abdolrahim javaherian

Different depositional environments and their subsequent diagenetic processes usually result in various rock textures with complex pore structure in carbonate rocks. This study considers Fahliyan carbonate formation in an Iranian oil field within the Abadan plain to quantify pore shapes using a rock physics model. In this modeling, the aspect ratios of different pore shapes and their volume fractions are calculated using differential effective medium theory, taking into account the pore shape effect on the elastic moduli estimation. Different pore shapes together with their aspect ratios and volume fractions are quantified using differential effective medium theory, and then, it is used further to predict elastics logs by Xu-Payne modeling. The results indicate that two pore types of reference pores and stiff pores can be characterized as the main pores in Fahliyan carbonate formation. This conclusion is confirmed by formation micro imager (FMI) log and core information.

Reservoir modeling is the process of creating a three-dimensional numerical model to show the spatial distribution of geological or petrophysical properties of the reservoir. The process of obtaining elastic properties from seismic data is called seismic inversion. There are different methods for seismic inversion, which are classified into two main groups:deterministic methods and stochastic methods. Understanding the differences between these two methods and their restrictions is important for their correct application and interpretation. Due to the band-limited nature of the seismic data, the results of deterministic methods are smooth maps of acoustic impedance and may be far from the reservoir facts. In contrast, stochastic inversion produces high-resolution maps of the acoustic impedance because the spatial continuity models (variograms) control the frequency content of stochastic inversion results. A well-known challenge of stochastic inversion is that it is often extremely expensive from computational point of view. In this study, a stochastic method has been used to obtain the facies and other properties of the reservoir.

In this study, to show the ability of the introduced method in modeling reservoir facies, a two-dimensional artificial model has been used. The formation in the reference model of this study consists of sandstone facies with high porosity (reservoir interval) and dense shale facies (non-reservoir interval). The formation is located at a depth of 2000 to 2200 m. At the top and bottom of the formation, a 50-m layer of shale with constant properties is considered. In order to model the facies of the reservoir, variogram parameters for different facies have been calculated from the well logs of the reference model. In the next step, the conditional probability of occurrence of the facies in each cell has been calculated using the sequential indicator simulation method with different random seeds. Then, the probability perturbation optimization algorithm has been applied to update each facies model until the model become consistent with the seismic data. At each step, a geophysical forward model is constructed and compared with seismic data.

After implementing the stochastic inversion method to the seismic data with a signal-to-noise ratio of 9 in the reference model, it was found that the optimized facies had an 81.83% correlation with the reference facies and was improved the initial facies model by 19.97%. The correlation values decreased to 77.67% and 72.38% when the seismic data with the signal-to-noise ratios of 4 and 2 were respectively used. When the seismic data with the signal-to-noise ratio of 4 was used, the initial model was improved by 15.81%, and when the seismic data with the signal-to-noise ratio of 2 was used, the correlation decreased to 10.52%.

Gaussian process regression, as a nonparametric probabilistic model based on Bayesian statistics, is highly capable of supporting ‎sparse features such as global anomalies. Detecting abnormal behavior from normal behavior makes Gaussian process regression ‎as an edges detector where faults may occur in the seismic data. In this study, the Gaussian process regression-based anomaly ‎detection was applied to both synthetic and real data containing normal fault to detect the fault edge. To identify the fault edges, ‎the geological layers are considered as normal interaction and the fault edge as a global anomaly which disrupts the normal ‎behavior of layers. The error of regression is analyzed to separate the fault edge. To evaluate the proposed method, it was applied ‎on a series of synthetic seismic data and a real 2D seismic section of F3 block of the North Sea containing the fault. The results ‎show the ability of this method in fault detection.‎

Characterization of siliciclastic reservoirs from seismic data is very sensitive to their clay and shale content. Clay can affect P- and S-wave velocities through its type and shape as well as location. Such an elastic behavior of clay infers that reservoir properties can be overlooked if their clay content is not understood and interpreted adequately. Clay can exist in different types with their specific shape and even can be distributed within siliciclastic rocks in various forms- structural, laminar, interstitial and dispersed clay- with different velocity responses. The mixture of the various clay types and forms can make their velocity interpretation for reservoir properties more complicated. Therefore, a proper strategy to separate the effects of clay types and clay forms is necessary for any seismic reservoir characterization on siliciclastic reservoirs with high clay content. Rock physics is a bridge between seismic and reservoir properties. An important goal of this branch of science is to understand the physical properties of the reservoir, so it is important for this kind of study. This study integrates rock physics modeling and simultaneous seismic inversion in order to find different clay distribution (forms) in one of the oilfields in the western part of the Persian Gulf. The well log data (wells A, B, and C) from this field show how the reservoir quality varies within the field with no obvious relationship to their shale content. This independent behavior of shale content and reservoir properties could be an indication that clay distribution may vary and clay type is not the only parameter for clay effects on the reservoir properties. Therefore, Thomas-Stieber rock physics template, first, is used to characterize shale distribution at well location and then the same template is applied on the reservoir properties derived from simultaneous seismic inversion to understand clay distribution in the whole area. Our results confirm that at wells, clay distribution is varying from top to the bottom of the reservoir. We find out that reservoir quality is not changed within the bottom part of the reservoir with high clay content (due to structural clay) while the same clay content reduced reservoir quality in the top and middle parts of the reservoir (due to the dispersed and layered clay). In order to do reservoir characterization, a map of shale content from top Ghar and top lower Asmari is generated. This generated map differentiates proper reservoir interval from the non-reservoir interval. Therefore, by using the proposed method in this study, one can delineate the potential zones of the reservoir for the future plan of drilling and production.

Seismic technologies have been recently evolved into a central position in reservoir characterization and monitoring with the recent improvements and its cost efficiency. In this regards rock physics play an essential role by connecting seismic data to the presence of in-situ hydrocarbons. Modeling the effects of pore fluids on rock velocity and density is an essential part which normally is used to detect the influence of pore fluids on seismic signature. In recent years, one of the most important developments in rock physics has been the fast progress toward quantifying the relations between geologic processes and geophysical signatures. This quantification is normally done through application of different types of rock physics models: theoretical, empirical and hybrid models. However, fluid substitution methods make it possible to predict the elastic response of a rock saturated with one type of fluid from the elastic response of the same rock saturated with another fluid. This infers that seismic wave velocity could be predicted in geological formations for any possible hydrocarbon signature based on the measured velocities in the counterpart water-saturated formations. Therefore, fluid substitution is an important part of any seismic rock physics analysis (e.g., amplitude versus offset and time lapse studies), and can provides an efficient tool for fluid identification and quantification in a given reservoir. Fluid substitution commonly performed by using Gassmann’s equation which has already being discussed frequently. In general, Gassmann applicability is questionable in carbonates as it can under-predict, over- predict or even correctly predict seismic velocity changes by changing pore fluids. This is normally attributed to the violation of some of the Gassmann assumptions like their pore space connectivity in carbonates. The goal of this study is to perform fluid substitution and seismic modelling of one of the Iranian carbonate oil field to investigate validity of Xu and Payne (2009) for the carbonate field. This model generally emphasizes the behavior of rocks related to different pore types. Fluid substitution results are then compared and verified with the laboratory measurements of core sample taken from the same reservoir intervals. The final output of fluid substitution is saturated bulk modulus, shear modulus and density for either of the defined saturation scenarios. Our results show that Xu and Payne (2009) can be used on the studied reservoir. Also, the obtained results were confirmed using other source of information like ultrasonic measurements. Furthermore, this model was used to model frame bulk modulus as an input into the fluid substitution purposes. The results of the fluid substitution confirm the applicability of the introduced approach to discriminate different fluid responses in this field.

Reservoir characterization has a leading role in the reservoir geophysics and reservoir management. Since the interests of the reservoir geophysics and reservoir managements are the elastic properties and reservoir properties of the subsurface rock for their purposes, a robust method is required for converting seismic data into elastic properties. Accordingly, by employing a rock physics model and using the inverted seismic data, one can describe the reservoir for purposes such as improvement in the production of the reservoir. In the present study, we employ one of the methods for converting the seismic data into the elastic properties. This method of inversion is known as simultaneous inversion, which is grouped in amplitude-variation-with-offset (AVO) inversion category. In this method, unlike the other methods of AVO inversion, the pre-stack seismic data are directly inverted into the elastic properties of the rock and an excellent lithology and fluid indicator (VP/VS) are provided. Then, this indicator is tested on one of the oilfields of the Persian Gulf. Moreover, by means of this method, one can locate the fluids contact and the lithological interlayers; also, by the inversion results, which are the cubes of the seismic properties of the rock, one can generate sections of the elastic properties of the rock such as Poisson’s ratio and Young modulus which are useful for geomechanical analysis. Therefore, this kind of method is a quick way for the prior analysis of the studied area.

Seismic noise can be divided to random and coherent in reflection survey. The ground roll is a coherent noise in land seismic data that has high energy, high amplitude, low frequency and low velocity. It usually masks the reflections. Therefore, it must be attenuated in the seismic data processing. In this paper, we proposed a modification on the common offset common reflection surface method to attenuate ground roll and random noise. The CO CRS stacking operator is a hyperbola; therefore, it fits the hyperbolic reflections in the prestack data. Ground roll and random noise has linear and uncorrelated traveltime respectively. When the CO CRS operator is applied to the data, the reflection events can be detected by the coherency analyses. High coherency values belong to the reflection events, and low values indicate that no events with hyperbolic traveltime are detected. As a result, when the events are distinguished, any event with non-hyperbolic traveltime can be muted. We applied the proposed method on two real land data sets. The new method was compared with the f-k filtering and conventional CO CRS stacking after the f-k filtering. Results showed that the proposed method attenuated aliased ground roll better than the f-k filtering and conventional CRS. Further investigation was the effect of reflection amplitudes on ground roll attenuation by the CO CRS stacking. For a better attenuation, the minimum coherency of reflections had to be higher than the maximum coherency of the ground roll. Therefore, the intersection of the minimum reflections coherency and the maximum ground roll coherency is an SNR threshold (dB) for ground roll attenuation with FO CRS stacking.

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.

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.

Integration of 3D seismic data with petrophysical measurements gives a better vision to reservoir characterization. The integration of well-logs and seismic data has been a consistent aim of geoscientists which become increasingly important and successful. In recent years، because of the shift from exploration to development of existing fields with a large number of wells penetrating them، improving reservoir study has been the most important pre-drilling activity. One type of integration is forward modelling of synthetic seismic data from the logs. A second type of integration is inverse modelling of the logs from the seismic data. It is called seismic inversion. Seismic inversion، in geophysics، is the process of transforming seismic reflection data into a quantitative rock-property description of the reservoir. Another method is to estimate the log properties by seismic attributes. In this study، linear multiattribute transform، and non-linear multi-attribute transform were used for predicting porosity in one of the Iranian hydrocarbon fields. The analysis data consisted of the target log (in this study، the porosity log) from wells tied with 3D seismic volume. From the 3D seismic volume، a series of sample-based attributes was calculated. The objective was to derive a multi-attribute transform، which was a linear or nonlinear transform between a subset of attributes and target log values. The selected subset was determined by a process of forward stepwise regression، which derived increasingly larger subsets of attributes. In the linear mode، the transform consisted of a series of weights derived by least-squares minimization. These weights are coefficients of the selected attributes in a linear multi-attribute transform. In the nonlinear mode، a neural network was trained using the selected attributes as the input. Two methods of neural network used in this study include probabilistic neural network and multi-layer feedforward network. The basic idea behind the general regression probabilistic neural network is to use a set of one or more measured values، called independent variables، to predict the value of a single dependent variable. The multi-layer feed-forward network method consists of a set of neurons، arranged into two or more layers. There is always an input layer and an output layer، each containing at least one neuron. Between them، there are one or more ‘hidden’ layers. The neurons are connected in the following fashion: inputs to neurons in each layer come from outputs of the previous layer، and outputs from these neurons are passed to neurons in the next layer، and each connection represents a weight. To estimate the reliability of the derived multi-attribute transform، cross-validation was used. In this process، each well was systematically removed from the training set، and the transform was rederived from the remaining wells. Then، the prediction error was calculated for the hidden well. The validation error، which is the average error for all hidden wells، was used as a measure of the likely prediction error when the transform was applied to the seismic volume. There was a continuous improvement in predictive power as it was progressed from a single-attribute regression to a linear multi-attribute prediction. This improvement was evident not only in the training data but، more importantly، in the validation data. In addition، the neural network did not show a significant increase in resolution over that from the linear regression. As a conclusion، the best result of porosity estimation in this field was provided by the linear multi-attribute transform.

In oil exploration, because of the anisotropy of the earth, the velocity of the waves in horizontal nd vertical directions are not uniform; however, with a good accuracy in exploration procedures, e can assume that in a layer, velocity changes are limited as a results of slow variations in ensity as well as the elastic properties of the layers in these horizontal directions. In general, ariations of the above mentioned parameters inhorizontal directions are much slower than in ertical ones. For this reason, the acquisition area is often divided into smaller areas; horizontal variations are neglected while the same vertical velocity distributions are applied in any sub-area. There are basically two methods in calculation of the bin size and migration aperture in a 3-D seismic survey design. The first method is based on using a constant velocity model which is not compatible with real conditions. In this model, we assume that the medium between the surface of the earth and the target layer is replaced with supposed layer and ascribe a constant amount velocity to this layer that is equal to the average velocity in medium between the surface and the target layer. The second method uses the model wherein the velocity changes with depth and therefore a linear velocity model is assumed which is more compatible with reality in comparison with the previous method. Whereas the linear velocity method can include all important wave propagation effects, it involves a certain circular logic. This method, involves building a detailed subsurface velocity model and uses ray tracing or other simulation techniques to customize the survey for the local subsurface. In Ahwaz Oil Field, the main target was Asmari Formation and the deep target was Fahlian Formation. The 3-D seismic survey design of Ahwaz Oil Field was performed on the main target located in the depth of 2900 m and the deep target located in the depth of 5000 m from the mean sea level. Ground level was about 15 to 40 m higher than the sea level in this area. By considering the check shot, VSP and sonic log data from 14 well logs, the image area was divided into 14 parts, so that the variations of the horizontal velocity could be neglected in each part and the constant contribution for the vertical velocity could be used. Finally, using the velocity values at the desired vertical depth to the reflection point (target depth), the dip angles of the target horizon (dip of reflector at the reflection Point) and the maximum frequency reflected from the main target, we were able to calculate the bin size and migration aperture in each part. At last, we could select a value for the bin size in this project. In this study, we examined the parameters of the velocity-dependent 3-D seismic survey design. These parameters included the bin size and migration aperture. Conventional formula for the bin size and migration aperture for Ahwaz Oil Field was carried out based on the linear model between the velocity and depth. As an intermediate between constant velocity and interval velocity model, we have given expressions valid for a linear velocity function. By using the linear velocity model, the design parameters incorporated first-order ray bending. Hence, this method was adjusted to the reality and led to better results compared to a constant velocity model. Linear V(z) is an attractive approximation for three reasons. First, this kind of velocity variation captures the first-order effect of the pressure and the temperature increases with depth. It does not require detailed knowledge of the subsurface velocities. Second, analytical expressions are available for the ray path geometry and travel times in such a medium. Third, the linear V(z) propagation allows turning waves which have potential for imaging dips beyond 90 degrees. Migration aperture is overestimated by constant velocity calculations, whereas the bin size is underestimated and this results in an increase in costs. On the other hand, calculations based on a linear velocity model require a less migration aperture and a larger bin size. The bin size and migration aperture are two sensitive economy parameters. Hence, using a larger bin size and a smaller migration aperture obtained from a linear velocity model, the cost of a 3-D seismic survey design will be decreased.

In blasting operations different proportions of the energy released are consumed in fragmenting the rock mass, heating up the surrounding and some portion of it is used in propagating blast waves, causing vibration of the particles in the nearby environment. However, reflections, transmissions and absorptions lead the attenuation of these waves. The influence of the extent of rock-mass discontinuities and different thickness of layers in fragmentation efficiency and stability of the mine steps are important for controlling the ground vibrations in the vicinity of blast holes. Hence, predicting the influence of blast-induced waves on the surrounding should be carried out prior to the operation for safety concerns. The main criterion for evaluating the damage caused by the blasting includes the particle displacement analysis, peak particle velocity, particle acceleration, changes in the applied stress waves and frequency contents. Research activities in this area could be classified into the followings: field studies, experimental investigations, analytical and numerical methods; the first two are expensive, and the analytical methods are often based on the unrealistic simplifications with respect to the rockmass behavior. However, the numerical methods by far offer a cost effective, speedy and reliable analysis. In this article, the effect of filling material thickness on the wave propagation of PETN blasting in a vertical discontinuity in plexiglass has been investigated using UDEC simulation. For this purpose, a uniform normal pressure dynamic of 11.0GPa was applied on a 5.08mm diameter blasthole wall. This dynamic pressure was obtained using a semi-empirical Liu and Tidman correlation. The shockwave caused by the blasting was assumed to be a triangular pulse, having a 0.05ms duration. A polymeric plexiglass, with dimensions of 230×114.3 mm was used for this study. Super glue was used as the filling material. Having considered the fixed geometrical involved, the influence of the filling material thickness (5, 10, 15, 20 and 25 mm) were investigated in a cylindrical blasthole vertically drilled (with large length to diameter ratio and complete decoupling). Peak particle horizontal displacement, peak particle velocity and induced stresses on either sides of the vertical discontinuity were measured in the elastic behaviour mode of the surrounding media. The energy reflection coefficient (ERC) and the energy transmission coefficient (ETC) obtained from the numerical analysis showed a good agreement with those of the analytical ones. However, the ETC in the numerical method decreased from 28% to 8.7% when filling material thickness increased from 5 to 25 mm, while this remained constant at 50 % for analytical method. An exponential correlation has been proposed showing the relationship between the ETC obtained from the numerical and analytical methods, with a correlation coefficient of 0.9962. Increased filling material thickness led to nonlinear reduction of both PPV and stress wave, and considering the constant reflection coefficient, it also led to the transmission coefficient being reduced exponentially while energy absorption increased exponentially, too.

Most geologic changes have a seismic response but sometimes this is expressed only in certain spectral ranges hidden within the broadband data. Spectral decomposition is one of the methods which can be utilized to help interpreting such cases. There are several time-frequency methods including: short-time Fourier transform (STFT), continuous wavelet transform (CWT), Wigner-Ville distribution (WVD), S-transform, and matching pursuit decomposition (MPD). In this paper, we use the MPD method. This method is newer than the other time-frequency methods used in exploration seismology. Mallat and Zhang (1993) have improved time and frequency resolution simultaneously by using MPD method. In this method, a signal is decomposed into constructive wavelets. Time and frequency properties of wavelets are used locally for spectral decomposition. Pursuits are the algorithms which search the best time-frequency matching between the signal and a linear combination of selected wavelets from wavelet dictionary. Matching pursuit which is an iterative procedure optimizes signal estimation by each new wavelet chosen from a dictionary. These wavelets combined linearly to obtain the best match with the signal. A signal should expand to waveforms which their time-frequency properties could be matched to local structures. Such waveforms are called time-frequency atoms. There are many approaches to match wavelets of dictionary to a seismic signal and to obtain time-frequency spectrum in matching pursuit decomposition. The base of all approaches is the Mallat and Zhang’s algorithm. However, computing time of the original algorithm is very high due to many iterations and that is why particular conditions have been applied in different researches to limit the matching pursuit algorithm for obtaining the lower performing time. In this work, particular conditions are rather similar to Wang’s (2007) method. On seismic data, layer thickness is described on the basis of the seismic travel time. When a layer with different properties has a thickness by one-fourth wavelength, top and base reflections will interfere constructively. For thin layers less than tuning thickness, combined seismic amplitude decreases with thickness (when reflection coefficients of the top and of the base are opposite). Generally, spectral decomposition has many applications in interpretation of seismic sections and so there will be extra needs to study and develop them. Thin layers and many stratigraphic hydrocarbon reservoirs are beneath the threshold of the vertical seismic resolution (tuning thickness) and because of their low thicknesses, they are not resolvable. For this reason, mapping the small-scale geological structures is one of the important interpretational cases. When the thickness of a thin layer decreases pick frequency slightly increases. In this work, this issue has been used to detect thin layers by time-frequency spectrum and by single-frequency sections obtained from MPD. In this paper, we investigated the performance of the matching pursuit decomposition for time-frequency analysis of seismic sections to delineate and detect thin layers on synthetic data (including simple thin layer model and also wedge model) and real data. It is observed that the interpretation of thin layers is simpler by single-frequency sections. It is shown that for a simple thin layer if considerable frequency in single-frequency sections increases, ability in resolving layer interfaces would be increased. In the wedge model, as the frequency increases resolution threshold of layer interfaces moves to a lower thickness and therefore it would be possible to detect lower thickness layers. The tuning thickness has been decreased from 19 meters in original seismic section to 12 meters in 80 Hz frequency section. In the real data, it is shown that when a thin layer is not resolvable in a seismic section it might be detected using the MPD method. In this case, by providing 20, 40, 60, 80 and 100 Hz single-frequency sections when high frequency sections are studied, interfaces of thin layer are appeared gradually. It is concluded that time-frequency sections are useful instruments to detect and delineate thin layers.

Any 3D seismic survey can have an acquisition footprint. Acquisition footprint is an expression of the surface geometry (most common on land data) that leaves an imprint on the stack of 3D seismic data. Often we recognize it as amplitude and phase variations on time slices, which of course display the amplitudes within our data set at a specified two way time (Cordsen, 2004). On the other hand, acquisition footprint is often used to describe amplitude stripes that appear in time slices or horizon slices produced from 3D seismic data volumes. Although acquisition design of a 3D survey has a major influence on the nature and severity of a footprint, improper data processing techniques such as the use of incorrect normal moveout (NMO) velocities can also create footprint (Cordsen, et al., 2000). More seriously, on horizon slices, footprint can interfere with and confuse stratigraphic patterns. Many different contributions to the generation of acquisition footprint are possible. These can be divided into two main categories: (1) geometry effects: line spacing, fold variations, wide versus narrow patch geometry, source generated noise and variations of offset and azimuth distribution. (2) non-geometry effects: topography, culture, weathers, surface conditions and processing artifacts. In this article we study the effects of these parameters for 3D seismic survey in AHWAZ oil field and calculate acquisition footprint noise in this field. Most of the time the acquisition footprint is based on the source and receiver line spacing and orientations. The larger the line spacing, the more sever the footprint. In land situations where access is very open and, therefore, the lines are very regularly spaced, we may be able to recognize the footprint very clearly. Because the geometry is regular, the footprint also will have the same periodicity. Fold variation themselves are the simplest form of an acquisition footprint. Fold changes with offset (or rather mute distance from the source point); each offset range, therefore, has differing fold contributions (Cordsen, 1995). Because each individual bin of a 3D survey has changing offset distributions, the CMP stack of all traces in a bin will display bin-to-bin amplitude variations. This variation in itself can produce an acquisition footprint. Generally it has been thought that acquisition footprint is far worse in the shallow part of the seismic and therefore, of course, the geological section, mainly because the fold is lower, and amplitude variations necessarily are far more dramatic. Offset limited fold variations alone may produce a recognizable footprint. The higher the fold, the better the signal to noise ratio; therefore, less footprint is evident. Wide recording patch geometries are far more accepted these days than narrow patch geometries (Cordsen, et al., 2000). The reasons are numerous and ranges from reduction in acquisition footprint (particularly that due to back-scattered shot noise) to improved statics solutions and the availability of large channel capacities on seismic recording crews (also leading to higher fold). In addition to the impact of the fold variations, acquisition footprint is made worse by source generated noise trains that penetrate our data sets. The lower the signal to noise ratio is, the worse the footprint will be. Unfortunately, the noise typically has a low frequency content that is much less affected by attenuation. Therefore the noise becomes more prominent relative to the signal content deeper in the section. Our experiences have shown that acquisition footprint problems can be just as prevalent in the deep section as they are in the shallower section. If surface access is poor because of topography variations, tree cover, towns, etc., we irregularize the geometry by moving source points to locations of easier access, and therefore mask the acquisition footprint. It is still present, however. The footprint is just so much harder to identify. Weather and surface conditions may also impact the recorded amplitudes. One can model an acquisition footprint by creating a stack response on either synthetic or real data. We stack the data in a 3-D cube and display the resulting seismic data over a small time window. The best input is a single NMO and static corrected, offset sorted 2D (or 3D) CMP gather. These traces will be applied to each CMP Bin in the recording geometry. In summary, we should attempt to minimize footprints by employing proper seismic acquisition and processing techniques, but if a footprint persists in the stacked data, there are ways to filter the data and mitigate its effect on geological interpretation. In this article we optimized acquisition parameters in order to minimize acquisition footprint noise for 3D seismic survey in AHWAZ oil field and finally with 3D modeling by OMNI software we saw the intensity of this noise in our seismic sections.

Acoustic waves propagated in gas and lossy media are less attenuated and more reliable than elastic waves. Accordingly, the detection and measurement of acoustic waves are more precise than for elastic waves. Consequently, reservoir characterization using processing techniques and seismic data inversion are based on propagations of acoustic waves. The wave field in acoustic media is described by a scalar quantity rather than by a vector. The tau-p transformations in the near field for the acoustic and elastic wave equations are similar, as they both yield the eikonal equation in isotropic media. The reflection and transmission behaviors of waves, however, differ considerably in each of the two media. In acoustic media, all P-wave energy is conserved and as a result it can be used for near offset tau-p modeling. Fryer (1980) developed a reflection method for modeling the VSP data in tau-p domain. The main idea of using tau-p domain is to investigate the amplitude variations as a function of near offset, changes of phase, estimation of attenuation factor (1/Q) and separation of primary multiples. Laboratory measurements and well log data have shown that the Q factor depends on the type of media and also the percentage of saturation. Therefore, Q is a very strong factor for characterization of reservoir gas zones. Due to its sensitivity, this factor is used in our two different modeling programs. In this study, the VSP modeling was used in the t-x and the tau-p domains. One of the most important parameters in this modeling is the velocity model. To generate the velocity model and synthetic seismograms, a computer program was developed in t-x and tau-p domains. Then, for two exploration wells, the VSP models were compared using real data. Based on the above algorithm, a software package was developed using a finite difference method in the tau-p domain. The slowness-time reflectivity method was used to calculate tau-p synthetic seismograms with the inclusion of the attenuation factor (1/Q) in lossy media. For large acoustic impedance contrasts, the attenuation factor occurs as an amplitude decay and phase rotation for some range of high frequencies. First, the upcoming and downgoing VSP synthetic data for side locations along each well were modeled and compared with the VSP real data. Second, the normal incident seismic sections based on well logs were compared with the tau-p sections derived from the acoustic and viscoacoustic media. The VSP data and seismic modeling techniques were used for the detection of gas zones in two wells in one of the south Iranian reservoir. The models of traveltime and amplitude changes of VSP data in the tau-p and in the t-x domains proved to be effective techniques for detection of gas zones from the VSP seismic data. Using this technique, gas zones in the reservoir can be very reliably detected using acoustic waves. The aforementioned procedure were applied in verification of different media. A comparison of the VSP data generated in the tau-p and the t-x procedures can yield valuable results. Results show that modeling in the tau-p domain using a localized slant stack is faster and more reliable than are conventional methods. Additionally, wave energy characteristics and amplitude changes of seismic waves in two different acoustic and viscoacoustic media were investigated using a 2D acoustic wave algorithm in lossy media. By using acoustic, viscoacoustic and anisotropic models in the tau-p domain and comparing them with normal reflections and VSP data, one can detect saturated gas zones. The synthetic VSP in the tau-p domain definitely helped to verify changes in amplitudes and phases in two VSP well data sets investigated here. Using this technique, it was found that reservoir gas zone can be reliably detected by acoustic waves. Furthermore, it was established that these waves can be used for better comparison with real VSP data in the reservoir gas zones.

During the past two decades, marine researches have focused on gas hydrates because of many reasons that the most important of them is future energy resource potential. Gas hydrates are ice-like crystals that form a rigid cage of water molecules and entrap hydrocarbon and non-hydrocarbon gas by hydrogen bonding. There are many methods for prospecting gas hydrates. Seismic methods are the most applicable tools to study them. The presence of the suitable thermodynamic conditions, gas and water in appropriate amount and gas migration pathway from depth to sea floor sediment are necessary for growth and stability of gas hydrates and evaluation of these conditions are necessary for prospecting gas hydrates. The occurrence of gas hydrates and free gas in host sediments changes the elastic properties and makes them detectable with seismic methods. The important seismic indicators of gas hydrates are bottom simulating reflector, flat spot and bright spot. Therefore, the presence of the gas hydrates in host sediments are detectable with seismic indicators and the study of the gas hydrate anomalies are applicable with seismic attributes. In this article, the appropriate conditions for growth and stability of gas hydrates in OmanSea were examined with 2D seismic sections, the occurrence of the gas hydrates in sediments was proven with seismic indicators and the properties of their indicators were studied with reflection strength and apparent polarity attributes. Also the effect of the gas hydrates in attenuation of seismic wave amplitude was studied with spectral decomposition attribute. The results of this study prove the presence of gas hydrates in OmanSea.