共查询到19条相似文献，搜索用时 125 毫秒
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对两种气象要素场数据分别用距离平方反比法、三次B样条和克里格（Kriging）法插值计算.比较了三种方法结果的差异和当计算场满足不同类型克里格数学假设前提下,普通克里格法（OK）与泛克里格法（UK）插值结果的异同.结果表明：克里格法的误差普遍偏小,且在插值区域峰值处克里格法的最大绝对误差和残差方差均可能较样条的小,说明只要充分了解研究区域特点,恰当选用参数,克里格法有可能得到优于样条的结果,而距离平方反比法和克里格法用全场数据插值不如使用局部数据插值的精度高,则表明内插计算具有局域性.同时还发现,虽然插值场是否满足克里格法假设对插值结果存在影响,但这种影响有时并不重要,它依赖于插值场的性质. 相似文献
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相带控制下协克里金方法孔隙度预测 总被引：1，自引：0，他引：1
针对东营凹陷南坡沙四段浊积岩砂岩储层纵横向物性变化剧烈的特点,在协克里金方法空间估计理论方法基础上,提出相带控制下协克里金孔隙度预测方法.从定量角度对沉积相加以刻画,在三维空间协同克里金估值计算时,使用相控因子对估值进行约束,使得结果能够更好地反映储层物性如孔隙度的空间展布特征.加入相带控制处理后,使变异函数较早达到基台值,减小理论模型拟合误差.利用该技术方法估算了沙四段第1小段的孔隙度,利用抽稀井进行对比分析,估算结果误差较小,预测值接近真实值,证实了该方法的有效性. 相似文献
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通过建立地质模型寻找地震属性参数来预测含油气性的方法不断发展并取得了较好的效果.但是对于海相沉积环境下受构造控制的复合油气藏的研究,常规的油气预测方法存在局限性、多解性,还无法准确识别有效含气层,因此探索适合该地区的油气预测技术有着重大意义.为此本文首先对物性参数敏感性孔隙度及渗透率作了定量分析,依据分频数据体对流体信息的高灵敏度,利用改进的广义S变换结合AVF关系同时在井数据约束下对孔隙度和渗透率进行了分频反演,获得了孔隙度和渗透率反演剖面,准确有效地刻画了含气储层.实验证明,分频反演方法对南海深水区珠江组储层进行精细预测,克服了常规反演在少井无井区无法有效预测的问题,且分频反演预测油气结果比常规方法吻合度要高,提高了深水区油气勘探的精度. 相似文献
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储层孔隙度、泥质含量及流体类型估测是地球物理勘探的重点及难点问题.基于岩石物理理论及等效模型,可以构建油气储层反射特征与储层孔隙度、泥质含量及流体的映射关系.本文从流体替换模型出发,结合矿物平均模型,首先推导了以孔隙度、泥质含量、流体模量和密度表征的非线性反射系数及弹性阻抗公式;然后根据推导的反射系数和弹性阻抗公式,建立了一套两步法反演策略：利用部分角度叠加数据进行线性反演预测弹性阻抗体;利用预测的弹性阻抗体开展泥质含量、孔隙度、流体模量和密度等变量的非线性反演,引入弹性阻抗对于反演变量的一阶和二阶导数以提高反演的精度.最后,利用层状模型验证了新推导的反射系数方程的精度,并分别利用测井数据模型生成的含噪声合成地震记录及实际工区地震数据验证了所提出的反演方法的可靠性. 相似文献
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储层物性参数是反映储层油气储集能力的重要参数,表征了不同地质时期的沉积特征.地球物理测井参数由深及浅反映了不同地质时期的声、放、电等沉积特征,因而测井参数和泥质含量(孔隙度)之间有很强非线性映射关系,并具有时间序列特征.充分利用多种测井参数预测储层泥质含量和孔隙度对于储层精细描述具有十分重要的意义.深度学习技术具有极强的数据结构挖掘能力,目前,全连接的深度神经网络已经在泥质含量预测进行了初步尝试并取得了较好的效果.而长短时记忆(LSTM)循环神经网络更适合解决序列化的数据问题,因此本文提出基于LSTM循环神经网络利用多种测井参数进行泥质含量和孔隙度预测的方法,预测结果的均方根误差比常规全连接深度神经网络分别下降了42.2%和48.6%,实际应用表明,对于具有序列化特性的泥质含量和孔隙度,LSTM循环神经网络预测的准确性和稳定性要明显优于常规全连接深度神经网络. 相似文献
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开展NBR油田地震优质储层预测,可有效支撑低效井区开发和外扩区储量动用,对油田产能建设有重要意义.本文针对地震反演存在叠后反演参数单一、叠前反演多解性强的问题,提出三步流程实现叠前叠后联合反演,达到优质储层定量表征目的.步骤一通过叠后地质统计学反演预测砂泥岩;步骤二开展岩石物理分析,明确了剪切模量为含油砂体敏感弹性参数,再以步骤一反演结果为约束,采用叠前扩展弹性阻抗反演(EEI)预测储层含油性;步骤三仍以步骤一结果为约束,采用高斯配置协模拟测井参数反演预测孔隙度、渗透率.最后按照优质储层物性界限,通过剪切模量、孔隙度和渗透率体得到优质储层三维表征体.上述方法在NBR油田A组油层中优质储层预测符合率达到78.3%,优选甜点区部署水平井效果显著. 相似文献
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复杂孔隙储层往往同时发育孔缝洞等多种孔隙类型,这种孔隙结构的复杂性使得岩石的速度与孔隙度之间的相关性很差.经典的二维岩石物理模版只研究弹性参数与孔隙度和饱和度之间的定量关系,而不考虑孔隙结构的影响,用这样的模版来预测复杂孔隙储层的物性参数时带来很大偏差.本文首先证明多重孔隙岩石的干骨架弹性参数可以用一个等效孔隙纵横比的单重孔隙岩石物理模型来模拟;进而基于等效介质岩石物理理论和Gassmann方程,建立一个全新的三维岩石物理模版,用它来建立复杂孔隙岩石的弹性性质与孔隙扁度及孔隙度和饱和度之间的定量关系;在此基础上,预测复杂储层的孔隙扁度、孔隙度以及孔隙中所包含的流体饱和度.实际测井和地震反演数据试验表明,三维岩石物理模版可有效提高复杂孔隙储层参数的预测精度. 相似文献
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Cross-well seismic reflection data, acquired from a carbonate aquifer at Port Mayaca test site near the eastern boundary of Lake Okeechobee in Martin County, Florida, are used to delineate flow units in the region intercepted by two wells. The interwell impedance determined by inversion from the seismic reflection data allows us to visualize the major boundaries between the hydraulic units. The hydraulic (flow) unit properties are based on the integration of well logs and the carbonate structure, which consists of isolated vuggy carbonate units and interconnected vug systems within the carbonate matrix. The vuggy and matrix porosity logs based on Formation Micro-Imager (FMI) data provide information about highly permeable conduits at well locations. The integration of the inverted impedance and well logs using geostatistics helps us to assess the resolution of the cross-well seismic method for detecting conduits and to determine whether these conduits are continuous or discontinuous between wells. A productive water zone of the aquifer outlined by the well logs was selected for analysis and interpretation. The ELAN (Elemental Log Analysis) porosity from two wells was selected as primary data and the reflection seismic-based impedance as secondary data. The direct and cross variograms along the vertical wells capture nested structures associated with periodic carbonate units, which correspond to connected flow units between the wells. Alternatively, the horizontal variogram of impedance (secondary data) provides scale lengths that correspond to irregular boundary shapes of flow units. The ELAN porosity image obtained by cokriging exhibits three similar flow units at different depths. These units are thin conduits developed in the first well and, at about the middle of the interwell separation region, these conduits connect to thicker flow units that are intercepted by the second well. In addition, a high impedance zone (low porosity) at a depth of about 275 m, after being converted to ELAN porosity, is characterized as a more confined low porosity structure. This continuous zone corresponds to a permeability barrier in the carbonate aquifer that separates the three connected conduits observed in the cokriging image. In the zones above and below this permeability barrier, the water production is very high, which agrees with water well observations at the Port Mayaca aquifer. 相似文献
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《国际泥沙研究》2023,38(1):128-140
The porosity of gravel riverbed material often is an essential parameter to estimate the sediment transport rate, groundwater-river flow interaction, river ecosystem, and fluvial geomorphology. Current methods of porosity estimation are time-consuming in simulation. To evaluate the relation between porosity and grain size distribution (GSD), this study proposed a hybrid model of deep learning Long Short-Term Memory (LSTM) combined with the Discrete Element Method (DEM). The DEM is applied to model the packing pattern of gravel-bed structure and fine sediment infiltration processes in three-dimensional (3D) space. The combined approaches for porosity calculation enable the porosity to be determined through real time images, fast labeling to be applied, and validation to be done. DEM outputs based on the porosity dataset were utilized to develop the deep learning LSTM model for predicting bed porosity based on the GSD. The simulation results validated with the experimental data then segregated into 800 cross sections along the vertical direction of gravel pack. Two DEM packing cases, i.e., clogging and penetration are tested to predict the porosity. The LSTM model performance measures for porosity estimation along the z-direction are the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) with values of 0.99, 0.01, and 0.01 respectively, which is better than the values obtained for the Clogging case which are 0.71, 0.14, and 0.03, respectively. The use of the LSTM in combination with the DEM model yields satisfactory results in a less complex gravel pack DEM setup, suggesting that it could be a viable alternative to minimize the simulation time and provide a robust tool for gravel riverbed porosity prediction. The simulated results showed that the hybrid model of the LSTM combined with the DEM is reliable and accurate in porosity prediction in gravel-bed river test samples. 相似文献
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The spatial variability of precipitation has often been a topic of research, since accurate modelling of precipitation is a crucial condition for obtaining reliable results in hydrology and geomorphology. In mountainous areas, the sparsity of the measurement networks makes an accurate and reliable spatialization of rainfall amounts at the local scale difficult. The purpose of this paper is to show how the use of a digital elevation model can improve interpolation processes at the subregional scale for mapping the mean annual and monthly precipitation from rainfall observations (40 years) recorded in a region of 1400 km2 in southern Italy. Besides linear regression of precipitation against elevation, two methods of interpolation are applied: inverse squared distance and ordinary cokriging. Cross‐validation indicates that the inverse distance interpolation, which ignores the information on elevation, yields the largest prediction errors. Smaller prediction errors are produced by linear regression and ordinary cokriging. However, the results seem to favour the multivariate geostatistical method including auxiliary information (related to elevation). We conclude that ordinary cokriging is a very flexible and robust interpolation method because it can take into account several properties of the landscape; it should therefore be applicable in other mountainous regions, especially where precipitation is an important geomorphological factor. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
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We investigate prediction abilities of different variants of kriging and different combinations of data in a local geometric (GNSS/leveling based) geoid modeling. In order to generate local geoid models, we have used GNSS/leveling data and EGM2008 geopotential model. EGM2008 has been used twofold. Firstly, it was used as a basic long wave-length trend to be removed from geoid undulation data to generate a residual field of geoid heights modeled later by kriging (remove-restore technique). Secondly, EGM2008-based undulations were used as a secondary variable in a cokriging prediction procedure (as pseudo-observations). Besides the use of EGM2008, the kriging-based local geometric geoid models were generated only on the basis of raw undulations data. Kriging itself was used in two variants, i.e. ordinary kriging and universal kriging for univariate and bivariate cases (cokriging). The quality of kriging-based prediction for all its variants and all data combinations have been investigated on one fixed validation dataset consisting of 86 points and three training data sets characterized by a different density of sampling. Results of this study indicate that incorporation of EGM08 as a long wave-length trend in kriging prediction procedure outperforms cokriging strategy based on incorporation of EGM08 as a secondary spatially correlated variable. 相似文献
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Prediction of effective porosity from seismic attributes using locally linear model tree algorithm 
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下载免费PDF全文 Ahmad Ashtari Talkhestani 《Geophysical Prospecting》2015,63(3):680-693
In this study, a locally linear model tree algorithm was used to optimize a neuro‐fuzzy model for prediction of effective porosity from seismic attributes in one of Iranian oil fields located southwest of Iran. Valid identification of effective porosity distribution in fractured carbonate reservoirs is extremely essential for reservoir characterization. These high‐accuracy predictions facilitate efficient exploration and management of oil and gas resources. The multi‐attribute stepwise linear regression method was used to select five out of 26 seismic attributes one by one. These attributes introduced into the neuro‐fuzzy model to predict effective porosity. The neuro‐fuzzy model with seven locally linear models resulted in the lowest validation error. Moreover, a blind test was carried out at the location of two wells that were used neither in training nor validation. The results obtained from the validation and blind test of the model confirmed the ability of the proposed algorithm in predicting the effective porosity. In the end, the performance of this neuro‐fuzzy model was compared with two regular neural networks of a multi‐layer perceptron and a radial basis function, and the results show that a locally linear neuro‐fuzzy model trained by a locally linear model tree algorithm resulted in more accurate porosity prediction than standard neural networks, particularly in the case where irregularities increase in the data set. The production data have been also used to verify the reliability of the porosity model. The porosity sections through the two wells demonstrate that the porosity model conforms to the production rate of wells. Comparison of the locally linear neuro‐fuzzy model performance on different wells indicates that there is a distinct discrepancy in the performance of this model compared with the other techniques. This discrepancy in the performance is a function of the correlation between the model inputs and output. In the case where the strength of the relationship between seismic attributes and effective porosity decreases, the neuro‐fuzzy model results in more accurate prediction than regular neural networks, whereas the neuro‐fuzzy model has a close performance to neural networks if there is a strong relationship between seismic attributes and effective porosity. The effective porosity map, presented as the output of the method, shows a high‐porosity area in the centre of zone 2 of the Ilam reservoir. Furthermore, there is an extensive high‐porosity area in zone 4 of Sarvak that extends from the centre to the east of the reservoir. 相似文献
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The variable elevation of the groundwater table in the St. Louis area was estimated using multiple linear regression (MLR), ordinary kriging, and cokriging as part of a regional program seeking to assess liquefaction potential. Surface water features were used to determine the minimum water table for MLR and supplement the principal variables for ordinary kriging and cokriging. By evaluating the known depth to the water and the minimum water table elevation, the MLR analysis approximates the groundwater elevation for a contiguous hydrologic system. Ordinary kriging and cokriging estimate values in unsampled areas by calculating the spatial relationships between the unsampled and sampled locations. In this study, ordinary kriging did not incorporate topographic variations as an independent variable, while cokriging included topography as a supporting covariable. Cross validation suggests that cokriging provides a more reliable estimate at known data points with less uncertainty than the other methods. Profiles extending through the dissected uplands terrain suggest that: (1) the groundwater table generated by MLR mimics the ground surface and elicits a exaggerated interpolation of groundwater elevation; (2) the groundwater table estimated by ordinary kriging tends to ignore local topography and exhibits oversmoothing of the actual undulations in the water table; and (3) cokriging appears to give the realistic water surface, which rises and falls in proportion to the overlying topography. The authors concluded that cokriging provided the most realistic estimate of the groundwater surface, which is the key variable in assessing soil liquefaction potential in unconsolidated sediments. 相似文献
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Effective prediction of scour downstream of ski-jump buckets using artificial neural networks 总被引：1，自引：0，他引：1
The main objective of this research was to analyze and quantify the uncertainty of artificial neural network in prediction of scour downstream ski-jump buckets. Hence, at first, three artificial neural network models were developed to predict depth, length, and width of scour hole. Then, Monte-Carlo simulation was applied in the estimates of artificial neural network modeling procedure. The uncertainties were quantified by means of two criteria: 95 percent prediction uncertainty and d-factor. The results of the artificial neural network models showed superior performance of it in comparison with some empirical formulas because of higher correlation coefficient (R 2 > 0.95) and lower error (RMSE < 1.63). The obtained result from uncertainty analysis of the models revealed the satisfactory performance of them. In this procedure it was clarified that the artificial neural network model for length prediction was more reliable than the others with d-factor and 95 percent prediction uncertainty equal to 2.53 and 92, respectively. 相似文献
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Artificial neural networks (ANNs) were developed to accurately predict highly time-variable specific conductance values in an unconfined coastal aquifer. Conductance values in the fresh water lens aquifer change in response to vertical displacements of the brackish zone and fresh water-salt water interface, which are caused by variable pumping and climate conditions. Unlike physical-based models, which require hydrologic parameter inputs, such as horizontal and vertical hydraulic conductivities, porosity, and fluid densities, ANNs can "learn" system behavior from easily measurable variables. In this study, the ANN input predictor variables were initial conductance, total precipitation, mean daily temperature, and total pumping extraction. The ANNs were used to predict salinity (specific conductance) at a single monitoring well located near a high-capacity municipal-supply well over time periods ranging from 30 d to several years. Model accuracy was compared against both measured/interpolated values and predictions were made with linear regression, and in general, excellent prediction accuracy was achieved. For example, although the average percent change of conductance over 90-d periods was 39%, the absolute mean prediction error achieved with the ANN was only 1.1%. The ANNs were also used to conduct a sensitivity analysis that quantified the importance of each of the four predictor variables on final conductance values, providing valuable insights into the dynamics of the system. The results demonstrate that the ANN technology can serve as a powerful and accurate prediction and management tool, minimizing degradation of ground water quality to the extent possible by identifying appropriate pumping policies under variable and/or changing climate conditions. 相似文献
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《Acta Geochimica》2021,(3)
Soil salinization is one of the most predominant environmental hazards responsible for agricultural land degradation, especially in the arid and semi-arid regions.An accurate spatial prediction and modeling of soil salinity in agricultural land are so important for farmers and decision-makers to develop the appropriate mechanisms to prevent the loss of fertile soil and increase crop production.El Outaya plain is marked by soil salinity increases due to the excessive use of poor groundwater quality for irrigation. This study aims to compare the performance of simple kriging, cokriging(SCOK), multilayer perceptron neural networks(MLP-NN), and support vector machines(SVM)in the prediction of topsoil and subsoil salinity. The field covariates including geochemical properties of irrigation groundwater and physical properties of soil and environmental covariates including digital elevation model and remote sensing derivatives were used as input candidates to SCOK, MLP-NN, and SVM. The optimal input combination was determined using multiple linear stepwise regression(MLSR). The results revealed that the SCOK using field covariates including water electrical conductivity(ECw) and sand percentage(sand %), and environmental covariates including land surface temperature(LST), topographic wetness index(TWI), and elevation could significantly increase the accuracy of soil salinity spatial prediction. The comparison of the prediction accuracy of the different modeling techniques using the Taylor diagram indicated that MLP-NN using LST, TWI, and elevation as inputs were more accurate in predicting the topsoil salinity [ECs(TS)] with a mean absolute error(MAE) of 0.43, root mean square error(RMSE) of 0.6 and correlation coefficient of 0.946. MLP-NN using ECw and sand % as inputs were more accurate in predicting the subsoil salinity [ECs(SS)] with MAE of 0.38, RMSE of0.6, and R of 0.968. 相似文献