جستجوی مقالات مرتبط با کلیدواژه « نشانگر لرزه ای » در نشریات گروه « زمین شناسی »

Salt dome is a diapir shaped structure of salt that intrudes vertically through sediment layers and surrounding strata due to its low density. Salt area identification, determining its boundaries and its 3D modeling in seismic data is a crucial issue in the literature of the seismic data interpretation. Due to its high impermeability characteristic, it can form stratigraphic oil traps by sealing the hydrocarbon reservoirs and also could be used as underground storage for natural gas and disposal sites for hazardous waste such as isolation nuclear waste and creation of the compressed air reservoir. The steeply dipping complex-shaped structures related to the salt movement and significant difference in seismic wave propagation velocity inside the salt dome with the enclosed media, imposes significant challenges for seismic data processing and interpretation. Identification and delineation of salt body is a key step in seismic data processing and interpretation, which can help geophysicist to overcome aforementioned problems. In reflection seismic methods, salt boundaries are more often characterized by change of seismic character of the signal also called texture. There are several methods available for texture analysis in image processing that can be divided into seven classes which are statistical analysis, structural methods, transform based approaches, model-based methods, graph-based techniques, learning based strategies and entropy-based methods. Textural attributes characterize the spatial arrangement of neighboring amplitudes. Extraction of seismic texture attributes can be performed using spectral information of image such as Gabor filters and local 2D Fourier spectra. Dip, similarity and coherence are the common structural attributes which are used generally for textural analysis in seismic data. The most common rational approach to describe the texture in seismic image is to measure the statistical properties of the image. Gray Level Co-occurrence Matrix, chaos and variance are three conventional statistical seismic texture attributes used for this purpose. Due to the textural contrast of the salt dome with the surrounding layers and sediments, edge detection tools can also be used to determine the boundaries of textural changes and delineate the salt area. In this study, we used a new textural seismic attribute known as the gradient of texture to characterize the change of seismic character between the salt body and its surrounding geology. It calculates the texture gradient in two adjacent windows around a sample in different directions. It is supposed that different area in seismic image with different textural pattern will exhibit diverse gradient of texture. Thus, it will be appropriate for image segmentation for specific interpretation investigation. The gradient of texture attribute will differentiate desired area from the rest of the image through supervised classification and growth strategy in extending the selected classes. Efficiency of the introduced method for salt dome delineation and modeling in seismic data was investigated here by applying on a synthetic model and 3D seismic data from the Persian Gulf. Comparison between obtained results of the proposed method and conventional attributes revealed superiority of the 3D texture gradient in textural segmentation and salt dome modeling from seismic data.

Assessing a time-frequency representation of signal with an acceptable timefrequency resolution, and for specific purposes in different applied studies, has always been a challenge for signal processing researchers. In case of seismic data, using a time-frequency representation with high resolution will yield a higher precision in processing and interpretational applications of timefrequency analysis of data. In the most of time-frequency analysis methods, a form of smoothing is used for generating time-frequency map, which it causes energy dissipation in time-frequency plane and decreasing the resolution. Reassignment is an efficient technique for compensating this issue and increasing the resolution. It can provide a high time-frequency resolution through moving and concentrating the energy distribution in the time-frequency plane to true location. Reassignment has been applied to various time-frequency analysis methods and its performance has been presented in different researches. In this paper, the reassigned Stransform as a new development on S-transform to provide higher time and frequency resolution is utilized to extract some seismic attributes. The performance of the method in providing an acceptable time-frequency resolution is shown by testing on synthetic non-stationary chirp and seismic signals. As a seismic application, the reassigned S-transform is utilized in studying low frequency shadows through time-frequency analysis of seismic data set acquired on a hydrocarbon reservoir. For this purpose, some time-frequency attributes including single-frequency, instantaneous amplitude, instantaneous dominant frequency and sweetness factor are extracted by this method. The results show that the reassigned S-transform can provide much higher energy concentration rather than standard S-transform, and the events and anomalies can be interpreted with more precision due to their better time and space resolution in attribute sections.

The time-frequency analysis methods are among the most common signal and image processing techniques in different applied fields of electric engineering, mechanical engineering, geoscience, etc. Time-frequency methods are employed in seismic data processing and interpretation applications for denoising, attenuation estimation, deconvolution, hydrocarbon detection, channels and faults visualization and so on.
There are several time-frequency analysis methods. One of the main reasons of developing new time-frequency methods is to reach higher time-frequency resolution. The reassignment is one of the successful approaches in this field. The mission of reassignment method (RM) is to move the energy distribution of the time-frequency plane to true location. Through this way, a precise distribution of instantaneous frequency has been provided for any time sample. Reassigning is also carried out in time direction. The RM has been applied in several time-frequency methods such as wavelet transform, Wigner-Ville distribution, Gabor transform and S-transform. In this paper, the reassigned Stransform has been studied in seismic data time-frequency analysis. The method has been utilized for detection of low frequency shadows in a seismic dataset to locate probable gas reservoir.

In this paper, the performance of reassigned S-transform has been studied by its application on synthetic chirp signal and seismic trace. The results show that the method is capable of providing a well-concentrated time-frequency maps. As an application in real seismic data, the method has been utilized for studying the low frequency shadows related to probable gas bearing zones. This approach extracts some attributes including single frequency, instantaneous amplitude, instantaneous dominant frequency and sweetness factor, through time-frequency analysis of the data. The results show that the reassigned S-transform can provide higher time and space resolution, and thus, the events and anomalies can be interpreted more precise compared to standard S-transform results.

This study has focused on identifying fault systems in the Hormuz Strait area using compilation of seismic attributes and artificial neural networks. Faults and fractures play an important role in creating areas of high porosity and permeability. In addition, they cut off the cap and reservoir rocks along fluid migration pathways. Intense tectonic activities and salt tectonics have resulted in complex structures in the Strait of Hormuz area. Therefore, precise identification of faults and fracture zones and their extensions has special importance in increasing petroleum production traps. In order to identify the geometry and kinematics of faults in the Mishan and Aghajari Formations and in the units under the base-Guri unconformity in the Hormuz Strait area (eastern part of the Persian Gulf), we have used structural imaging and visualization techniques of seismic interpretation. The structural imaging of the fault zones was obtained by this technique based on the integration of input attributes in an artificial neural network system and creating new attributes. First, a set of advanced attributes were introduced as input for the artificial neural network system to train and compile the calculated attributes on fault and non-fault interpreted points. As a powerful exploration tool, finally, the fault cube was obtained to precisely identify fault systems and better detect faults and fractures in quantitative modeling of the area. As a result of integrated attributes, the high correlation between the faults within the fault cube provides more accurate and reliable tracking of fault extensions. Therefore, three types of fault systems were identified in study area, which are thought to be results of the extensional and compressional tectonics of the Oman Orogeny, vertical tectonic movements of the Zagros Orogeny, and syn-sedimentary salt movements.

Seismic data interpretation methods provide useful information about underground structures. Since many years ago, several methods have been developed to aim this goal. Seismic facies analysis is one of the new methods in seismic interpretations. This method can produce a classified section using reflection seismic data and/or seismic attributes. Classified sections can reveal lateral changes in seismic facies which may relate to geological facies changes. Using different pattern recognition methods, several seismic facies analysis methods have been developed in recent years. However, in this study, an agglomerative hierarchical clustering algorithm has been utilized to produce classified sections. Seismic facies is a group of data whose attributes are different from those of neighbor groups. Each attribute can extract additional information about underground. Using a single attribute makes it difficult to get more information. However, by combining several attributes in a hierarchical clustering algorithm, it is possible to interpret seismic data in a more appropriate way. In hierarchical clustering, all time samples are divided into similar clusters. At first, each sample is assigned to one cluster. Dissimilarity matrix is constructed based on a distance definition such as Euclidean distance between samples. This matrix is then used to cluster all samples in a hierarchical procedure. In each step, more similar clusters merge into a new cluster and the dissimilarity matrix is updated. Finally, all samples merge into one cluster. Before clustering it is common to perform a principal components analysis, PCA. PCA is a statistical technique to perform dimension reduction. Using PCA, we can find the directions in data with the highest variation and reduce the dimensionality of a large data set with interrelated variables without considerable loss of information. In this study, the PCA was utilized to attenuate the redundant and random noisy data. Prior to the PCA, it is necessary to normalize the data. Clustering algorithm in this study was applied to three synthetic models as well as 2D and 3D real seismic data of an oilfield, Southwest of Iran. The first model was a horizontal-layer one with lateral changes in facies. The second model was a horizontal-layer one with a normal fault which caused a movement of layers. The third model was an anticline one with lateral changes at the top of the anticline. Real seismic data from an oilfield in the Southwest of Iran was used for this study. Nine seismic attributes were calculated using the Paradigm software to extract more information from migrated seismic data. These nine attributes and the primary seismic data were normalized and entered into the PCA. Seven principal components were selected based on the PCA. These data were used to apply to clustering algorithm. Our results showed that the seismic facies analysis can provide useful information about the underground structures and lateral changes. In the cases of the first and second models, lateral facies changes were revealed for signal-to-noise ratios of up to 4 dB. Regarding the third model, the results were acceptable for signal-to-noise ratios of up to 8 dB. In addition, it was shown that defining more number of clusters could not lead to better results. By comparing 2D and 3D data clustering, it is concluded that the resolution of seismic facies in 3D clustering is quite related to 2D one.

Determination of porosity distribution is important in hydrocarbon reserve estimation, facies variations, optimized planning for field development and decrease in drilling risks and costs. Porosity is one of the most important parameters, which is considered as a fundamental factor in reservoir engineering. By knowing this parameter, specialists are able to design and manage, effectively, the process of oil and gas field development. Seismic attributes and well logs are the data available in most of the reservoir studies. Seismic attribute analysis is generally done through correlating multi attributes to the reservoir characteristics. A good interpreter needs to observe several maps with certain information to prepare optimal drilling points. Such a process is long and exhausting with high probability of error. We present in this paper a method to ease the interpreter’s task of analyzing dozens of seismic attributes by integrating all the information into just one map, this map, the similarity map, shows the resemblance of the seismic response of each region of the whole study area with respect to a selected location in the field. In this paper, 3D seismic data in the study area are interpreted using well data. In addition, seismic inversion was conducted in order to estimate the porosity distribution based on the acoustic impedance within the study area. Moreover, an attempt was made to predict the effective porosity by designing a probabilistic neural network (PNN) and simultaneously using seismic attributes and effective porosity logs in the reservoir window. This was done by deriving a multi-attribute transformation between an optimum subset of seismic attributes and effective porosity logs. Seismic traces close to the well locations were used to generate seismic attributes. Effective porosity logs at the reservoir area were the target logs in this study. A set of seismic attributes were generated using HRS software and a forward stepwise regression process was used to determine an optimum subset of attributes to be utilized in the training of neural networks. Ultimately, we obtained a porosity map of the studied area. The inputs of the similarity analysis included a set of uncorrelated seismic attribute maps, the coordinates of the control point, and the radius around the control point that circles an area (the reference zone) of nearly constant attribute response. Four different attribute volumes generated were then used in the study: instantaneous amplitude, instantaneous phase, instantaneous frequency, and acoustic impedance. A horizon-slice at the reservoir was extracted from each of the attribute volumes. First, Well 08-08 (a high producing well) was chosen as a reference well. The selection of the reference well could be the highest production well, the lowest production well, a dry well, or any other classification depending on the objective of the analysis. The objective was to map the reservoir of the field based on the reference point 08-08 for possible high production areas. A radius value around the well was then chosen to calculate the mean and the standard deviation of the reference point within the radius from the extracted horizon slice for each of the attributes. The output of the first step was (N) different reference means and reference standard deviation for the same reference point; (N=4) is the number of attributes that were used in the study. The next step was to calculate a zero-one matrix from the extracted horizon-slice for each attribute based on a statistical criterion that would assign either zero or one to every node for a given horizon-slice. Finally, zero-one maps were integrated into one single map. The four attributes revealed different information and their zero-one maps showed different distributions that help the interpreters correlate each map to other types of information such as production or geologic information. The final map was obtained by integrating the zero-one maps. Studying the results obtained from the “similarity map” and “porosity map” in reservoir zone presented a convincing correlation between the two maps found through different methods each having specific information for the interpreters and helping them make more reliable decision to choose a prospective point, with less drilling risks.

During the last decade, there has been an increasing interest in the use of attributes derived from 3-D seismic data to define reservoir properties, such as the presence and amount of porosity and fluid content. Therefore, it is worthwhile to continue the advances in the study and application of expert systems in the petroleum industry so that it is possible to use the attributes in reservoir characterization more effectively. The establishment of the existence of an intelligent formulation between two sets of data (inputs/outputs) has been the main topic of such studies. One such topic of great interest was the characterization of 3D seismic data with relation to lithology, rock type, fluid content, porosity, shear wave velocity, and other reservoir properties. Petrophysical parameters, such as water saturation and porosity, are very important data for hydrocarbon reservoir characterization. Hitherto, several researchers endeavored to predict them from seismic data using statistical methods and intelligent systems (Russell et al., 2002; Russell et al., 2003; Chopra and Marfurt, 2006). Correct recognition of porosity model and estimation of petrophysical parameters in reservoirs is a key issue in any oil project. The correct estimation of porosity as a petrophysical parameter can inform decisions that have high financial risk, such as drilling. By determining reservoir characterizations and assessing petrophisical parameters with a adequate accuracy during the first steps of studies, researchers would be able to produce optimum exploitation with a minimum number of wells. This paper focuses on the link between seismic attributes and reservoir properties such as lithology, porosity, and pore-fluid saturation. Typically, seismic attributes have been the only information obtainable from seismic data. Using statistical rock-physics, the type of seismic attributes that are direct functions (analytically defined) of the elastic properties can be probabilistically transformed, sample-by-sample and independently one of each other, into reservoir properties. In this paper, we combine the methods of geostatistics and multiattribute prediction for the integration of seismic and well-log data, and illustrate this new procedure with a case study. A number of new ideas are developed for the statistical determination of reservoir parameters using seismic attributes, combining the classical techniques of multivariate statistics and the more recent methods of neural network analysis. We first extract average porosity values at the zone of interest, and then compare these values to average seismic attributes over the same zone. The technique of cross-validation is subequently used to show which attributes are significant. We then apply the results of the training and cross-validation to data slices derived from both the seismic data cube and the inverted cube to produce an initial porosity map. Finally, we improve the fit between the well log values and the porosity map using co-kriging. The main purpose of this paper is to present a quantitative assessment of porosity as a petrophisical parameter in an offshore oil field in Iran using the newly proposed method of reservoirs parameter estimation. This paper shows that by using both seismic data and well logging data it is possible to obtain a more accurate model of porosity in a given reservoir. Specifically, the study determines the relationship between a set of seismic attributes and a reservoir parameter such as porosity at well locations, and then uses this relationship to compute reservoir parameters from sets of seismic attributes throughout a seismic volume. Therefore, a primary plan of porosity is available for the area of study. In the next step, by using geostatistics and, according to the initial plan, as a secondary variable in collocated cokriging, we can approach a more accurate plan to show the distribution of porosity. In effect, the proposed method combines geostatistics with multiattribute transforms. This technique uses multivariate statistics and neural networks to improve the secondary dataset used in the collocated cokriging technique.