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The investigation throughout the world in past two decades provides evidence which indicate that significance variation of radon and other soil gases occur in association with major geophysical events such as earthquake. The traditional statistical algorithm includes regression to remove the effect of the meteorological parameters from the raw radon and anomalies are calculated either taking the periodicity in seasonal variations or periodicity computed using Fast Fourier Transform. In case of neural networks the regression step is avoided. A neural network model can be found which can learn the behavior of radon with respect to meteorological parameter in order that changing emission patterns may be adapted to by the model on its own. The output of this neural model is the estimated radon values. This estimated radon value is used to decide whether anomalous behavior of radon has occurred and a valid precursor may be identified. The neural network model developed using Radial Basis function network gave a prediction rate of 87.7%. The same was accompanied by huge false alarms. The present paper deals with improved neural network algorithm using Probabilistic Neural Networks that requires neither an explicit step of regression nor use of any specific period. This neural network model reduces the false alarms to zero and gave same prediction rate as RBF networks. 相似文献
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摘要：通过铸体薄片、扫描电镜、电子背散射、阴极发光等技术手段结合物性资料以及沉积(微)相划分,对研究区沙一二段储集层成岩作用类型及点(薄片)、线(单井)、面(平面展布)成岩相分布及有利储层分布进行了研究。结果表明：研究区沙一二段受同沉积火成岩喷发和水解的影响,广泛发育栉壳状白云石、钾长石钠长石化、石英溶蚀等碱性条件下发生的成岩作用,同时层状分布的火山岩对于下覆地层起到了保护作用,减弱了下覆地层的压实作用;此外,受后期酸性流体的影响,研究区也发育诸如长石、岩屑溶蚀等酸性条件下发生的成岩作用,沙一二段储层发育溶蚀孔,占比达到567%(均值)。采用“(微相)砂体部位+主体成岩现象+孔隙类型”的复合命名方式,对薄片、单井及平面区域进行了成岩相的命名和划分,共划分为三类：水下分流水道砂体侧翼细粉砂岩-菱铁矿胶结-低孔成岩相、三角洲前缘-中细砂岩-欠压实-白云石包壳-溶孔相、火山熔岩远端凝灰质中细砂岩-凝灰质胶结-致密相。欠压实-白云石包壳-溶孔相在测井响应上具有DT低、RD与RS间隔幅度较大、CNCF与RHOZ较为接近或交叉的特点;菱铁矿胶结-低孔成岩相具有DT高、RD与RS较为接近、CNCF与RHOZ间隔幅度较大的特点,测井解释的油层、油水同层与欠压实-白云石包壳-溶孔相具有较好的匹配关系。三角洲前缘-中细砂-欠压实-白云石包壳-溶孔相为有利储层发育区带。 相似文献
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基于110口探井1 100 m岩心、500余口探井总长107 000 m火成岩井段资料,通过岩心观察、岩矿鉴定、物性测试和含油气性分析,研究了辽河坳陷东部凹陷新生界不同层系火成岩岩性、岩相发育特征和储层控制因素。结果显示：本区发育火山岩、侵入岩、沉火山碎屑岩3大类16种岩性,其中玄武岩类占91.0%;发育6相16种亚相,其中溢流相占63.0%;发育4类9种储集空间,次生孔隙和次生裂缝占主体。砾-粒间孔隙和裂缝发育的粗面岩类、玄武质火山碎屑（熔）岩和火山口-近火山口相带的侵出相、火山通道相和爆发相是最有利的储集岩性、岩相带。储层主要受岩性、岩相、断裂等多方面控制,岩性决定储集空间类型和储层微观特征,岩相控制储层宏观分布规律,断裂控制岩体分布范围、火山口位置和储层有效性。近油源、靠断裂的优势岩性、岩相带是火成岩油气勘探最有利目标。 相似文献
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深水水道是深水环境下的油气储集的重要场所。当前,深水水道的沉积构型及级次划分已有诸多报道,但对于单一水道分类研究较少。现有主流分类方案主要依据水道侵蚀能力,然而受地震分辨率及露头完整度影响,在实际应用中存在较大局限性。基于全球26个野外露头和西非尼日尔三角洲Akpo油田X油藏的钻井岩心资料,对不同重力流相类型进行流态学解释,将水道内部岩相简化为高密度浊流相(HTL)、低密度浊流相(LTL)以及碎屑流相(CL);在此基础上,根据不同重力流相在水道内部占比,划分出9种单一水道类型: 高密度浊流单一充填水道(HTL>70%)、低密度浊流单一充填水道(LTL>75%)、碎屑流单一充填水道(CL>60%)、块状砂质混合充填水道(HTL=40%~70%、LTL=40%~20%、CL<30%)、含砾砂质混合充填水道(HTL=40%~70%、LTL<30%、CL=15%~50%)、层状砂质混合充填水道(HTL=5%~60%、LTL=40%~75%、CL<20%)、夹碎屑砂质混合充填水道(HTL<40%、LTL=40%~75%、CL=20%~40%)、等相混合充填水道(HTL=20%~40%、LTL=20%~40%、CL=20%~40%)、含砂砾质混合充填水道(HTL<50%、LTL<60%、CL=40%~60%)。根据野外露头及划分方案在深水油田实际应用,综合分析认为不同类型水道在垂向分布具有一定规律,即碎屑流相充填的水道往往发育在水道体系底部,高密度浊流相充填的水道类型靠近水道中下部,而低密度浊流相充填的水道主要位于水道体系中上部。方案根据不同重力流相充填的百分比,对不同水道类型进行了明确定义,具有更好的适用性与可操控性,同时对于深水水道储集层预测及储集层质量评价具有重要的实际意义。 相似文献
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A stochastic channel embedded in a background facies is conditioned to data observed at wells. The background facies is a fixed rectangular box. The model parameters consist of geometric parameters that describe the shape, size, and location of the channel, and permeability and porosity in the channel and nonchannel facies. We extend methodology previously developed to condition a stochastic channel to well-test pressure data, and well observations of the channel thickness and the depth of the top of the channel. The main objective of this work is to characterize the reduction in uncertainty in channel model parameters and predicted reservoir performance that can be achieved by conditioning to well-test pressure data at one or more wells. Multiple conditional realizations of the geometric parameters and rock properties are generated to evaluate the uncertainty in model parameters. The ensemble of predictions of reservoir performance generated from the suite of realizations provides a Monte Carlo estimate of the uncertainty in future performance predictions. In addition, we provide some insight on how prior variances, data measurement errors, and sensitivity coefficients interact to determine the reduction in model parameters obtained by conditioning to pressure data and examine the value of active and observation well data in resolving model parameters. 相似文献
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以鄂尔多斯东缘A区块为例,通过岩石物理正演分析,论证了储层岩石物理参数的敏感性,确定利用纵波阻抗与纵横波速度比参数进行储层的识别。在此基础上进行正演模拟,研究了地震的极限分辨率及不同厚度砂体的地震响应特征,确定了运用叠前地质统计学反演为核心的储层预测技术对该研究区太原组进行储层预测研究,以解决该区储层薄、横向变化快、单一岩石物理参数无法区分岩性等问题。地质统计学反演结果表明,该方法能有效的预测厚度大于3 m的储层。对比研究区内11口测井解释厚度与储层反演厚度表明,储层反演预测厚度平均误差为7.5%,其中5口盲井的平均误差为10.2%,一口新钻井的砂体厚度预测误差为1.73%,为该区的井位部署提供了可靠的资料参考。 相似文献
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Azadeh Ahmadi Ali Moridi Elham Kakaei Lafdani Ghasem Kianpisheh 《Journal of Earth System Science》2014,123(7):1603-1618
Many of the applied techniques in water resources management can be directly or indirectly influenced by hydro-climatology predictions. In recent decades, utilizing the large scale climate variables as predictors of hydrological phenomena and downscaling numerical weather ensemble forecasts has revolutionized the long-lead predictions. In this study, two types of rainfall prediction models are developed to predict the rainfall of the Zayandehrood dam basin located in the central part of Iran. The first seasonal model is based on large scale climate signals data around the world. In order to determine the inputs of the seasonal rainfall prediction model, the correlation coefficient analysis and the new Gamma Test (GT) method are utilized. Comparison of modelling results shows that the Gamma test method improves the Nash–Sutcliffe efficiency coefficient of modelling performance as 8% and 10% for dry and wet seasons, respectively. In this study, Support Vector Machine (SVM) model for predicting rainfall in the region has been used and its results are compared with the benchmark models such as K-nearest neighbours (KNN) and Artificial Neural Network (ANN). The results show better performance of the SVM model at testing stage. In the second model, statistical downscaling model (SDSM) as a popular downscaling tool has been used. In this model, using the outputs from GCM, the rainfall of Zayandehrood dam is projected under two climate change scenarios. Most effective variables have been identified among 26 predictor variables. Comparison of the results of the two models shows that the developed SVM model has lesser errors in monthly rainfall estimation. The results show that the rainfall in the future wet periods are more than historical values and it is lower than historical values in the dry periods. The highest monthly uncertainty of future rainfall occurs in March and the lowest in July. 相似文献
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Dieu Tien Bui Tran Anh Tuan Harald Klempe Biswajeet Pradhan Inge Revhaug 《Landslides》2016,13(2):361-378
Preparation of landslide susceptibility maps is considered as the first important step in landslide risk assessments, but these maps are accepted as an end product that can be used for land use planning. The main objective of this study is to explore some new state-of-the-art sophisticated machine learning techniques and introduce a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods. The Son La hydropower basin (Vietnam) was selected as a case study. First, a landslide inventory map was constructed using the historical landslide locations from two national projects in Vietnam. A total of 12 landslide conditioning factors were then constructed from various data sources. Landslide locations were randomly split into a ratio of 70:30 for training and validating the models. To choose the best subset of conditioning factors, predictive ability of the factors were assessed using the Information Gain Ratio with 10-fold cross-validation technique. Factors with null predictive ability were removed to optimize the models. Subsequently, five landslide models were built using support vector machines (SVM), multi-layer perceptron neural networks (MLP Neural Nets), radial basis function neural networks (RBF Neural Nets), kernel logistic regression (KLR), and logistic model trees (LMT). The resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and several statistical evaluation measures. Additionally, Friedman and Wilcoxon signed-rank tests were applied to confirm significant statistical differences among the five machine learning models employed in this study. Overall, the MLP Neural Nets model has the highest prediction capability (90.2 %), followed by the SVM model (88.7 %) and the KLR model (87.9 %), the RBF Neural Nets model (87.1 %), and the LMT model (86.1 %). Results revealed that both the KLR and the LMT models showed promising methods for shallow landslide susceptibility mapping. The result from this study demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptibility mapping. 相似文献
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地震油藏地质研究及其在大港滩海地区的应用 总被引：2，自引：2，他引：0
针对滩海地区油藏评价阶段"井点少,以地震资料为主,面向水平井、大斜度井开发需要"的特点和要求,提出地震油藏地质研究的思路、方法和技术,其涉及的内容包括地震地层划分对比、地震构造精细解释、地震沉积相研究、地震储层预测、地震油藏流体识别以及油气分布的不均一性研究。以地震沉积学的原理及方法为指导,以单井相分析为基础,将地震高阶信息应用于沉积相平面分析;利用地层切片技术在地震数据体中提取等时地层单元信息,结合各种地震属性参数的分布研究不同地层单元的沉积相分布及其演化史。在对储集层特征进行重构基础上的随机模拟地震反演是储层表征与预测的有效方法。地震油藏地质研究的主要目的是揭示油藏的非均质性及油气分布的不均一性,进而较准确地评价油藏。实现这一目的的关键一方面是依据地质模式减少地震信息的多解性和提高地震信息的纵向分辨率;二是探索如何将地震信息与测井、录井、地质等多种信息真正融合起来,综合分析和解决地质问题。通过大港滩海地区地震油藏地质研究,发现研究区油气分布的不均一现象,这对认识复杂油藏具有重要意义。 相似文献
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An integrated geological-petrophysical analysis of the rudist-bearing sequence of the Cretaceous Sarvak Formation is given one giant oilfield, and provides an improved understanding of this main reservoir in the Abadan Plain, in the Zagros Basin, SW Iran. The main objective of this study is to evaluate reservoir potential of the Sarvak Formation, and then to utilize the calibrated well log signature to correlate reservoir potential in un-cored wells. Eight main facies are recognized and categori... 相似文献
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通过岩心观察并充分利用普通薄片、铸体薄片、阴极发光、X-衍射和扫描电镜等资料,对姬塬地区长8油层组储层的成岩作用、成岩矿物、成岩阶段和成岩演化序列特征等进行了研究。根据成岩作用类型及强度、成岩矿物等将储层划分为绿泥石衬边弱溶蚀、不稳定组分溶蚀、压实致密、高岭石充填和碳酸盐胶结5种成岩相。并通过岩心薄片资料刻度测井归纳出不同成岩相在GR、AC等测井曲线及其组合上的响应特征,由此实现各单井储层成岩相的连续划分。对长8油层组层序界面和基准面旋回与成岩相展布的关系进行了研究,结果表明层序界面附近不稳定组分溶蚀相及高岭石充填相较为发育,且各井之间对比性良好,同时层序界面也控制了碳酸盐胶结;中期基准面旋回的最大湖泛面处均发育井间可对比的压实致密相,长82中期基准面下降半旋回砂体一方面易于遭受溶蚀产生次生孔,另一方面由于受沉积驻留和碎屑组分影响导致压实强度较弱,因此物性总体比长81砂体好。 相似文献
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Using of neural networks for the prediction of nitrate groundwater contamination in rural and agricultural areas 总被引：1，自引：0，他引：1
Khamis Al-Mahallawi Jacky Mania Azzedine Hani Isam Shahrour 《Environmental Earth Sciences》2012,65(3):917-928
As a neural network provides a non-linear function mapping of a set of input variables into the corresponding network output,
without the requirement of having to specify the actual mathematical form of the relation between the input and output variables,
it has the versatility for modeling a wide range of complex non-linear phenomena. In this study, groundwater contamination
by nitrate, the ANNs are applied as a new type of model to estimate the nitrate contamination of the Gaza Strip aquifer. A
set of six explanatory variables for 139 sampled wells was used and that have a significant influence were identified by using
ANN model. The Multilayer Perceptrons (MLP), Radial Basis Function (RBF), Generalized Regression Neural Network (GRNN), and Linear Networks were used. The best network found to simulate Nitrate
was MLP with six input nodes and four hidden nodes. The input variables are: nitrogen load, housing density in 500-m radius
area surrounding wells, well depth, screen length, well discharge, and infiltration rate. The best network found had good
performance (regression ratio 0.2158, correlation 0.9773, and error 8.4322). Bivariate statistical test also were used and
resulting in considerable unexplained variation in nitrate concentration. Based on ANN model, groundwater contamination by
nitrate depends not on any single factor but on the combination of them. 相似文献
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地震多属性拟合技术在粗粒扇体沉积相图编制中的应用--以玛131井区百口泉组二段为例 总被引：1，自引：1，他引：0
由于砾岩岩石类型复杂,储层非均质性强,难以建立地震属性与储层之间的一一对应关系,导致应用地震属性常常具有不确定性和多解性。为了克服单一属性反映粗粒沉积展布的片面性,同类属性间相关信息的彼此干扰性,地震相预测扇体分布的盲目性,针对性的提出了基于地震多属性拟合技术编制粗粒扇体沉积相图的方法:1）建立岩性识别图版;2）属性优化与线性逐步回归拟合含砂砾率;3）均方根振幅属性正态分布约束砂体边界;4）应用拟合含砂砾率等值线图,结合岩相和测井相编制粗粒扇体沉积相图。并将该方法用于玛131井区百口泉组二段的沉积研究中,为研究粗粒扇体沉积提供了新的思路,为玛湖滚动勘探及井位论证提供了可靠地质依据。 相似文献
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莫里青油田从2008年开始一直坚持勘探与开发一体化,在油藏增产增效方面取得了重大突破。储集层呈典型的岩性油藏特征,多期多物源沉积物的叠加,导致该油田岩性变化快,储层非均质性强,在2011年之前随着生产井数量的增加,产量不断上升,但是之后随着压力亏空,能力补充不及时,目前产油呈下降趋势,加之岩性油藏的隐蔽性和复杂性,使开发遇到了瓶颈。从油田开发效果来看,为了有效改善和提高莫里青油田开采效率,必须加强地质认识,在此基础上进行高效的生产。在区域沉积背景下,通过大量岩心观察与描述,从沉积物形成条件、物源方向以及分选性对莫里青油田的沉积模式进行识别,提出了斜坡裙沉积模式。在沉积模式指导下,基于单井砂体统计基础,识别出3种沉积亚相,并进一步细分出5种微相类型。 相似文献