جستجوی مقالات مرتبط با کلیدواژه « خوشه بندی » در نشریات گروه « زمین شناسی »

Asmari carbonate formation in Qale Nar oil field is one of the most important oil reservoirs in Dezful embayment. This reservoir has a lot of heterogeneity due to intensive changes in the sedimentary environment and diagenetic processes, which makes it very difficult to predict the petrophysical properties of the rock in reservoir. Creating a logical connection between sedimentary and electrical facieses, it can be effective in better understanding the reservoir and reducing uncertainties. In this study, using petrographic data and core description in six wells, 7 limy facies associations and one coarse crystalline dolomite were identified. Based on the petrophysical well logs of gamma, neutron, density, acoustic and photoelectric, and using Geolog software by MRGC algorithm, 5 electrofacieses (from 1 to 5) of the reservoir were determined. Electrofacies 1 has the best and electrofacies 5 has the worst reservoir quality. Examination of the frequency of electrofacies shows that good and very good electrofacieses make 30% of the Asmari reservoir of Qale Nar oilfield.

Rivers, as the living artery of the watershed, have played a decisive establishment of civilizations. But today, large amounts of pollutants enter the aquatic ecosystems, due to overpopulation and the expansion of industrial and agricultural activities. Among these, heavy elements are of special importance due to their stability, non-degradability, toxicity and bioaccumulation potential. Therefore, in this study, potential toxic elements ( AS, Cd, Cr, Ni, Cu, Pb, Zn) were investigated in order to determine the concentration, contamination factor and degree of contamination, geo-accumulation index, Enrichment and to detect the level of pollution caused by them. For this purpose, 21 surface sediment samples were collected based on the distribution of point and non-point pollution sources and river morphology. Samples were conditioned and concentration of toxic elements and sulfur were measured using ICP-MS at dry weight with accuracy of mg/kg and percentage of total organic carbon. Using enrichment factor and sediment quality standards (ISQGs, PEL, SEL), the level of contamination caused by toxic elements was detected. Hierarchical clustering analysis (AHC) with pearson similarity coefficient was used to identify metal elements with similar behavior in river sedimentary environments and the most important cationic adsorbent such as organic matter and clay particles using XLSTAT2018 software

Various sources and methods used to identify different zones of a field that can investigate different zones and layers of the reservoir. The purpose of this study is to determine electrofacies and HFUs in the Kangan and upper Dalan Formations in one of the southern Iranian gas fields. The importance of determining and investigate the facies with the well quality and separation of this facies from other facies causes better knowledge of quality zones and also due to the use of available information can save time and money. In this study use well log, the concept of clustering and the MRGC method in Geolog software, the log petrophysical data were clustered and finally, 5 electrofacies were identified. Among these identified facies, electrofacies No. 4 with dolomite lithology was identified as the best reservoir zone due to high effective porosity and low shale volume. After determining electrofacies by using the aforementioned methods, HFUs were identified with core data. Finally, after determining the HFUs, to investigate the relationship between electrofacies and HFUs, the cross sections of wells and HFUs are placed next to the designated electrofacies. After examining depth to depth, a high correlation was observed between them. This result shows that, this electrofacies model can be used in all wells in this field.

The study of temporal and spatial variations and the estimation of groundwater fluctuations in researching and planning water resources management are important for continuation or development of exploitation.So far, various models have been used to estimate the groundwater level parameter. Stochastic models have been used more than the other models for hydrological studies. The most common stochastic model is autoregressive integrated moving average model (ARIMA). This model is based on the Markov chain theory.In previous studies, one or a few wells have been studied using stochastic models but in this research, 46 selected stations with the monthly data of the Tabriz catchment were used to analyze temporal and spatial variations of groundwater level.In this case study, there was a good correlation between the values of the groundwater level of wells near each other than the other wells, thus to avoid the effects of closed wells fluctuations, at first the whole stations are divided into seven sections using the clustering process, thenspatial studies done just for selected station in every section. The results indicate that the first, third and seventh clusters didn't have acceptable accuracy in simulation due to the throw data and four other clusters had good accuracy. Also, the second and first clusters had the best and worst modeling with maximum R-Square and minimum RMSE, respectively.The use of the Arima model on various stations in the Tabriz catchment area revealed that the accuracy of the model is reduced when the time series have more fluctuations with trends in the mean and variance that lead to unsteady series.

Geomechanical studies have important applications in various topics such as wellbore stability, well completion, well orientation, hydraulic fracturing plans and operations, sand production and hydrocarbon fields subsidence. It is necessary to prepare earth mechanical model of the well in the field. In addition, one of the necessary subjects to prepare mechanical earth model (MEM) is providing continuous rock mechanical parameters in the well. Rock mechanical parameters change by any variation in lithology. In this study, rock mechanical parameters are provided in continuous form, for Faraghan, Zakeen and Sarchahan formations for a field in the Persian Gulf and these parameters are clustered. Clustering resulted in recognizing six clusters with various rock mechanical characteristics. Petrographic study (i.e. determining facies, cementation and diagenesis) recognized five facies with different petrographic and cementation characteristics. These facies include quartz arenite and arkosic sandstones, shales, red mudstone and carbonates. A correlation between sedimentary and geomechanical facies was found. According to petrographic and geomechanical studies, the studied interval was divided into 7 sections. Accordingly, variation of rock mechanical parameters with regard to change in lithology was investigated. In addition, the impact of rock composition, cementation and compaction changes on rock mechanical parameters were evaluated as well.

Extreme cold and ice ages Are The major risks of climate Usually annually in damages to crops and plants as well. Synoptic statistical analysis to predict the occurrence of frost and before the correct decision, and it helps them to deal with. The study aims to identify and analyze the West Synoptic cold waves, collect data, daily minimum temperature stations during the statistical period 1961 to 2010 for the cold months (December to March) was. Using statistical indicators of long-term mean daily minimum temperature, Numerical threshold was cold waves and cold waves were identified on the basis of the 56-day combined. Cluster analysis of data and the level of 500 hPa SLP for 56 days, three patterns of atmospheric circulation there. In each model, the main cause of heat waves in the sea level pressure with a central pressure of 1032 hPa core exposure to the northwest, southeast of the North West and West of the country.
The anticyclonic circulation over the top of his loss very cold air over the region. At the level of 500 hPa deep trough is located in the western part of the region which fall within the northern cold air down through the long, cold waves is the main factor. Meridian flow westerly winds and currents of the North Important role in the occurrence of cold temperatures, resulting in the loss of cold air blowing across the top of the West. In most stations gust of cold temperatures aggravated by the high altitude region.

The analysis of seismic facies is a technique for mapping geological features and properties using seismic data. To analyze seismic facies, seismic attributes are categorized and classified using machine learning algorithms to identify different seismic facies. Seismic facies analysis due to the nature of seismic data, which always has a degree of uncertainty, can produce different results with even small changes in input parameters of the analyzing method. For this reason, it is necessary to select the different stages of analysis, including the selection of input parameters and algorithm of machine learning, with high accuracy with regard to the objective of the seismic facies analysis. In this study, an interactive method with the supervision of interpreter is proposed for producing seismic facies map, using the optimal selection of the input parameters and the the proper selection of clustering and classification algorithms. In this method, the interpreter in a recursive and rotational process can compare the results of the analysis and generate thr optimal results by changing the input parameters. The method presented in this article is implemented in SeisART software. SeisART has a complete environment for data initialization (importing seismic data and well data). A user-friendly interactive environment allows the user to implement several methods and monitor the corresponding result in 2D and 3D.
SeisART software makes the possibility of the interpreter contribution in the whole stages of seismic facies analysis procedure. The interpreter can select the input attributes and chose the proper methods of pattern recognition to reach the best possible result. In the software, various evaluation utilities have been provided in each stage of seismic facies analysis. These utilities allow the interpreter to monitor the results of each method quantitatively and qualitatively. In the unsupervised system, clustering quality factors are used. The interpreter calculates the validation indices for different methods of clustering and identifies the proper method which has been more successful in discovering the natural grouping of patterns in the data set. Afterward, if there is structural geology information about the horizon of interest, the interpreter can decide on the clustering result with more accuracy. In the supervised system, the most proper method is feasible using minimization of training data and validation data errors. In this case, the interpreter can use geological knowledge and well data information to verify obtained results. In this method, the interpreter can obtain different results by changing the input parameters. Comparing these results, and taking into account the path leading to this result, the interpreter gains more knowledge of existing facies.
This method has been applied to the MSF4 horizons of the 3D seismic data of the North Sea F3 and has been shown which method is more efficient for different purposes

In recent years, fuzzy partitioning cluster algorithms have become more popular. Similar to crisp partitioning clustering, fuzzy algorithms can be used to integrate multiple models into a single zonal multiparameter model by grouping samples in a multidimensional space into clusters. The concept of partial cluster memberships integrates the structural heterogeneity of all input models and describes it in a fuzzy sense. In this study, the electrical resistivity data inversion is made by Gauss-Newton least squares method using RES2DINV software and also, the first arrival or break times are calculated using PickWin software, and moreover, the refraction seismic tomography data inversion is carried out using GeotTom CG software. Then, the data results have been clustered by three K-Means, FCM and Gustafson-Kessel FCM. Using Dunn index to optimize the number of clusters, the number of optimal clusters has been obtained equal to 12 that is a suitable number of clusters by considering the obtained electrical resistivity and refraction seismic tomography maps. Furthermore, considering the studies made in the dam site, alluvial basin and bedrock as well as bedding, it seems that Gustafson-Kessel FCM clustering method has shown better results. By starting Gustafson-Kessel algorithm and obtaining the results of running FCM, we see that the number of iterations is reduced and the speed of convergence is increased. The clustering algorithm computations in this research work have been made using programming in MATLAB software.

Identification of temperature regimes is very crucial for a better management of energy resources, recreations and truisms as well as for adequately determining agricultural calendars in different parts of the country. Few attempts have been made to adumbrate the Iranian temperature regimes; thus it is necessary to identify the most realistic temperature regimes of Iran using as many available stations across the country as possible. For this purposes, 155 Iranian synoptic stations with a relatively regular distribution across the country, mostly having full data records for the common period of 1990 to 2014, were used for the identification of the temperature regimes. In all stations, the average of the mean monthly temperature was computed in the mentioned period and further employed for the analysis. A principal Component Analysis (PCA) was applied to the inter-stations correlations matrix (155×12) composed of 155 stations and 12 mean monthly temperature values for each station. The computed Kaiser-Meyer-Olkin (KMO) measure of the sampling adequacy indicated that the matrix with the KMO value of 0.87 is useful for a PCA application. The first three leading PCs were considered for further analysis based on the scree plot and the sampling errors of the PCs (North et al., 1982). The remaining PCs were then rotated using varimax orthogonal criterion. The PC scores of both rotated and un-rotated solutions were separately used as inputs for Cluster Analysis (CA) to partition the considered stations into distinctive clusters. Moreover, all agglomerative CA methods as well as K-means CA were examined so as to find out the most appropriate method for partitioning the data. The cophenetic correlation coefficient was employed to measure how well the hierarchical dendrogram of a given CA represents the relationships within the input data. The results indicated that all the clustering approaches properly represented the inherent structure of the input data, yet the Ward method was selected as the most appropriate method since it resulted in much realistic clusters that quite perfectly matched the topographic and geographical features of the country. The correct number of clusters was also selected based on the Silhouette index (Rousseeuw, 1987) that measures how well objects lie within their cluster, and which ones are merely somewhere in between the clusters. The average silhouette width provides an evaluation of clustering validity, and might be used to select an ‘appropriate’ number of clusters. Computing the index for a set of predefined cluster numbers (2 to 15 clusters), it was observed that six is the most appropriate number for clusters for a better representation of the inherent structure of the data. As such, all 155 stations were classified into six clusters applying Ward CA method on the three leading un-rotated PC scores.
The maps of varimax rotated PC scores properly represented areas characterized with seasonal temperature variability. The first and second varimax rotated PC sores respectively display the winter and summer temperature variability across the country. Applying Ward clustering on un-rotated PC scores resulted in seven distinct clusters that appropriately specified the Iranian temperature regimes. It was found that the western and northern mountainous areas have a mountainous temperature regime whereas the central-eastern Iran hosting the Iranian deserts has a plain temperature regime which is considerably warmer than the earlier one. The third temperature regime includes stations scattered across the mountainous region, all of which are characterized with a very high elevation, hence having the coldest winters and the coolest summers in the country. The hot temperature regime which is the warmest temperature regime in the country belonged to the southwest and certain parts of the south. The stations located in the coastal areas of the northern and southern Iran respectively have the Caspian coastal, Persian Gulf coastal and Oman Sea coastal temperature regimes, all with the lowest annual temperature ranges in the country. Four out of the seven Iranian temperature regimes are continental but the two mountainous temperature regimes are the most continental regimes in the country due to their wider temperature ranges. The results conduce to a better management of energy resources, recreations and tourisms as well as an optimal determination of agricultural calendars in different parts of the country.

In this paper, classification of a large hydrochemical data set from Varamin plain is done by using fuzzy c-means (FCM) and hierarchical cluster analysis (HCA) clustering techniques. Then its application to hydrochemical facies delineation is discussed. Groundwater samples were grouped into three classes according to the optimum number of the classes and fuzziness exponent by using the fuzzy c-mean. The data set includes 90 deep and moderate deep well samples from groundwater data set and 9 hydrochemical variables were used. Results from both FCM and HCA clustering produced cluster centers that can be used to identify the physical and chemical processes creating the variations in the water chemistries. The optimum cluster in FCM method determined by optimization function, but in HCA method by trial and error. The FCM method is potentially useful in establishing hydrochemical facies distribution and may provide a better tool than HCA for clustering large data sets when overlapping or continuous clusters exist. Plotting the cluster membership value contours on a map demonstrated the existence of three spatially continuous, well-defined clusters of groundwater samples. The results showed that the FCM method is more sound for investigating threshold data rather than HCA method (that represents sharp and abrupt variations).

Electro-facies analysis is a sure method in clustering petrophysical logs analysis that can well indicates the changes of geologic charactereristics of the different lithostratigraphic units as well. Petrographic studies of 120 prepared thin sections of the upper parts of the Sarvak Formation in studied oilfield¡ Southwestern Iran¡ has resulted in definition of 6 microfacies sedimentary types. These data suggested that the middle Creataceous sediments in the studied area were predominantly deposited in two subenviornments of patch organic barrier and lagoon corresponding to inner and shallow a homoclinal ramp. In addition¡ using petrophysical logs in the studied well¡ 10 electrical facies were determined. By analyzing the data petrophysical in Geolog software¡ 10 electerical facies were optimized and then their number was reduced to six. According to good adaptation of optimized electerical facies with sedimentary microfacies¡ we would generalized obtained dresults to all parts of the Sarvak Formation. Study of porosity changes in the electrical diagrams such as CGR corrected Gamma¡ and sonic curves in mentioned well¡ indicated a significant reduction in facies porosity of 1 to 6. In addition¡ the results showed that the reservoir quality reductionin studied microfacies is corresponded to Bioclastic Rudist debris floatstone/ Rudstone¡ Benthic foraminifera Rudist debris wackestone / packstone¡ Benthic Foraminifera peloid Grainstone/Packstone¡ with High diversity benthic foraminifera wackestone / packstone¡ Peloid bioclastic wackestone /packstone with Low diversity benthic Foraminifera and Mudestone/Wackestone¡ respectively. In this study¡ we concluded that comparison and correlation sedimentary facies with electric microfacies provide an efficient method to study the quality of the hydrocarbon reservoir system that especially can be used in other wells with no core.

Summary In recent years, due to increasing the size of three-dimensional (3-D) seismic data and the number of seismic attributes, it is difficult for interpreters to examine every seismic line and time slice. Pattern recognition techniques as first-hand interpretation tools are used to both address the problem of large size of seismic data and to provide an initial guidance when working on a new seismic data where previous studies and data are limited. Different types of unsupervised learning techniques have recently been used for seismic facies clustering and object detection in seismic data. Among unsupervised learning techniques k-means, self-organizing maps (SOM), generative topographic mapping, and principal component analysis (PCA) are used for facies analysis. In this study, we have applied k-means, SOM, and PCA on a 3-D seismic data volume acquired over the Strait of Hormuz to detect buried channels. Not surprisingly, the most important parameter in this study is the choice of appropriate seismic attributes. Although the PCA is not a clustering technique, it can detect channels in 3-D seismic data more efficiently than the k-means and SOM. According to the dip of the structure, the detected channels are prolonged from the west to the east and the southeast where there is a mini basin within the Mishan Formation.
Introduction One important class of machine learning tasks is the unsupervised learning. In the unsupervised learning, no labels are given to the learning algorithm, leaving it on its own to find structure in its input. The main task of this learning method is data clustering, but some different tasks such as dimensionality reduction and density estimation are belonged to this category. PCA is a dimensionality reduction technique, which can be used for better visualization of data. After explaining the geology of the study area, we discuss the learning methods, and their workflows. In the next step, we present the chosen attributes, and the learning algorithms applied to the data.
Methodology and Approaches We have used OpendTect for attribute measurements. After preparing the data, we have applied three unsupervised learning techniques of k-means, SOM, and PCA, on attributes of the 3-D seismic data volume acquired over the Strait of Hormuz. The chosen attributes in this study are spectral decomposition, curvedness, and gray-level cooccurrence matrix (GLCM) homogeneity. First, we have applied the PCA to reduce the dimension of attribute data to 2-D, and then, the k-means and SOM are applied on the data. Next, we have presented the two first principal components of attributes to the RGB color system, and consequently, we found that this method is superior than the k-means and SOM in the illumination of the channels.
Results and Conclusions Although the PCA method is not a clustering technique, it can detect channels in 3-D seismic data more efficiently than the k-means and SOM clustering methods. According to the dip of structure, these channels are prolonged from the west to the east and to the southeast where there is a mini basin within the Mishan Formation.

Summary: Linear and nonlinear time-history analyses are necessary tools for many fields of structural/earthquake engineering including vulnerability analyses and response evaluations. In this regard، one of the most important requirements is the selection of proper acceleration time histories as the analysis input. These records can be obta from natural earthquake records or can be generated synthetically and artificially. The first option is usually preferred to the others since it can provide true information about strong ground shaking characteristics to reflect the source، path، and site effects. These characteristics include several parameters such as earthquake magnitude، epicenter distance، duration، Arias intensity، spectral intensity، soil type and peak ground responses. However، well-known seismic design codes offer their linear design spectra to which the earthquake accelerogram is scaled; instead of direct matching the seismologicalparameters. The number of ground motions recorded at a given site is not generally sufficient; thus، it is required to select other earthquakes from real-world catalogues based on their similarity to the site-specific record (s). However، it is a challenging task to deserve such a similarity due to the existence of several criteria affecting the earthquake characteristics. The present work deals with the problem of selecting a number of appropriate ground motions among an available catalogue in order to best match a given site-specific earthquake record. As an unsupervised solution in this research، clustering techniques were concerned. A set of data in a multi-dimensional space were classified into some clusters in such a way that the objects in every cluster have the most similarity with each other and the least similarity with any object in the other clusters. Here، both deterministic and non-deterministic approaches in such a clustering problem were employed and compared. The method of K-means was selected as a deterministic algorithm due to its popularity and computational efficiency among clustering techniques. A set of input clusters was required to be introduced for this algorithm before its run. However، this method itself was deterministic. Such an initial set is usually chosen by trial-error or randomly. Depending on the initial input clusters، the K-means algorithm reveals different local optima instead of global optimum as the clustering result. In order to overcome such a challenge، a non-deterministic approach was studied as well. In this regard، a Genetic Algorithm، GA، was utilized as a well-trusted metaheuristic among non-deterministic algorithms with the capability of overpassing localresults and approaching to the true global optimum. Proper genetic encoding and operators were developed for the current optimal clustering problem. Consequently، a variety of earthquake characteristics were taken into account in the utilized chromosome including magnitude، epicenteral distance، duration، Arias intensity، spectral intensity، soil type، peak ground responses and a specialized error measure. This measure was defined based on how compatible was the response spectrum of the record with the codified design spectrum as the target. In the present study، average silhouette widths and related profile plot were used to evaluate effectiveness of the proposed clustering processes. Quality of clustering was thus measured taking into account both compactness and separation of the clusters. Consequently، the clustering result corresponding to the maximum amount of the average silhouette bandwidth was announced as the solution. Input-records required for the timehistory analyses، were then selected from the cluster which includeed the target siteassociated earthquake. Using different numbers of clusters، the proposed genetic search stood well superior to the method of K-means in achieving the best clustering for the present problem.

Electro-facies is a unit of the vertical sediments sequence on the wire-line logs that can be differentiated from the upper and lower units. For detection of the electro-facies interval in Kangan and upper Dalan formations in south Pars Gas Field، wire-line log and core data analysis (wells A، B، C and D) were used. Note، only core data analysis in well A were available. First، thin-Sections study core plugs from this well resulted in the identification of 12 micro-facies and 7 sedimentary environment. For recognition of electro-fasies based on these data، clustering method and core data were used to categories their homogenization of data. In this study، Rock-Types (R-T) that have similar reservoir characteristics were identified using porosity/permeability cross plot from core data; therefore based on these data، 6 Rock-Type were identified. After classification of Rock-Types and micro-facies from the core data، MRGC models of these facies were identified from the wire-line logs in well A. Optimized model in well A were applied in the wells B، C and D to recognized electro-fasies in these wells with no-core data. Average petro-physical properties of each facies in different wells were compared for quality controls of predicted facies. Also facies of 4 wells were correlated to see if they are comparable. As a result، 6 electro-facies were identified with different petro-physical characteristics and the poorest reservoir quality belong to the facies (1) with dominant anhydrite lithology and the best quality reservoir belong to the facies (6) with the dominant dolomite and limetone with grainstone textue. Due to the high accuracy of the results، the suggested model can be used in other wells in this field.

Reservoir lithology determination is one of the main studies used for well correlation and analyzing productive zones of the reservoir. The best way for lithology determination is using core and cutting information. Nevertheless, in most wells these data is not complete and continual, so in these cases usually use well logging for lithology estimation. The purpose of this paper is representing accurate method for lithology estimation of petrophysical well data with Clustering-Estimation approach. This method has been generalized according to one well from one of the oil fields in South of Iran that contains core data. Then this method is generalized in uncored wells. Clustering is used as a way for grouping well data in homogeneous lithology clusters Afterward, percentage of mineral is estimated in each of these clusters. The regression coefficients are calculated 92.93% and 74.99% between real and estimated data respectively for calcite and dolomite in one of the wells. The results with high accuracy show the generalization of this method.

One of the basic discussions in geosciences is construction of different tectonic zoning maps. In conventional tectonic zoning, not only the great amounts of subjective judgment are involved but also accurate interpretation of high-dimensional data is so difficult and out of human capability. To alleviate these deficiencies, quantitative scientific methods in data mining domain can be applied as an effective and useful tool to construct the new numerical maps in geosciences. In this paper self-organizing map (SOM) neural network that is one of the common methods in data mining has been applied for numerical tectonic zoning of Iran. SOM is an unsupervised artificial neural network particularly adept at pattern recognition and clustering of high-dimensional data. Visualization of high-dimensional data in two-dimensional topological-preserving feature map is another specific capability of SOM that represent both homogeneity within and similarity between clusters. Although there are some similarities between SOM's numerical maps constructed here and the conventional maps but SOM method is more powerful for identification and interpretation of different zones than conventional methods. Utilizing SOM method enables us not only to evaluate the degree of homogeneity in each zone, but also to separate regions zone that experience similar geological evolutionary despite of their geographical locations. For instance Lut and Gavkhuni zones show more homogeneity than Makran and Azerbayejan zones also Kopeh-Dagh and Zagros are located at different regions, they have similar features. The results obtained here represent separation between Makran from East Iranian Ranges and Western Azerbaijan from Alborz Ranges, too. It is important to recognize that the SOM's results are based purely on the geophysical, geological and seismic features presented previously. So correspondences and differences between the SOM's zones and a given zone based on conventional method must receive careful thought.