used the adaptive network-based fuzzy inference system (ANFIS) to build a prediction model for reservoir management. To illustrate the applicability and capability of the ANFIS, the Shihmen reservoir, Taiwan, was used as a case study. A large number (132) of typhoon and heavy rainfall events with 8640 hourly data sets collected in past 31 years were used. To investigate whether this neuro-fuzzy model can be cleverer (accurate) if human knowledge, i.e. current reservoir operation outflow, is provided, we developed two ANFIS models: one with human decision as input, another without. The results demonstrate that the ANFIS can be applied successfully and provide high accuracy and reliability for reservoir water level forecasting in the next three hours. Furthermore, the model with human decision as input variable has consistently superior performance with regard to all used indexes than the model without this input. 相似文献
12.
A specific characteristic of karst systems is the occurrence of time variant recharge areas. In our study we present a new type of hydrological karst model and a new calibration approach both considering this specific characteristic. The new model type considers the spatial variability of karst system properties by distribution functions, and is compared to a simple reservoir model. Both models are applied to a karst system in Southern Spain where objective functions applied on hydrodynamic and hydrochemical information helped to determine model parameters playing a role for hydrodynamic response. Thereafter, the recharge area is determined separately for individual hydrological years and for the entire time series by calibrating the model to match the water balance. We show that hydrochemical information is crucial to find a reasonable set of parameters for both models. Considering different hydrological years, we find that the recharge area is changing significantly (from 28 to 53 km2). The newly developed model is able to reproduce this variation and provide acceptable simulation results for the entire time series of available data. The classic reservoir model shows inferior performance concerning hydrodynamics and fails to reproduce the water balance because it does not consider variations of recharge area. Our calibration approach allows identifying a variable recharge area and our new model is able to reproduce its variability. Hence we obtain a more realistic system representation, which can be of high significance when models are used for prediction, i.e. beyond the conditions they were calibrated, e.g. for land-use or climate change scenarios. 相似文献
13.
《水文科学杂志》2013,58(3):582-595
Abstract This paper explores the potential for seasonal prediction of hydrological variables that are potentially useful for reservoir operation of the Three Gorges Dam, China. The seasonal flow of the primary inflow season and the peak annual flow are investigated at Yichang hydrological station, a proxy for inflows to the Three Gorges Dam. Building on literature and diagnostic results, a prediction model is constructed using sea-surface temperatures and upland snow cover available one season ahead of the prediction period. A hierarchical Bayesian approach is used to estimate uncertainty in the parameters of the prediction model and to propagate these uncertainties to the predictand. The results show skill for both the seasonal flow and the peak annual flow. The peak annual flow model is then used to estimate a design flood (50-year flood or 2% exceedence probability) on a year-to-year basis. The results demonstrate the inter-annual variability in flood risk. The predictability of both the seasonal total inflow and the peak annual flow (or a design flood volume) offers potential for adaptive management of the Three Gorges Dam reservoir through modification of the operating policy in accordance with the year-to-year changes in these variables. 相似文献
14.
基于岩石物理和地震反演理论,提出了一种同步反演储层孔隙度和含水饱和度的方法.以岩石物理为基础,建立了砂泥岩储层物性和弹性参数之间定量的关系-Simon模型,以贝叶斯理论为手段,结合不同类型的砂泥岩储层,建立了多信息联合约束的物性参数反演目标函数,并通过蒙特卡罗和遗传算法相结合的思路求解该目标函数,最终得到孔隙度和含水饱和度的同步反演结果.将该方法应用于河道砂和砂砾岩两种不同的砂泥岩储层中,孔隙度和含水饱和度数据的联合应用,进一步减少了储层预测的多解性,为石油地质综合研究提供了更加丰富准确的基础数据. 相似文献
15.
We propose a novel intelligent reservoir operation system based on an evolving artificial neural network (ANN). Evolving means the parameters of the ANN model are identified by the GA evolutionary optimization technique. Accordingly, the ANN model should represent the operational strategies of reservoir operation. The main advantages of the Evolving ANN Intelligent System (ENNIS) are as follows: (i) only a small number of parameters to be optimized even for long optimization horizons, (ii) easy to handle multiple decision variables, and (iii) the straightforward combination of the operation model with other prediction models. The developed intelligent system was applied to the operation of the Shihmen Reservoir in North Taiwan, to investigate its applicability and practicability. The proposed method is first built to a simple formulation for the operation of the Shihmen Reservoir, with single objective and single decision. Its results were compared to those obtained by dynamic programming. The constructed network proved to be a good operational strategy. The method was then built and applied to the reservoir with multiple (five) decision variables. The results demonstrated that the developed evolving neural networks improved the operation performance of the reservoir when compared to its current operational strategy. The system was capable of successfully simultaneously handling various decision variables and provided reasonable and suitable decisions. 相似文献
16.
The importance of site-specific characters in prediction models for blast-induced ground vibrations 总被引：3，自引：0，他引：3
The aim of this study is to show the effect of geological factors in predicting the level of blast-induced ground vibrations. The site-specific character of ground must be involved in the prediction models especially if the ground conditions have a variable character like in this case. But in a blasting environment, this is only possible by using an empirical way. Towards this aim, an in-situ experimental study in a highly jointed sandstone quarry was carried out to incorporate the variable conditions into the prediction models. Therefore, 60 shots were organized and their ground vibrations monitored in two different directions to compare the results. These shots were normal production shots involving the true technological properties as well as geological properties into the prediction model. Based on these, the empirical relations between particle velocity, the amount of explosive and distance have been developed. The results show that the performances of these estimations depend on the site-specific character of these empirical relations. The best prediction was obtained with only 2.08% error level provided that the true technological and geological properties are involved. 相似文献
17.
Amir Abbas Babasafari Deva Prasad Ghosh Ahmed Mohammed Ahmed Salim Masoumeh Kordi 《Geophysical Prospecting》2020,68(8):2471-2493
Analysis of amplitude variation with offset is an essential step for reservoir characterization. For an accurate reservoir characterization, the amplitude obtained with an isotropic assumption of the reservoir must be corrected for the anisotropic effects. The objective is seismic anisotropic amplitude correction in an effective medium, and, to this end, values and signs of anisotropic parameter differences (Δδ and Δε) across the reflection interfaces are needed. These parameters can be identified by seismic and well log data. A new technique for anisotropic amplitude correction was developed to modify amplitude changes in seismic data in transversely isotropic media with a vertical axis of symmetry. The results show that characteristics of pre-stack seismic data, that is, amplitude variation with offset gradient, can be potentially related to the sign of anisotropic parameter differences (Δδ and Δε) between two layers of the reflection boundary. The proposed methodology is designed to attain a proper fit between modelled and observed amplitude variation with offset responses, after anisotropic correction, for all possible lithofacies at the reservoir boundary. We first estimate anisotropic parameters, that is, δ and ε, away from the wells through Backus averaging of elastic properties resulted from the first pass of isotropic pre-stack seismic inversion, on input data with no amplitude correction. Next, we estimate the anisotropic parameter differences at reflection interfaces (values and signs of Δδ and Δε). We then generate seismic angle gather data after anisotropic amplitude correction using Rüger's equation for the P-P reflection coefficient. The second pass of isotropic pre-stack seismic inversion is then performed on the amplitude-corrected data, and elastic properties are estimated. Final outcome demonstrates how introduced methodology helps to reduce the uncertainty of elastic property prediction. Pre-stack seismic inversion on amplitude-corrected seismic data results in more accurate elastic property prediction than what can be obtained from non-corrected data. Moreover, a new anisotropy attribute (ν) is presented for improvement of lithology identification. 相似文献
18.
In this paper, we present a conceptual‐numerical model that can be deduced from a calibrated finite difference groundwater‐flow model, which provides a parsimonious approach to simulate and analyze hydraulic heads and surface water body–aquifer interaction for linear aquifers (linear response of head to stresses). The solution of linear groundwater‐flow problems using eigenvalue techniques can be formulated with a simple explicit state equation whose structure shows that the surface water body–aquifer interaction phenomenon can be approached as the drainage of a number of independent linear reservoirs. The hydraulic head field could be also approached by the summation of the head fields, estimated for those reservoirs, defined over the same domain set by the aquifer limits, where the hydraulic head field in each reservoir is proportional to a specific surface (an eigenfunction of an eigenproblem, or an eigenvector in discrete cases). All the parameters and initial conditions of each linear reservoir can be mathematically defined in a univocal way from the calibrated finite difference model, preserving its characteristics (geometry, boundary conditions, hydrodynamic parameters (heterogeneity), and spatial distribution of the stresses). We also demonstrated that, in practical cases, an accurate solution can be obtained with a reduced number of linear reservoirs. The reduced computational cost of these solutions can help to integrate the groundwater component within conjunctive use management models. Conceptual approximation also facilitates understanding of the physical phenomenon and analysis of the factors that influence it. A simple synthetic aquifer has been employed to show how the conceptual model can be built for different spatial discretizations, the parameters required, and their influence on the simulation of hydraulic head fields and stream–aquifer flow exchange variables. A real‐world case was also solved to test the accuracy of the proposed approaches, by comparing its solution with that obtained using finite‐difference MODFLOW code. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
19.
A statistical framework of data fusion for spatial prediction of categorical variables 总被引：1，自引：0，他引：1
Guofeng Cao Eun-hye Yoo Shaowen Wang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2014,28(7):1785-1799
With rapid advances of geospatial technologies, the amount of spatial data has been increasing exponentially over the past few decades. Usually collected by diverse source providers, the available spatial data tend to be fragmented by a large variety of data heterogeneities, which highlights the need of sound methods capable of efficiently fusing the diverse and incompatible spatial information. Within the context of spatial prediction of categorical variables, this paper describes a statistical framework for integrating and drawing inferences from a collection of spatially correlated variables while accounting for data heterogeneities and complex spatial dependencies. In this framework, we discuss the spatial prediction of categorical variables in the paradigm of latent random fields, and represent each spatial variable via spatial covariance functions, which define two-point similarities or dependencies of spatially correlated variables. The representation of spatial covariance functions derived from different spatial variables is independent of heterogeneous characteristics and can be combined in a straightforward fashion. Therefore it provides a unified and flexible representation of heterogeneous spatial variables in spatial analysis while accounting for complex spatial dependencies. We show that in the spatial prediction of categorical variables, the sought-after class occurrence probability at a target location can be formulated as a multinomial logistic function of spatial covariances of spatial variables between the target and sampled locations. Group least absolute shrinkage and selection operator is adopted for parameter estimation, which prevents the model from over-fitting, and simultaneously selects an optimal subset of important information (variables). Synthetic and real case studies are provided to illustrate the introduced concepts, and showcase the advantages of the proposed statistical framework. 相似文献
20.
随着储层压力的降低,克林伯格效应对渗透率的影响越来越大.现有的煤储层渗透率预测模型大都忽略了克林伯格系数的变化,其预测结果与实际生产存在一定的差异,尤其是在低储层压力阶段.本文以体积不变假设为基础,基于火柴棍模型给出在储层压力降低过程中动态克林伯格系数的计算公式,并建立考虑动态克林伯格系数的渗透率预测模型;深入分析在煤储层压力降低过程中,煤储层渗透率和克林伯格系数的变化规律.研究结果表明：随着储层压力的降低,克林伯格系数呈先增大后减小的变化趋势;在相同储层压力下,克林伯格系数随渗透率增加呈指数减小趋势,随温度增加呈线性增大趋势.本文建立的渗透率模型参数简单易获取,预测结果与实际煤储层渗透率变化规律符合性较好,尤其是在低储层压力阶段,能准确预测煤储层渗透率变化. 相似文献