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1.
The analysis of well logging data plays key role in the exploration and development of hydrocarbon reservoirs. Various well log parameters such as porosity, gamma ray, density, transit time and resistivity, help in classification of strata and estimation of the physical, electrical and acoustical properties of the subsurface lithology. Strong and conspicuous changes in some of the log parameters associated with any particular geological stratigraphy formation are function of its composition, physical properties that help in classification. However some substrata show moderate values in respective log parameters and make difficult to identify the kind of strata, if we go by the standard variability ranges of any log parameters and visual inspection. The complexity increases further with more number of sensors involved. An attempt is made to identify the kinds of stratigraphy from well logs over Prydz bay basin, East Antarctica using fuzzy inference system. A model is built based on few data sets of known stratigraphy and further the network model is used as test model to infer the lithology of a borehole from their geophysical logs, not used in simulation. Initially the fuzzy based algorithm is trained, validated and tested on well log data and finally identifies the formation lithology of a hydrocarbon reservoir system of study area. The effectiveness of this technique is demonstrated by the analysis of the results for actual lithologs and coring data of ODP Leg 188. The fuzzy results show that the training performance equals to 82.95% while the prediction ability is 87.69%. The fuzzy results are very encouraging and the model is able to decipher even thin layer seams and other strata from geophysical logs. The result provides the significant sand formation of depth range 316.0- 341.0 m, where core recovery is incomplete.  相似文献   

2.
Porosity, the void portion of reservoir rocks, determines the volume of hydrocarbon accumulation and has a great control on assessment and development of hydrocarbon reservoirs. Accurate determination of porosity from core analysis is highly cost, time, and labor intensive. Therefore, the mission of finding an accurate, fast and cheap way of determining porosity is unavoidable. On the other hand, conventional well log data, available in almost all wells contain invaluable implicit information about the porosity. Therefore, an intelligent system can explicate this information. Fuzzy logic is a powerful tool for handling geosciences problem which is associated with uncertainty. However, determination of the best fuzzy formulation is still an issue. This study purposes an improved strategy, called hybrid genetic algorithm–pattern search (GA–PS) technique, against the widely held subtractive clustering (SC) method for setting up fuzzy rules between core porosity and petrophysical logs. Hybrid GA–PS technique is capable of extracting optimal parameters for fuzzy clusters (membership functions) which consequently results in the best fuzzy formulation. Results indicate that GA–PS technique manipulates both mean and variance of Gaussian membership functions contrary to SC that only has a control on mean of Gaussian membership functions. A comparison between hybrid GA–PS technique and SC method confirmed the superiority of GA–PS technique in setting up fuzzy rules. The proposed strategy was successfully applied to one of the Iranian carbonate reservoir rocks.  相似文献   

3.
岩性识别是认识地层及求解储层参数的基础,受地质环境复杂性和非均质性影响,测井曲线间存在着大量的信息冗余,数据集类间分布不平衡,常用的分类算法无法满足实际需求.针对常用分类算法容错性差,识别岩性单一和无法有效解决类间不平衡的问题,本文改进合成少数过采样技术(Synthetic Minority Over Sampling Technique,SMOTE)来处理数据集,可得到类间平衡的新数据集,并提出一种新的模糊隶属度函数改进模糊孪生支持向量机,在北美Hugoton油气田实际测井数据的基础上,用改进多分类孪生支持向量(Improve Multi Class Twin Support Vector Machine,IMCTSVM)综合自然伽马(GR)、电阻率(RL)、光电效应(PE)、中子密度孔隙度差异(DPHI)和平均中子密度孔隙度(PHIND)五种测井参数,以及相对位置(RELPOS)和非海洋/海洋指标(NM_M)两种地质约束变量,识别出9种岩性.将识别结果与传统支持向量机、深度神经网络等方法进行对比与分析,发现IMCTSVM算法优于上述两种分类算法,取得了较好的识别效果.  相似文献   

4.
Different parameters obtained through well-logging geophysical sensors such as SP, resistivity, gamma–gamma, neutron, natural gamma and acoustic, help in identification of strata and estimation of the physical, electrical and acoustical properties of the subsurface lithology. Strong and conspicuous changes in some of the log parameters associated with any particular stratigraphy formation, are function of its composition, physical properties and help in classification. However some substrata show moderate values in respective log parameters and make difficult to identify or assess the type of strata, if we go by the standard variability ranges of any log parameters and visual inspection. The complexity increases further with more number of sensors involved.An attempt is made to identify the type of stratigraphy from borehole geophysical log data using a combined approach of neural networks and fuzzy logic, known as Adaptive Neuro-Fuzzy Inference System. A model is built based on a few data sets (geophysical logs) of known stratigraphy of in coal areas of Kothagudem, Godavari basin and further the network model is used as test model to infer the lithology of a borehole from their geophysical logs, not used in simulation. The results are very encouraging and the model is able to decipher even thin cola seams and other strata from borehole geophysical logs. The model can be further modified to assess the physical properties of the strata, if the corresponding ground truth is made available for simulation.  相似文献   

5.
Based on the genetic algorithms (GAs), a fuzzy sliding mode control (FSMC) method for the building structure is designed in this research. When a fuzzy logic control method is used for a structural system, it is hard to get proper control rules directly, and to guarantee the stability and robustness of the fuzzy control system. Generally, the fuzzy controller combined with sliding mode control is applied, but there is still no criterion to reach an optimal design of the FSMC. In this paper, therefore, we design a fuzzy sliding mode controller for the building structure control system as an optimization problem and apply the optimal searching algorithms and GAs to find the optimal rules and membership functions of the FSMC. The proposed approach has the merit to determine the optimal structure and the inference rules of fuzzy sliding mode controller simultaneously. It is found that the building structure under the proposed control method could sustain in safety and stability when the system is subjected to external disturbances. Copyright © 2002 John Wiley & Sons, Ltd.  相似文献   

6.
The main goal of this study was to research the answer to two important questions in flow modeling; i) how to optimally design the cross‐section of an open channel for a given flow, and ii) in the case of selecting the fuzzy method for modeling, how to construct the membership functions (MFs) and fuzzy rules (FRs) such that the system yields the best results. The first question is answered in order to minimize difficulties in excavation and related costs by using the appropriate flow velocity. To provide the best answer researchers use several methods. The second question is answered in order to minimize model error. For this aim, there are many algorithms proposed by researchers in the literature. In this paper, the fuzzy logic method was used for open canal flow modeling. Furthermore, a simple membership function and fuzzy rule generation technique (SMRGT) is introduced, and used for fuzzy modeling. Two fuzzy models, each for different cross‐sectional shape, are presented in this study as an application of SMRGT. The comparison depends on various statistics, mean absolute relative error, and contour maps showed that the fuzzy models were successful in open channel flow modeling and SMRGT is useful for MF (membership function) and FR (fuzzy rule) generation.  相似文献   

7.
Analysis of reservoir water quality using fuzzy synthetic evaluation   总被引：12，自引：0，他引：12  
A general methodology for fuzzy synthetic evaluation is developed and illustrated with a case study of trophic status assessment for Fei-Tsui Reservoir in Taiwan. The historical data base was collected from the management agency of Fei-Tsui Reservoir from 1987 to 1996. In fuzzy synthetic evaluation, the classification is determined by a matrix operation of the weighted vector with the fuzzy evaluation matrix. After all individual membership functions of evaluated factors have been determined, the fuzzy evaluation matrix can be established. The weighted vector is determined by the analytic hierarchy process method (AHP). The results of this investigation show that the long-term change of water quality and the overturn phenomena cannot be observed with the Carlson index from 1987 to 1992 but is expressed by fuzzy synthetic evaluation. Fuzzy synthetic evaluation is better suited than the Carlson index to rating the trophic status of self-sustaining lakes. Interpretation of the results can provide valuable information to decision makers and aid reservoir management.  相似文献   

8.
Utilizing the rainfall intensity, and slope data, a fuzzy logic algorithm was developed to estimate sediment loads from bare soil surfaces. Considering slope and rainfall as input variables, the variables were fuzzified into fuzzy subsets. The fuzzy subsets of the variables were considered to have triangular membership functions. The relations among rainfall intensity, slope, and sediment transport were represented by a set of fuzzy rules. The fuzzy rules relating input variables to the output variable of sediment discharge were laid out in the IF-THEN format. The commonly used weighted average method was employed for the defuzzification procedure.The sediment load predicted by the fuzzy model was in satisfactory agreement with the measured sediment load data. Predicting the mean sediment loads from experimental runs, the performance of the fuzzy model was compared with that of the artificial neural networks (ANNs) and the physics-based models. The results of showed revealed that the fuzzy model performed better under very high rainfall intensities over different slopes and over very steep slopes under different rainfall intensities. This is closely related to the selection of the shape and frequency of the fuzzy membership functions in the fuzzy model.  相似文献   

9.
Accurate forecasting of hydrological time‐series is a quite important issue for a wise and sustainable use of water resources. In this study, an adaptive neuro‐fuzzy inference system (ANFIS) approach is used to construct a time‐series forecasting system. In particular, the applicability of an ANFIS to the forecasting of the time‐series is investigated. To illustrate the applicability and capability of an ANFIS, the River Great Menderes, located in western Turkey, is chosen as a case study area. The advantage of this method is that it uses the input–output data sets. A total of 5844 daily data sets collected from 1985 to 2000 are used for the time‐series forecasting. Models having various input structures were constructed and the best structure was investigated. In addition, four various training/testing data sets were built by cross‐validation methods and the best data set was obtained. The performance of the ANFIS models in training and testing sets was compared with observations and also evaluated. In order to get an accurate and reliable comparison, the best‐fit model structure was also trained and tested by artificial neural networks and traditional time‐series analysis techniques and the results compared. The results indicate that the ANFIS can be applied successfully and provide high accuracy and reliability for time‐series modelling. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

10.
An important constraint for the inference of mantle viscosity is the variation of the Holocene relative sea-level (RSL) height (with respect to today) following the last deglaciation. As a measure of this variation, sea-level indicators (SLIs) related to the RSL heights at specific past time epochs are used. For the inversion of the RSL-height change in terms of mantle viscosity, neighbouring SLIs may be grouped into an RSL diagram taken as representative for the region considered. Usually, the nominal height and age of a particular SLI are the only characteristics considered when determining the former RSL height. However, only SLIs based on isolation basins yield a narrow range for this height, whereas SLIs based on fossil samples provide a lower bound (shells), an upper bound (driftwood) or a finite interval (basal peat) for it. To also use fossil samples objectively, we develop a classification scheme of the depositional conditions based on fuzzy logic. After the definition of appropriate membership functions, this method leads to a systematic interpretation of the large number of SLIs available. We apply this method to SLIs from the Richmond-Gulf region, southeastern Hudson Bay, near the former glaciation center of Canada and derive a decay time of 5 ka for the exponential function best fitting the RSL diagram for this region.  相似文献   

11.
The complexity of most geological and geophysical problems prompts sometimes the use of non linear mathematical methods to handle them. An adaptive neuro fuzzy inference system (ANFIS) that combines fuzzy logic with neural networks, is applied here to study a paleoclimate section from the Quaternary sedimentary fill of the Lake Mucubají (western Venezuela). The purpose of this work is to find a set of numerical relationships that could predict the possible connections between oxygen isotope (δ18O) values from two different locations in the northern hemisphere (Ammersee in southern Germany and an ice core from the Greenland Ice Core Project — GRIP) and rock-magnetic parameters measured in Mucubají samples (i.e. mass-specific magnetic susceptibility — χ, magnetic remanence S-ratio, mass-specific saturation isothermal remanent magnetization — SIRM and anhysteretic remanent magnetization — ARM). The best inferences in terms of coefficient of determionation R2 and the Root Mean-Square Error (RMSE) are obtained using those magnetic data as input that include information about magnetite grain size distributions, e.g., SIRM and ARM in FIS structures [1χ, 4ARM] and [4ARM, 1SIRM]. A comparison between Ammersee and GRIP actual data, as well as their corresponding inferences for the FIS structure [4ARM, 1SIRM], reveals a reasonable good inference of global trends for both records, overlooking the regional and/or local paleoclimate forcings that might have affected Ammersee. A better correlation between global isotope paleoclimate records and magnetic proxies, is perhaps prevented by the role played by local and regional paleoclimate and tectonism in Mucubají. We also argue that the ratio of ARM over SIRM appears to be related in a complex way to the onset and to the end of the Younger Dryas. Our novel approach to the assessment of a specific paleoclimate case study shows the potential of the ANFIS technique in solving problems where traditional univariate and multivariate linear regression methods could prove inadequate.  相似文献   

12.
Two types of fuzzy inference systems (FIS) are used for predicting municipal water consumption time series. The FISs used include an adaptive neuro-fuzzy inference system (ANFIS) and a Mamdani fuzzy inference systems (MFIS). The prediction models are constructed based on the combination of the antecedent values of water consumptions. The performance of ANFIS and MFIS models in training and testing phases are compared with the observations and the best fit model is identified according to the selected performance criteria. The results demonstrated that the ANFIS model is superior to MFIS models and can be successfully applied for prediction of water consumption time series.  相似文献   

13.
The Neuro Fuzzy System (NFS) is a hybrid algorithm that combines fuzzy logic with neural networks. Since it can be used as a pattern recognition technique, we explore its potential to characterize the major lithological units encompassed by the first 512 m of the Colombian stratigraphic well Saltarin 1A (Guayabo and León Formations). Thus, we employ the NFS to infer the magnetic remanence S-ratio using bulk magnetic susceptibility (κ), κ-normalized saturation isothermal magnetization (SIRM κ) and/or volume of shale (Vsh) obtained from a gamma-ray log. The best results in terms of their corresponding Root Mean-Square Error (RMSE) values, throughout most of the upper Guayabo Formation, where magnetite seems to be an important magnetic phase, are attained with logκ and SIRM κ as input variables. Beyond 350 m downcore, the quality of the inference decreases over the León Formation, characterized by a significant presence of pyrrhotite. However, the extra input variable Vsh adjusts the inferred S-ratio to their experimental counterparts throughout this formation suggesting that the early diagenesis process that led to the formation of dispersed clay in these samples was also responsible for the formation of pyrrhotite. Hence the inclusion of manifold input data increases the ability of the net to predict S-ratio in complex geological settings with a sequence of changing lithologies, varying amounts and types of magnetic minerals, and different distributions of mineral grain sizes. In case these variables do not properly infer the actual S-ratio data, the extent of the different lithostratigraphic units would be still identifiable in some cases by the uneven quality of the correlation observed between inferred and experimental values.  相似文献   

14.
This paper introduces the dynamic neuro-fuzzy local modeling system (DNFLMS) that is based on a dynamic Takagi–Sugeno (TS) type fuzzy inference system with on-line and local learning algorithm for complex dynamic hydrological modeling tasks. Our DNFLMS is aimed to implement a fast training speed with the capability of on-line simulation where model adaptation occurs at the arrival of each new item of hydrological data. The DNFLMS applies an on-line, one-pass, training procedure to create and update fuzzy local models dynamically. The extended Kalman filtering algorithm is then implemented to optimize the parameters of the consequence part of each fuzzy model during the training phase. Local generalization in the DNFLMS is employed to optimize the parameters of each fuzzy model separately, region-by-region, using subsets of training data rather than all training data.  相似文献   

15.
针对塔里木盆地库车坳陷北部侏罗系烃源岩受泥浆污染、分析数据少、样品分布不连续及类型多样等问题突出,利用测井资料来对有机地球化学参数进行评价难度大,无法满足勘探需求.本文阐述了不同类型烃源岩的有机质级别、测井响应特征、有机地球化学参数定量计算及品质评价等多种方法,来研究富集区烃源岩对致密气储层的持续供气能力.文中介绍了煤层、碳质泥岩、暗色泥岩等3种类型烃源岩的有机质级别和测井响应特征.首先,提出分不同岩性烃源岩的ΔlogR法,其计算结果与铀曲线相关法、多元回归法对比,效果最好;再次建立了生烃潜率、氯仿沥青“A”及镜质体发射率等多个有机质地球化学参数的测井评价模型;最终,考虑能够反映烃源岩性质的参数来综合定义烃源岩品质指数,形成了完善的烃源岩测井综合评价方法研究技术.研究表明,利用这套地球物理技术提供了烃源岩定量评价和品质分类研究方法及实际应用案例.  相似文献   

16.
In this study, several types of adaptive network‐based fuzzy inference system (ANFIS) with different membership functions (MFs) and artificial neural network (ANN) were employed to predict hourly photochemical oxidants that were oxidizing substances such as ozone and peroxiacetyl nitrate produced by photochemical reactions. The results indicated that ANFIS statistically outperforms ANN in terms of hourly oxidant prediction. The minimum mean absolute percentage errors (MAPEs) of 4.99% could be achieved using ANFIS with bell shaped MFs. The maximum correlation coefficient, the minimum mean square errors, and the minimum root mean square errors were 0.99, 0.15, and 0.39, respectively. ANFIS's architecture consists of both ANN and fuzzy logic including linguistic expression of MFs and if‐then rules, so it can overcome the limitations of traditional neural network and increase the prediction performance.  相似文献   

17.
Probabilistic-fuzzy health risk modeling   总被引：3，自引：2，他引：1  
Health risk analysis of multi-pathway exposure to contaminated water involves the use of mechanistic models that include many uncertain and highly variable parameters. Currently, the uncertainties in these models are treated using statistical approaches. However, not all uncertainties in data or model parameters are due to randomness. Other sources of imprecision that may lead to uncertainty include scarce or incomplete data, measurement error, data obtained from expert judgment, or subjective interpretation of available information. These kinds of uncertainties and also the non-random uncertainty cannot be treated solely by statistical methods. In this paper we propose the use of fuzzy set theory together with probability theory to incorporate uncertainties into the health risk analysis. We identify this approach as probabilistic-fuzzy risk assessment (PFRA). Based on the form of available information, fuzzy set theory, probability theory, or a combination of both can be used to incorporate parameter uncertainty and variability into mechanistic risk assessment models. In this study, tap water concentration is used as the source of contamination in the human exposure model. Ingestion, inhalation and dermal contact are considered as multiple exposure pathways. The tap water concentration of the contaminant and cancer potency factors for ingestion, inhalation and dermal contact are treated as fuzzy variables while the remaining model parameters are treated using probability density functions. Combined utilization of fuzzy and random variables produces membership functions of risk to individuals at different fractiles of risk as well as probability distributions of risk for various alpha-cut levels of the membership function. The proposed method provides a robust approach in evaluating human health risk to exposure when there is both uncertainty and variability in model parameters. PFRA allows utilization of certain types of information which have not been used directly in existing risk assessment methods.  相似文献   

18.
Prediction of factors affecting water resources systems is important for their design and operation. In hydrology, wavelet analysis (WA) is known as a new method for time series analysis. In this study, WA was combined with an artificial neural network (ANN) for prediction of precipitation at Varayeneh station, western Iran. The results obtained were compared with the adaptive neural fuzzy inference system (ANFIS) and ANN. Moreover, data on relative humidity and temperature were employed in addition to rainfall data to examine their influence on precipitation forecasting. Overall, this study concluded that the hybrid WANN model outperformed the other models in the estimation of maxima and minima, and is the best at forecasting precipitation. Furthermore, training and transfer functions are recommended for similar studies of precipitation forecasting.  相似文献   

19.
Producing accurate seismic hazard map and predicting hazardous areas is necessary for risk mitigation strategies. In this paper, a fuzzy logic inference system is utilized to estimate the earthquake potential and seismic zoning of Zagros Orogenic Belt. In addition to the interpretability, fuzzy predictors can capture both nonlinearity and chaotic behavior of data, where the number of data is limited. In this paper, earthquake pattern in the Zagros has been assessed for the intervals of 10 and 50 years using fuzzy rule-based model. The Molchan statistical procedure has been used to show that our forecasting model is reliable. The earthquake hazard maps for this area reveal some remarkable features that cannot be observed on the conventional maps. Regarding our achievements, some areas in the southern (Bandar Abbas), southwestern (Bandar Kangan) and western (Kermanshah) parts of Iran display high earthquake severity even though they are geographically far apart.  相似文献   

20.
Decision‐making in reservoir operation has become easy and understandable with the use of fuzzy logic models, which represent the knowledge in terms of interpretable linguistic rules. However, the improvement in interpretability with increase in number of fuzzy sets (‘low’, ‘high’, etc) comes with the disadvantage of increase in number of rules that are difficult to comprehend by decision makers. In this study, a clustering‐based novel approach is suggested to provide the operators with a limited number of most meaningful operating rules. A single triangular fuzzy set is adopted for different variables in each cluster, which are fine‐tuned with genetic algorithm (GA) to meet the desired objective. The results are compared with the multi fuzzy set fuzzy logic model through a case study in the Pilavakkal reservoir system in Tamilnadu State, India. The results obtained are highly encouraging with a smaller set of rules representing the actual fuzzy logic system. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

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