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1.
成熟勘探区剩余资源量的多少倍受关注。根据金湖凹陷的油藏规模分布特征,应用分形方法估计该区的石油地质资源。石油地质资源总量为12284×104t,分布在507个油藏中;其中未发现的具有经济价值的石油地质资源量为2740×104t,分布在126个油藏中。这反映了该区的油气勘探程度较高,但仍有一定的勘探潜力,大量中-小油藏是今后勘探的主攻目标。分形方法作为油气资源评价的一种新途径,计算过程简便,结果可靠,应用前景广阔。 相似文献
2.
Haiyu Gao Zhuoheng Chen Kirk G. Osadetz Peter Hannigan Cameron Watson 《Mathematical Geology》2000,32(6):725-749
An approach is proposed to predict the spatial distributions of undiscovered petroleum resources. Each pool is parameterized as a marked-point. The independence chain of the Hastings algorithm is used to generate an appropriate structure for pool combinations in a play. Petroleum-bearing favorability estimated from geological observations is used to represent the sampling probabilities of pool locations. An objective function measuring the distance between characteristics of the realization and constraints is constructed from both the pool size distribution and entropy maximum criterion, in which the entropy criterion places all undiscovered pools in the most favorable positions. The geometrical convergence property of the proposed Hastings algorithm is presented. The method is illustrated by a case study from the Western Canada Sedimentary Basin. 相似文献
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Hystad Grethe Eleish Ahmed Hazen Robert M. Morrison Shaunna M. Downs Robert T. 《Mathematical Geosciences》2019,51(4):401-417
Recently, statistical distributions have been explored to provide estimates of the mineralogical diversity of Earth, and Earth-like planets. In this paper, a Bayesian approach is introduced to estimate Earth’s undiscovered mineralogical diversity. Samples are generated from a posterior distribution of the model parameters using Markov chain Monte Carlo simulations such that estimates and inference are directly obtained. It was previously shown that the mineral species frequency distribution conforms to a generalized inverse Gauss–Poisson (GIGP) large number of rare events model. Even though the model fit was good, the population size estimate obtained by using this model was found to be unreasonably low by mineralogists. In this paper, several zero-truncated, mixed Poisson distributions are fitted and compared, where the Poisson-lognormal distribution is found to provide the best fit. Subsequently, the population size estimates obtained by Bayesian methods are compared to the empirical Bayes estimates. Species accumulation curves are constructed and employed to estimate the population size as a function of sampling size. Finally, the relative abundances, and hence the occurrence probabilities of species in a random sample, are calculated numerically for all mineral species in Earth’s crust using the Poisson-lognormal distribution. These calculations are connected and compared to the calculations obtained in a previous paper using the GIGP model for which mineralogical criteria of an Earth-like planet were given.
相似文献5.
Undiscovered oil and gas assessments are commonly reported as aggregate estimates of hydrocarbon volumes. Potential commercial value and discovery costs are, however, determined by accumulation size, so engineers, economists, decision makers, and sometimes policy analysts are most interested in projected discovery sizes. The lognormal and Pareto distributions have been used to model exploration target sizes. This note contrasts the outcomes of applying these alternative distributions to the play level assessments of the U.S. Geological Survey's 1995 National Oil and Gas Assessment. Using the same numbers of undiscovered accumulations and the same minimum, medium, and maximum size estimates, substitution of the shifted truncated lognormal distribution for the shifted truncated Pareto distribution reduced assessed undiscovered oil by 16% and gas by 15%. Nearly all of the volume differences resulted because the lognormal had fewer larger fields relative to the Pareto. The lognormal also resulted in a smaller number of small fields relative to the Pareto. For the Permian Basin case study presented here, reserve addition costs were 20% higher with the lognormal size assumption. 相似文献
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Obtaining reliable hydrological input parameters is a key challenge in groundwater modeling. Although many quantitative characterization techniques exist, experience applying these techniques to highly heterogeneous real-world aquifers is limited. Three geostatistical characterization techniques are applied to the Edwards Aquifer, a limestone aquifer in south-central Texas, USA, for the purposes of quantifying the performance in an 88,000-cell groundwater model. The first method is a simple kriging of existing hydraulic conductivity data developed primarily from single-well tests. The second method involves numerical upscaling to the grid-block scale, followed by cokriging the grid-block conductivity. In the third method, the results of the second method are used to establish the prior distribution for a Bayesian updating calculation. Results of kriging alone are biased towards low values and fail to reproduce hydraulic heads or spring flows. The upscaling/cokriging approach removes most of the systematic bias. The Bayesian update reduced the mean residual by more than a factor of 10, to 6 m, approximately 2.5% of the total head variation in the aquifer. This agreement demonstrates the utility of automatic calibration techniques based on formal statistical approaches and lends further support for using the Bayesian updating approach for highly heterogeneous aquifers. 相似文献
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In this paper, a Bayesian approach for updating a semi-empirical model for predicting excavation-induced maximum ground settlement using centrifuge test data is presented. The Bayesian approach involves three steps: (1) prior estimate of the maximum ground settlement and model bias factor, (2) establishment of the likelihood function and posterior distribution of the model bias factor using the settlement measurement in the centrifuge test, and (3) development of posterior distribution of the predicted maximum settlement. This Bayesian approach is demonstrated with a case study of a well-documented braced excavation, and the results show that the accuracy of the maximum settlement prediction can be improved and the model uncertainty can be reduced with Bayesian updating. 相似文献
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A recently developed Bayesian interpolation method (BI) and its application to safety assessment of a flood defense structure
are described in this paper. We use a one-dimensional Bayesian Monte Carlo method (BMC) that has been proposed in (Rajabalinejad
2009) to develop a weighted logical dependence between neighboring points. The concept of global uncertainty is adequately explained
and different uncertainty association models (UAMs) are presented for linking the local and global uncertainty. Based on the
global uncertainty, a simplified approach is introduced. By applying the global uncertainty, we apply the Guassian error estimation
to general models and the Generalized Beta (GB) distribution to monotonic models. Our main objective in this research is to
simplify the newly developed BMC method and demonstrate that it can dramatically improve the simulation efficiency by using
prior information from outcomes of the preceding simulations. We provide theory and numerical algorithms for the BI method
geared to multi-dimensional problems, integrate it with a probabilistic finite element model, and apply the coupled models
to the reliability assessment of a flood defense for the 17th Street Flood Wall system in New Orleans. 相似文献
9.
松辽盆地北部的油气勘探程度虽然较高,但仍然有一些尚未发现的油气资源。本文在对预测油气资源的分形分布模型和截断帕雷托(TSP)分布模型两种统计模型进行系统研究的基础上,结合油气藏地质规律,通过预测该区未发现的油(气)田数量及其储量规模分布,认为方法可行,结果供参考,并待实践进一步验证。得出主要结论是:分形分布模型是一种预测油气田数量和储量(资源量)的可靠方法,在勘探程度较高的盆地或区带应用效果较好,而在勘探新区应用则有一定难度;截断帕雷托分布模型相对于分形分布模型不很完善,但应用该模型进行油气资源分布预测受具体盆地地质条件的影响较小,具有应用价值。 相似文献
10.
《地学前缘(英文版)》2018,9(6):1665-1677
Determining soilewater characteristic curve(SWCC) at a site is an essential step for implementing unsaturated soil mechanics in geotechnical engineering practice, which can be measured directly through various in-situ and/or laboratory tests. Such direct measurements are, however, costly and timeconsuming due to high standards for equipment and procedural control and limits in testing apparatus. As a result, only a limited number of data points(e.g., volumetric water content vs. matric suction)on SWCC at some values of matric suction are obtained in practice. How to use a limited number of data points to estimate the site-specific SWCC and to quantify the uncertainty(or degrees-of-belief) in the estimated SWCC remains a challenging task. This paper proposes a Bayesian approach to determine a site-specific SWCC based on a limited number of test data and prior knowledge(e.g., engineering experience and judgment). The proposed Bayesian approach quantifies the degrees-of-belief on the estimated SWCC according to site-specific test data and prior knowledge, and simultaneously selects a suitable SWCC model from a number of candidates based on the probability logic. To address computational issues involved in Bayesian analyses, Markov Chain Monte Carlo Simulation(MCMCS), specifically Metropolis-Hastings(M-H) algorithm, is used to solve the posterior distribution of SWCC model parameters, and Gaussian copula is applied to evaluating model evidence based on MCMCS samples for selecting the most probable SWCC model from a pool of candidates. This removes one key limitation of the M-H algorithm, making it feasible in Bayesian model selection problems. The proposed approach is illustrated using real data in Unsaturated Soil Database(UNSODA) developed by U.S. Department of Agriculture. It is shown that the proposed approach properly estimates the SWCC based on a limited number of site-specific test data and prior knowledge, and reflects the degrees-of-belief on the estimated SWCC in a rational and quantitative manner. 相似文献
11.
Most approaches in statistical spatial prediction assume that the spatial data are realizations of a Gaussian random field.
However, this assumption is hard to justify for most applications. When the distribution of data is skewed but otherwise has
similar properties to the normal distribution, a closed skew normal distribution can be used for modeling their skewness.
Closed skew normal distribution is an extension of the multivariate skew normal distribution and has the advantage of being
closed under marginalization and conditioning. In this paper, we generalize Bayesian prediction methods using closed skew
normal distributions. A simulation study is performed to check the validity of the model and performance of the Bayesian spatial
predictor. Finally, our prediction method is applied to Bayesian spatial prediction on the strain data near Semnan, Iran.
The mean-square error of cross-validation is improved by the closed skew Gaussian model on the strain data. 相似文献
12.
Stochastic simulation has been proven to be a useful tool for revealing uncertainties in petroleum exploration and exploitation. The application to petroleum resource assessment would result in predicted potential accumulations with geographic locations, a desirable feature for improving both resource management and exploration efficiency. The associated uncertainties with the prediction provide information useful for exploration risk analysis. This attempt has been encumbered by two typical technical difficulties: biased observation data and lack of information with respect to the undiscovered accumulation locations. In this paper we propose a model-based simulation approach, in which models are used to perform unbiased parameter estimation from biased data and to facilitate the location of undiscovered petroleum accumulations based on reasoning of available geological and geophysical observations. The Fourier transform algorithm is chosen for the simulation because the spatial correlation and location-specific features can be studied separately from different data sources and integrated in the simulation in a frequency domain. The proposed approach is illustrated by an example from the Rainbow petroleum play in the West Canadian Sedimentary Basin. In the application example, a pre-1994 exploration history data set was used as input, and the predictions are then checked against the locations of post-1993 exploratory drilling results. The comparison of the predictions from the proposed approach and the traditional conditional simulation shows that the model-based approach captures the essentials of geological controls on the spatial distribution of petroleum accumulation, thus improving the projections of undiscovered petroleum accumulations. 相似文献
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M. Power 《Mathematical Geology》1992,24(8):929-945
Economic filtration has been offered as an explanation of the observed lognormality in the size distribution of discovered oil and gas deposits. The result leads to the conclusion that one cannot impute the shape of the underlying parent distribution from the observed discoveries size distribution. The fact that the largest pools tend to be discovered early in the exploration history of an area of interest suggests the existence of an inherent sampling bias in the discovery process. The bias is influenced by the levels of geologic knowledge and technological sophistication. Furthermore, the existence of the bias leads to lognormality in the observed discoveries size distribution of oil and gas pools. A discovery process model explicitly incorporating the notion of sampling bias was applied to a series of Weibull parent frequency size distributions. The selected parent distributions are of a class suggested in the literature as more reflective of nature's size distribution and have empirical support. The distribution of discoveries resulting from the application of the model to the chosen parent size distributions were tested for lognormality using a chi-squared test. Lognormality was found to be an acceptable model of the discoveries size distribution over a wide range of resource exhaustion measures. When combined with the notion of economic filtration, sampling bias leads to the conclusion that one should not expect the lognormal distribution to accurately represent the underlying parent size distribution of oil and gas deposits. 相似文献
16.
自流井背斜构造T1j^4-1～T1j^3气藏已有150年的勘探开发史,气藏由高压高产统一气藏逐步变成各次高点的独立区块,天然气被围闭其内形成独立气藏.通过对自流井背斜构造西段张家山气藏的储集特征、控制因素的研究,探讨气藏的气水分布规律,寻找挖潜方案及新区开发. 相似文献
17.
The Bayesian bridge between simple and universal kriging 总被引：1，自引：0，他引：1
Kriging techniques are suited well for evaluation of continuous, spatial phenomena. Bayesian statistics are characterized by using prior qualified guesses on the model parameters. By merging kriging techniques and Bayesian theory, prior guesses may be used in a spatial setting. Partial knowledge of model parameters defines a continuum of models between what is named simple and universal kriging in geostatistical terminology. The Bayesian approach to kriging is developed and discussed, and a case study concerning depth conversion of seismic reflection times is presented. 相似文献
18.
《地学前缘(英文版)》2021,12(5)
Rock mechanical parameters and their uncertainties are critical to rock stability analysis, engineering design, and safe construction in rock mechanics and engineering. The back analysis is widely adopted in rock engineering to determine the mechanical parameters of the surrounding rock mass, but this does not consider the uncertainty. This problem is addressed here by the proposed approach by developing a system of Bayesian inferences for updating mechanical parameters and their statistical properties using monitored field data, then integrating the monitored data, prior knowledge of geotechnical parameters,and a mechanical model of a rock tunnel using Markov chain Monte Carlo(MCMC) simulation. The proposed approach is illustrated by a circular tunnel with an analytical solution, which was then applied to an experimental tunnel in Goupitan Hydropower Station, China. The mechanical properties and strength parameters of the surrounding rock mass were modeled as random variables. The displacement was predicted with the aid of the parameters updated by Bayesian inferences and agreed closely with monitored displacements. It indicates that Bayesian inferences combined the monitored data into the tunnel model to update its parameters dynamically. Further study indicated that the performance of Bayesian inferences is improved greatly by regularly supplementing field monitoring data. Bayesian inference is a significant and new approach for determining the mechanical parameters of the surrounding rock mass in a tunnel model and contributes to safe construction in rock engineering. 相似文献
19.
Erik Rüttener Juan José Egozcue Dieter Mayer-Rosa Stephan Mueller 《Natural Hazards》1996,14(2-3):165-178
Seismic hazard analysis is based on data and models, which both are imprecise and uncertain. Especially the interpretation of historical information into earthquake parameters, e.g. earthquake size and location, yields ambiguous and imprecise data. Models based on probability distributions have been developed in order to quantify and represent these uncertainties. Nevertheless, the majority of the procedures applied in seismic hazard assessment do not take into account these uncertainties, nor do they show the variance of the results. Therefore, a procedure based on Bayesian statistics was developed to estimate return periods for different ground motion intensities (MSK scale).Bayesian techniques provide a mathematical model to estimate the distribution of random variables in presence of uncertainties. The developed method estimates the probability distribution of the number of occurrences in a Poisson process described by the parameter . The input data are the historical occurrences of intensities for a particular site, represented by a discrete probability distribution for each earthquake. The calculation of these historical occurrences requires a careful preparation of all input parameters, i.e. a modelling of their uncertainties. The obtained results show that the variance of the recurrence rate is smaller in regions with higher seismic activity than in less active regions. It can also be demonstrated that long return periods cannot be estimated with confidence, because the time period of observation is too short. This indicates that the long return periods obtained by seismic source methods only reflects the delineated seismic sources and the chosen earthquake size distribution law. 相似文献
20.
Fragility curves (FCs) constitute an emerging tool for the seismic risk assessment of all elements at risk. They express the probability of a structure being damaged beyond a specific damage state for a given seismic input motion parameter, incorporating the most important sources of uncertainties, that is, seismic demand, capacity and definition of damage states. Nevertheless, the implementation of FCs in loss/risk assessments introduces other important sources of uncertainty, related to the usually limited knowledge about the elements at risk (e.g., inventory, typology). In this paper, within a Bayesian framework, it is developed a general methodology to combine into a single model (Bayesian combined model, BCM) the information provided by multiple FC models, weighting them according to their credibility/applicability, and independent past data. This combination enables to efficiently capture inter-model variability (IMV) and to propagate it into risk/loss assessments, allowing the treatment of a large spectrum of vulnerability-related uncertainties, usually neglected. As case study, FCs for shallow tunnels in alluvial deposits, when subjected to transversal seismic loading, are developed with two conventional procedures, based on a quasi-static numerical approach. Noteworthy, loss/risk assessments resulting from such conventional methods show significant unexpected differences. Conventional fragilities are then combined in a Bayesian framework, in which also probability values are treated as random variables, characterized by their probability density functions. The results show that BCM efficiently projects the whole variability of input models into risk/loss estimations. This demonstrates that BCM is a suitable framework to treat IMV in vulnerability assessments, in a straightforward and explicit manner. 相似文献