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1.
3D inversion of DC data using artificial neural networks 总被引：2，自引：0，他引：2
Ahmad Neyamadpour W. A. T. Wan Abdullah Samsudin Taib Danesh Niamadpour 《Studia Geophysica et Geodaetica》2010,54(3):465-485
In this paper, we investigate the applicability of artificial neural networks in inverting three-dimensional DC resistivity
imaging data. The model used to produce synthetic data for training the artificial neural network (ANN) system was a homogeneous
medium of resistivity 100 Ωm with an embedded anomalous body of resistivity 1000 Ωm. The different sizes for anomalous body
were selected and their location was changed to different positions within the homogeneous model mesh elements. The 3D data
set was generated using a finite element forward modeling code through standard 3D modeling software. We investigated different
learning paradigms in the training process of the neural network. Resilient propagation was more efficient than any other
paradigm. We studied the effect of the data type used on neural network inversion and found that the use of location and the
apparent resistivity of data points as the input and corresponding true resistivity as the output of networks produces satisfactory
results. We also investigated the effect of the training data pool volume on the inversion properties. We created several
synthetic data sets to study the interpolation and extrapolation properties of the ANN. The range of 100–1000 Ωm was divided
into six resistivity values as the background resistivity and different resistivity values were also used for the anomalous
body. Results from numerous neural network tests indicate that the neural network possesses sufficient interpolation and extrapolation
abilities with the selected volume of training data. The trained network was also applied on a real field dataset, collected
by a pole-pole array using a square grid (8 ×8) with a 2-m electrode spacing. The inversion results demonstrate that the trained
network was able to invert three-dimensional electrical resistivity imaging data. The interpreted results of neural network
also agree with the known information about the investigation area. 相似文献
2.
P. R. Sutcliffe 《Annales Geophysicae》1997,15(10):1257-1264
The pattern recognition capabilities of artificial neural networks (ANNs) have for the first time been used to identify Pi2 pulsations in magnetometer data, which in turn serve as indicators of substorm onsets and intensifications. The pulsation spectrum was used as input to the ANN and the network was trained to give an output of +1 for Pi2 signatures and –1 for non-Pi2 signatures. In order to evaluate the degree of success of the neural-network procedure for identifying Pi2 pulsations, the ANN was used to scan a number of data sets and the results compared with visual identification of Pi2 signatures. The ANN performed extremely well with a success rate of approximately 90% for Pi2 identification and a timing accuracy generally within 1 min compared to visual identification. A number of potential applications of the neural-network Pi2 scanning procedure are discussed. 相似文献
3.
基于青藏高原多年冻土区三个钻孔的地球物理测井数据和钻孔编录资料,我们对多年冻土厚度和多年冻土层内地下冰与地球物理测井数据之间的关系进行了相关的分析研究.研究表明,当地层为土壤类型时,可以使用井径和侧向测井曲线来判断多年冻土层厚度;而当地层为致密的基岩时,不能使用上述两种测井曲线来判断多年冻土层厚度.此外,还可以使用长源距伽马-伽马曲线和侧向测井曲线来识别多年冻土层内部分地下冰层的位置,其前提条件是地下冰层具有一定的厚度,或即使厚度较薄,但连续出现.这一研究结果对于利用地球物理测井曲线来调查多年冻土情况具有一定的应用价值. 相似文献
4.
In many areas of the world, the presence of shallow high velocity, highly heterogeneous layers complicate seismic imaging of deeper reflectors. Of particular economic interest are areas where potentially hydrocarbon-bearing strata are obscured by layers of basalt. Basalt layers are highly reflective and heterogeneous. Using reflection seismic, top basalt is typified by a high-amplitude, coherent reflector with poor resolution of reflectors below the basalt, and even bottom basalt. Here, we present a new approach to the imaging problem using the pattern recognition abilities of a back-propagation Artificial Neural Network (ANN). ANNs are computational systems that attempt to mimic natural biological neural networks. They have the ability to recognize patterns and develop their own generalizations about a given data set. Back-propagation neural networks are trained on data sets for which the solution is known and tested on the data that are not previously presented to the ANN in order to validate the network result. We show that Artificial Neural Networks, due to their pattern recognition capabilities, can invert the medium statistics based on the seismic character. We produce statistically defined models involving a basalt analogous layer, and calculate full wavefield finite difference synthetic seismograms. We vary basalt layer thickness and source frequency to generate a synthetic model that produces seismic that is similar to real sub-basalt seismic, i.e. high amplitude top basalt reflector and the absence of base basalt and sub-basalt events. Using synthetic shot gathers, generated in a synthetic representation of the sub-basalt case, we can invert the velocity medium standard deviation by using an ANN. By inverting the velocity medium standard deviation, we successfully identified the transition from basalt to sub-basalt on the synthetic shot gathers. We also show that ANNs are capable of identifying the basalt to sub-basalt transition in the presence of incoherent noise. This is important for any future applications of this technique to the real-world seismic data, as this data is never completely noise-free. There is always a certain level of residual (noise remaining after initial noise filtering) environmental/ambient noise present on the recorded seismics, hence, neural network training with noise-free synthetic seismic is less than optimal. 相似文献
5.
《中国科学D辑(英文版)》2008,(Z2)
A full 3-D finite element method numerical modeling program is written based on the principle and technical specification of borehole electric image well logging tool. The response of well logging is computed in the formation media model with a single fracture. The effect of changing fracture aperture and resistivity ratio to the logging response is discussed. The identification ability for two parallel fractures is also present. A quantitative evaluation formula of fracture aperture from borehole electric image logging data is set up. A case study of the model well is done to verify the accuracy of the for-mula. The result indicates that the formula is more accurate than the foreign one. 相似文献
6.
Laura Carmine Elsa Aristodemou Christopher Pain Ann Muggeridge Cassiano de Oliveira 《Geophysical Prospecting》2008,56(1):115-140
The purpose of this work was to investigate a new and fast inversion methodology for the prediction of subsurface formation properties such as porosity, salinity and oil saturation, using time‐dependent nuclear well logging data. Although the ultimate aim is to apply the technique to real‐field data, an initial investigation as described in this paper, was first required; this has been carried out using simulation results from the time‐dependent radiation transport problem within a borehole. Simulated neutron and γ‐ray fluxes at two sodium iodide (NaI) detectors, one near and one far from a pulsed neutron source emitting at ～14 MeV, were used for the investigation. A total of 67 energy groups from the BUGLE96 cross section library together with 567 property combinations were employed for the original flux response generation, achieved by solving numerically the time‐dependent Boltzmann radiation transport equation in its even parity form. Material property combinations (scenarios) and their correspondent teaching outputs (flux response at detectors) are used to train the Artificial Neural Networks (ANNs) and test data is used to assess the accuracy of the ANNs. The trained networks are then used to produce a surrogate model of the expensive, in terms of computational time and resources, forward model with which a simple inversion method is applied to calculate material properties from the time evolution of flux responses at the two detectors. The inversion technique uses a fast surrogate model comprising 8026 artificial neural networks, which consist of an input layer with three input units (neurons) for porosity, salinity and oil saturation; and two hidden layers and one output neuron representing the scalar photon or neutron flux prediction at the detector. This is the first time this technique has been applied to invert pulsed neutron logging tool information and the results produced are very promising. The next step in the procedure is to apply the methodology to real data. 相似文献
7.
对于裂缝、溶蚀孔洞发育的碳酸盐岩缝洞储层,如何从测井资料中提取裂缝、溶蚀孔洞信息是评价储层有效性的关键问题.为了从电成像测井静态图像上准确地分割出清晰的裂缝、溶蚀孔洞子图像进而提取其参数信息,本文提出了一种基于小波变换模极大值图像分割技术的电成像测井资料缝洞面孔率提取方法.以钮扣电极电导率曲线为对象,先消除井壁凹凸不平导致的地层背景噪声的影响,利用小波变换模极大值图像分割方法得到包含裂缝和溶蚀孔洞目标的子图像,根据子图像提取裂缝-孔洞总面孔率、裂缝面孔率、孔洞面孔率等信息.本文利用塔里木盆地奥陶系碳酸盐岩地层的电成像测井数据提取了缝洞面孔率参数,还利用同井岩心CT扫描图像计算的平均缝洞面孔率、双侧向电阻率、常规测井资料三孔隙度模型计算的相对连通缝洞孔隙度进行了对比,并进行了试油验证.对比表明,电成像测井裂缝-孔洞总面孔率、裂缝面孔率、孔洞面孔率与岩心CT扫描图像平均缝洞面孔率、双侧向电阻率、相对连通缝洞孔隙度、试油结果均有较好的一致性.这一方面验证了采用本文方法提取的裂缝-孔洞总面孔率、裂缝面孔率、孔洞面孔率的合理性,另一方面表明所提取参数可用于指示缝洞型碳酸盐岩储层的渗透性和有效性. 相似文献
8.
为研究井周裂缝发育特征,本文提出一种新型方位侧向测井方法,利用三维有限元法,模拟裂缝的方位侧向测井响应.结果显示,深浅侧向电阻率幅度差异受裂缝倾角的控制,低角度缝为负差异,高角度缝为正差异;倾斜裂缝张开度的增大使测井响应值减小,方位电阻率差异增大;井周方位电阻率可反映裂缝方位产状,单一缝或裂缝密度较小时,沿裂缝走向的方位电阻率小,沿裂缝倾向的方位电阻率大;裂缝发育地层的测井响应显示宏观各向异性特征,但方位电阻率的差异显示发生反转现象,即沿裂缝走向/层理方向的方位电阻率大,沿裂缝倾向/垂直层理方向的方位电阻率小;对方位电阻率测井响应进行井周成像,直观显示了裂缝的产状和发育特征. 相似文献
9.
Imaging Pathways in Fractured Rock Using Three‐Dimensional Electrical Resistivity Tomography 
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下载免费PDF全文 Judith Robinson Lee Slater Timothy Johnson Allen Shapiro Claire Tiedeman Dimitrios Ntarlagiannis Carole Johnson Frederick Day‐Lewis Pierre Lacombe Thomas Imbrigiotta John Lane 《Ground water》2016,54(2):186-201
Major challenges exist in delineating bedrock fracture zones because these cause abrupt changes in geological and hydrogeological properties over small distances. Borehole observations cannot sufficiently capture heterogeneity in these systems. Geophysical techniques offer the potential to image properties and processes in between boreholes. We used three‐dimensional cross borehole electrical resistivity tomography (ERT) in a 9 m (diameter) × 15 m well field to capture high‐resolution flow and transport processes in a fractured mudstone contaminated by chlorinated solvents, primarily trichloroethylene. Conductive (sodium bromide) and resistive (deionized water) injections were monitored in seven boreholes. Electrode arrays with isolation packers and fluid sampling ports were designed to enable acquisition of ERT measurements during pulsed tracer injections. Fracture zone locations and hydraulic pathways inferred from hydraulic head drawdown data were compared with electrical conductivity distributions from ERT measurements. Static ERT imaging has limited resolution to decipher individual fractures; however, these images showed alternating conductive and resistive zones, consistent with alternating laminated and massive mudstone units at the site. Tracer evolution and migration was clearly revealed in time‐lapse ERT images and supported by in situ borehole vertical apparent conductivity profiles collected during the pulsed tracer test. While water samples provided important local information at the extraction borehole, ERT delineated tracer migration over spatial scales capturing the primary hydrogeological heterogeneity controlling flow and transport. The fate of these tracer injections at this scale could not have been quantified using borehole logging and/or borehole sampling methods alone. 相似文献
10.
An ANN application for water quality forecasting 总被引：12，自引：0，他引：12
Rapid urban and coastal developments often witness deterioration of regional seawater quality. As part of the management process, it is important to assess the baseline characteristics of the marine environment so that sustainable development can be pursued. In this study, artificial neural networks (ANNs) were used to predict and forecast quantitative characteristics of water bodies. The true power and advantage of this method lie in its ability to (1) represent both linear and non-linear relationships and (2) learn these relationships directly from the data being modeled. The study focuses on Singapore coastal waters. The ANN model is built for quick assessment and forecasting of selected water quality variables at any location in the domain of interest. Respective variables measured at other locations serve as the input parameters. The variables of interest are salinity, temperature, dissolved oxygen, and chlorophyll-a. A time lag up to 2Deltat appeared to suffice to yield good simulation results. To validate the performance of the trained ANN, it was applied to an unseen data set from a station in the region. The results show the ANN's great potential to simulate water quality variables. Simulation accuracy, measured in the Nash-Sutcliffe coefficient of efficiency (R(2)), ranged from 0.8 to 0.9 for the training and overfitting test data. Thus, a trained ANN model may potentially provide simulated values for desired locations at which measured data are unavailable yet required for water quality models. 相似文献
11.
We have correlated the longitudinal unit conductance CL obtained from interpreted vertical electrical sounding data with the formation resistivity Rt and the formation resistivity factor F, obtained by carrying out electrical borehole logging. Interpreted geophysical data of eleven soundings and two electrical borehole log records are used for the analysis. The geophysical data used were acquired in a sedimentary basin. The study area is called Lower Maner Basin located in the province of Andhra Pradesh, India. Vertical electrical soundings were carried out using a Schlumberger configuration with half current electrode separation varying from 600–1000 m. For logging the two boreholes, a Widco logger‐model 3200 PLS was used. True formation resistivity Rt was calculated from a resistivity log. Formation resistivity factor F was also calculated at various depths using Rt values. An appreciable inverse relation exists between the correlated parameters. The borehole resistivity Rt and the formation resistivity factor F decrease with the increase in the longitudinal unit conductance CL. We have shown the use of such a relation in computing borehole resistivity Rt and formation resistivity factor F at sites that posses only vertical electrical sounding data, with a fair degree of accuracy. Validation of the correlation is satisfactory. Scope for updating the correlation is discussed. Significance and applications of the relation for exploration of groundwater, namely to update the vertical electrical sounding data interpretation by translating the vertical electrical sounding data into electrical borehole log parameters, to facilitate correlations studies and to estimate the porosity (φ), permeability (K) and water saturation Sw of water bearing zones are discussed. 相似文献
12.
Fracture identification is important for the evaluation of carbonate reservoirs. However, conventional logging equipment has small depth of investigation and cannot detect rock fractures more than three meters away from the borehole. Remote acoustic logging uses phase-controlled array-transmitting and long sound probes that increase the depth of investigation. The interpretation of logging data with respect to fractures is typically guided by practical experience rather than theory and is often ambiguous. We use remote acoustic reflection logging data and high-order finite-difference approximations in the forward modeling and prestack reverse-time migration to image fractures. First, we perform forward modeling of the fracture responses as a function of the fracture–borehole wall distance, aperture, and dip angle. Second, we extract the energy intensity within the imaging area to determine whether the fracture can be identified as the formation velocity is varied. Finally, we evaluate the effect of the fracture–borehole distance, fracture aperture, and dip angle on fracture identification. 相似文献
13.
庐枞盆地砖桥科学钻探ZK01孔为深部探测技术与实验研究专项在庐枞盆地施工的钻探验证孔,全井段实施了连续取心和地球物理测井工作.测井工作分三次完成,测井总深度1994.02m.测井项目包括视电阻率、极化率、磁化率、纵波速度、超声成像、自然伽马、密度、井斜、井径、井温、泥浆电阻率、井中三分量磁测等10多种方法,获得了钻孔剖面原位物性参数、钻孔几何形态及井壁超声图像.通过对地球物理测井和钻孔岩心编录等资料的研究,完成了岩性的人工识别与支持向量机判别,建立了钻孔测井解释岩性剖面;通过对矿化地层的测井响应分析,将电阻率和磁化率作为粗安岩矿化的识别标识;根据超声成像测井资料推断本地区深部地层最大水平主应力方向为南北走向.在ZK01孔1500~1900m发现放射性异常,对铀当量大于万分之一的21处异常进行了定量解释,铀矿化段累积厚度93.02m,为庐枞地区深部找铀矿提供了重大线索. 相似文献
14.
K. H. Hamed A. E. Hassan 《Stochastic Environmental Research and Risk Assessment (SERRA)》2000,14(6):428-448
An efficient numerical solution for the two-dimensional groundwater flow problem using artificial neural networks (ANNs)
is presented. Under stationary velocity conditions with unidirectional mean flow, the conductivity realizations and the head
gradients, obtained by a traditional finite difference solution to the flow equation, are given as input-output pairs to train
a neural network. The ANN is trained successfully and a certain level of recognition of the relationship between input conductivity
patterns and output head gradients is achieved. The trained network produced velocity realizations that are physically plausible
without solving the flow equation for each of the conductivity realizations. This is achieved in a small fraction of the time
necessary for solving the flow equations. The prediction accuracy of the ANN reaches 97.5% for the longitudinal head gradient
and 94.7% for the transverse gradient. Head-gradient and velocity statistics in terms of the first two moments are obtained
with a very high accuracy. The cross covariances between head gradients and the fluctuating log-conductivity (log-K) and between velocity and log-K obtained with the ANN approach match very closely those obtained by a traditional numerical solution. The same is true for
the velocity components auto-covariances. The results are also extended to transport simulations with very good accuracy.
Spatial moments (up to the fourth) of mean-concentration plumes obtained using ANNs are in very good agreement with the traditional
Monte Carlo simulations. Furthermore, the concentration second moment (concentration variance) is very close between the two
approaches. Considering the fact that higher moments of concentration need more computational effort in numerical simulations,
the advantage of the presented approach in saving long computational times is evident. Another advantage of the ANNs approach
is the ability to generalize a trained network to conductivity distributions different from those used in training. However,
the accuracy of the approach in cases with higher conductivity variances is being investigated. 相似文献
15.
B. R. Spies 《Surveys in Geophysics》1996,17(4):517-556
Electrical and electromagnetic geophysical techniques have reached a high level of technological sophistication since they were first used in boreholes less than one hundred years ago. Borehole logging-the detailed determination of rock and fluid properties adjacent to the borehole, and borehole geophysics-extending the range of geophysical investigation large distances away from the borehole, are essential for exploration, assessment and production of earth resources, as well as for fundamental studies of the earth. Borehole electrical and electromagnetic methods incorporate 17 decades of the electromagnetic spectrum, from 1000-s geomagnetic studies, through resistivity and permittivity measurements, to high-resolution resistivity imaging, NMR and optical spectroscopy. 相似文献
16.
Magnetotelluric inversion for azimuthally anisotropic resistivities employing artificial neural networks 总被引：1，自引：0，他引：1
An extension of an artificial neural network (ANN) approach to solve the magnetotelluric (MT) inverse problem for azimuthally anisotropic resistivities is presented and applied for a real dataset. Three different model classes, containing general 1-D and 2-D azimuthally anisotropic features, have been considered. For each model class, characteristics of three-layer feed forward ANNs trained through an error back propagation algorithm have been adjusted to approximate the inverse modeling function. It appears that, at least for synthetic models, reasonable results would be obtained by applying the amplitudes of the complex impedance tensor elements as inputs. Furthermore, the Levenberg-Marquart algorithm possesses optimal performance as a learning paradigm for this problem. The evaluation of applicability of the trained ANNs for unknown data sets excluded from the learning procedure reveals that the trained ANNs possess acceptable interpolation and extrapolation abilities to estimate model parameters accurately. This method was also successfully used for a field dataset wherein anisotropy had been previously recognized. 相似文献
17.
声反射成像测井中常用的基于射线理论的Kirchhoff积分偏移算法和基于单程波理论的F-K偏移算法均可实现井旁缝洞反射体的快速偏移成像,但其仅适用于地层垂向变化较弱的速度场和高陡角裂缝的偏移成像,无法实现低角度反射体的准确偏移归位,产生偏移假象误导测井解释.逆时偏移基于全波动方程,可适应强垂向变化速度场,实现近似水平反射体的偏移成像.本文详细分析了将逆时偏移应用于声反射成像测井时存在的数据准备、时间采样间隔匹配和成像条件改进等若干问题,通过设置多组理论模型来说明算法对井旁不同反射体的识别能力.模拟资料和实际资料处理结果证实,较F-K偏移算法,逆时偏移算法成像精度更高、收敛性更好,可有效实现近似水平构造偏移归位.改进的归一化互相关成像条件可解决深部地层的远井壁成像衰减问题,降低测井解释的多解性.逆时偏移将成为声反射成像测井高精度偏移技术的发展方向. 相似文献
18.
Neural computing has moved beyond simple demonstration to more significant applications. Encouraged by recent developments in artificial neural network (ANN) modelling techniques, we have developed committee machine (CM) networks for converting well logs to porosity and permeability, and have applied the networks to real well data from the North Sea. Simple three‐layer back‐propagation ANNs constitute the blocks of a modular system where the porosity ANN uses sonic, density and resistivity logs for input. The permeability ANN is slightly more complex, with four inputs (density, gamma ray, neutron porosity and sonic). The optimum size of the hidden layer, the number of training data required, and alternative training techniques have been investigated using synthetic logs. For both networks an optimal number of neurons in the hidden layer is in the range 8–10. With a lower number of hidden units the network fails to represent the problem, and for higher complexity overfitting becomes a problem when data are noisy. A sufficient number of training samples for the porosity ANN is around 150, while the permeability ANN requires twice as many in order to keep network errors well below the errors in core data. For the porosity ANN the overtraining strategy is the suitable technique for bias reduction and an unconstrained optimal linear combination (OLC) is the best method of combining the CM output. For permeability, on the other hand, the combination of overtraining and OLC does not work. Error reduction by validation, simple averaging combined with range‐splitting provides the required accuracy. The accuracy of the resulting CM is restricted only by the accuracy of the real data. The ANN approach is shown to be superior to multiple linear regression techniques even with minor non‐linearity in the background model. 相似文献
19.
利用反射声场探测井外地质构造的声波远探测方法已逐渐成为石油测井的一门重要应用技术.在远探测测井中声源常采用单极、偶极和四极等.为了考察各种声学辐射器的辐射性能,本文从辐射声场和井孔声场的能流密度出发,提出了一种利用辐射器向井外辐射的能量与沿井筒传播的能量比的大小来评价其辐射效率的方法,考察了不同声源激发的沿井传播的导波能流、地层辐射波能流及声源辐射效率随频率的变化规律.计算结果表明:不同声源的辐射效率和优势激发频段各不相同.单极声源在低频下的辐射效率很低,因为此时声源激发的能量几乎全部被斯通利波带走,相比之下,偶极声源在低频时的辐射效率远大于单极声源.本文的结果说明了偶极声源作为低频远探测声源要优于单极声源. 相似文献
20.
This paper evaluates the feasibility of using an artificial neural network (ANN) methodology for estimating the groundwater levels in some piezometers placed in an aquifer in north‐western Iran. This aquifer is multilayer and has a high groundwater level in urban areas. Spatiotemporal groundwater level simulation in a multilayer aquifer is regarded as difficult in hydrogeology due to the complexity of the different aquifer materials. In the present research the performance of different neural networks for groundwater level forecasting is examined in order to identify an optimal ANN architecture that can simulate the piezometers water levels. Six different types of network architectures and training algorithms are investigated and compared in terms of model prediction efficiency and accuracy. The results of different experiments show that accurate predictions can be achieved with a standard feedforward neural network trained usung the Levenberg–Marquardt algorithm. The structure and spatial regressions of the ANN parameters (weights and biases) are then used for spatiotemporal model presentation. The efficiency of the spatio‐temporal ANN (STANN) model is compared with two hybrid neural‐geostatistics (NG) and multivariate time series‐geostatistics (TSG) models. It is found in this study that the ANNs provide the most accurate predictions in comparison with the other models. Based on the nonlinear intrinsic ANN approach, the developed STANN model gives acceptable results for the Tabriz multilayer aquifer. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献