فهرست مطالب ramin nikrouz

Identifying and locating stratigraphic traps is more difficult than structural traps because they are dependent on facies changes and their distribution follow the condition of the basin and the sedimentary environment. Accordingly, the three - dimensional seismic data based on seismic attributes provide a better way to establish lateral relationships with geological features. The purpose of this research is to identify the stratigraphic meandering channels by applying appropriate attributes on seismic data. For this purpose, three dimensional data from one of the northeastern fields of the Iran were employed by using Petrel software. Afterwards, the relevant attributes were applied on the raw sections and their reservoir characteristics were interpreted and surveyed. Through stratigraphic interpretations, the geometrical position of the meandering channels located in the Shurijeh Formation was better revealed by the envelope and extended spectral decomposition attributes. The spectral decomposition attribute was effective in revealing the geometry of meandering channels in seismic sections, and the envelope attribute confirmed the existence of meandering channels by showing bright spots related to stratigraphic oil traps. These results show good agreement with the results of lithological, facies, reservoir properties, and petrophysical interpretations of Shurijeh Formation in the study area.

Acoustic impedance can be considered as a measure of rock strength to the propagation of waves, which is a product of the density and compressive wave velocity of the rocks. Geophysicists are trying to estimate the acoustic impedance of rock layers from reflective seismic data, called seismic inversion. The difference of acoustic impedance in the common layer of layers reflects the seismic waves. Acoustic impedance is the property of rock itself, and the harder the rocks are, the higher the acoustic impedance is. Seismic inversion can be used as a reliable method for (1) estimating reservoir properties quantitatively and (2) obtaining quantified values of acoustic impedance from seismic data, well-logging data, and interpreted horizons. In general, acoustic impedance has a strong relationship with petrophysical properties such as fracture, saturation and porosity. The purpose of this study was to estimate the initial model of acoustic impedance using different seismic inversion methods to determine reservoir characteristics and compare their results in the Whicher-Range field of Perth Basin, Western Australia. For this purpose, two wells and a seismic section from the field were used. All steps were performed using the Hampson-Russell software. First, the depth-time relationship between well logs and seismic data was corrected. Then, the compatibility of the synthetic seismogram resulting from the seismic wavelet convolution with the series of reflections produced at the well location was investigated with seismic data. Various inversion algorithms including Model-based, Band-limited, Sparse Spike (Linear Programming) and Sparse Spike (Maximum Likelihood) were inspected. The correlation coefficient in all methods shows an acceptable value. Using wavelet extraction and investigation of different inversion algorithms, the model-based method, as the best method with high correlation coefficient and less error among other algorithms, was used for initial modeling. In the interval of 2050 to 2250 milliseconds of the studied section, acoustic impedance decreases significantly, indicating porosity in the reservoir area.

Brittleness is one of the most important properties of the rock. Brittleness is a function of strength and indicates rock strength to deformation in the elastic modules. However, there is no direct and standard method for brittleness measurement but it can be done indirectly by using rock properties such as different ratios of compressive strength and tensile strength of rock to determine the concept of brittleness. The purpose of this study is to investigate the concepts of brittleness which are presented by the researchers and to use seismic inversion, multi-attribute analysis and neural network in the Whicher-Range field in Perth, Western Australia to estimate brittleness. The Perth sedimentary basin stretches about 100,000 square kilometers in the north-south direction of the western Australian margin. About half of this sedimentary basin is located 1 km deep in the sea. Whicher-Range gas field is 22 km south of Baselton and 200 km south of Perth. Two wells and one seismic section of Whicher-Range field are selected in this research. The lowest brittleness indexes in the first and second wells drilled 1 km apart, are 1.69 and 1.67 MPa using criteria. The highest values of the brittleness are 39.78 and 48.15 MPa, respectively, which are difficult for drilling. The starting point of the inversion process is to have post-stack seismic data, velocity model, well logs, and seismic horizons. The product of the density log and sonic velocity is equal to the impedance. Then the current impedance is converted from depth to time using the appropriate depth to time relation. As a result, the convolution of a suitable wavelet and reflectivity over time will produce the synthetic seismic trace. Adaptation rate between synthetic seismic trace and composite field seismic trace yields an acceptable result. Next, the initial modeling is performed using wavelet, geological model, and a model-based algorithm. Next, the acoustic impedance of the inversion process along with other attributes is used to construct the optimal combination of attributes to estimate the brittleness index. First, an attribute is selected that has the highest correlation with the target log and the least estimation error in the training step. Then the second attribute, which makes the best combination with the first attribute, has the lowest estimation error. Then each attribute step is added to its previous step combination until the resulting combination results in the lowest estimation error. The results based on this method are obtained by increasing the number of attributes and decreasing the estimation error, while the error in the validation stage until the optimal attribute combination, is ascending. In the next step, three types of neural network algorithm including probabilistic method, multi-layer feed forward and radial basis function are used to estimate the target parameter, with optimal combination of available attributes and the use of neural network algorithm training from the optimal attributes using the Hampson-Russell software. In the last step, multi-attribute analysis is compared with three neural network algorithms. The results indicate a higher correlation coefficient for probabilistic neural network than that of multi-attribute analysis for determination of the brittleness index.

Nowadays there are many attempts to exploring stratigraphic traps all over the world. Because of existing Sarvak reservoir rock Formation in central Persian Gulf which consists of carbonate with various lateral changes in the terms of lithology, there is high probability to form of these stratigraphic traps in this region. The main aim of this research is to recognize the oil and gas stratigraphy traps by using seismic attributes and petrophysical and geological logs in the Bahregansar oil field, NW of Persian Gulf basin. In order to reconnaissance these regions, petrophysical logs (density, acoustic, neutron and gamma) and seismic reflection data from three wells have been studied. On the other hand, the obtained data have been interpreted with some seismic volume attributes such as momentary frequency, domain and acoustic impedance, reflection coefficient, normalized amplitude and envelope amplitude attributes to show the situation of hydrocarbon basin in the study area.
By using seismic attributes and compare with geological information we can infer reliable interpretation because of some reasons. The first reason is seismic velocity allow us to understand the situation of lithology, fluid content and abnormal pressure or temperature. The second reason is lateral amplitude changes permit the inference of geological situation such as changes in porosity, existence of hydrocarbon and thickness of lithology. The third reason is seismic trace morphologies or interpretation sections allow us to recognize depositional environments or faults and fractures. Finally, changes in measurements direction permit to deduce velocity anisotropy, or fracture orientation.
The software which used in this study was Petrel. Density log is creating with using neural network method by application of well No.3 in this oil field. The petrophysical study of gamma log shows well identity of formation boundaries in sections. Also the use of cross plot graph of density-neutron logs applied to well recognized of the efficient zones in Gurpi-Ilam, Sarvak and Kazhdumi-sand Formation. The Turonian epirogenic event is caused erosion of top of the Sarvak Formation and on the other hand Santonian epirogenic is caused hiatus stratigraphic trace in the lower part of Gurpi and Ilam Formation. By seismic volume attributes interpretation a number of the stratigraphy traps detected over some seismic sections. Furthermore, recognizing a main fault which exists in the western part of Hendijan oil field has main role in the changing of lithological effect in continuity quality of seismic reflection.
In order to increase interpretation accuracy, some seismic inversion has been made on considered sections and the obtained data have been compared with petrophysical logs and seismic attributes. By doing this research and interpretation of sections two type of stratigraphic traps recognized. The first type is oil trap related to the top of Turonian unconformity (truncation) which exists in the eastern part of fault. The second is relation to narrowing part which is belonging to the above of the reservoir layer (pinch out) under Turonian unconformity in the western part of seismic section. Meanwhile the study shows that there is an oil trap as hydrocarbon accumulation in the upper part of Kazhdumi sand formation seismic subunit acting like stratigraphy trap in the above unconformity. In general, all of oil traps recognized in this study were composed of anticline structural dip and on the other hand existence of faults has a main role of geological structure in this region. The obtained results clearly demonstrated the shape and the geological situation of the existing structural traps in the studied area.

Many steps of seismic data processing sequence suppose that data sets are sampled in time and spatial dimensions uniformly. Today, this assumption is true but only in time dimension. Modern seismic exploration equipment permits seismic data sets to be sampled uniformly and densely in time dimension. However, along spatial dimension uniform and dense sampling are not possible because of operating constraints, failure of equipment, topography conditions or commercial problems. It has been proved that the results of most of seismic data processing techniques are dependent on regularity, adequate sampling and density of input data sets. The fact that we need to interpolate seismic data sets causes several new-born approaches in this field. In most of the available seismic processing software, this task is done by ‘binning’ the data. This operation is one of the error sources of seismic sections. Moreover, there are some other different computational techniques to interpolate and reconstruct seismic data on a regular grid. Some of these approaches reconstruct seismic data at the given points using physical concepts of wave propagation and solving Kirchhoff's formula. In spite of practicability of these methods, need of initial accurate information about velocity model, geological property and high computational efforts restrict the domain of operation for these methods. Nowadays various mathematical methods are provided using the design of prediction filters, mathematical transformation and some other methods use rank reduction of data matrix to interpolate seismic data. According to their utilized assumptions, computational cost, noise, sampling type, and density of input data, each of these methods have their own constraints in performance and artifacts in final results which should be recognized. In science and engineering branches, a well-known algorithm that deals with signals is Matching Pursuit (MP). Originally, MP has been introduced to time-frequency transformation and finding the frequency content of signals. This transformation represents a signal as a linear composition of vectors that are available in a complete bank of time-frequency atoms (also called Dictionary). MP is an iterative algorithm that at each iteration finds a base vector in the dictionary that best matches to the signal, then subtracts the image of signal along this vector from the signal and updates the signal. This process will be continued until the remained signal is negligible. Originally, to have a good decomposition, this dictionary should contain a vast amount and kinds of wavelets like Gabor functions that each has its own dilation, modulation and translation.Heretofore MP is used to produce a single frequency seismic attribute in geophysics. For seismic data reconstruction and interpolation purposes, sine functions are applied as base vectors. The process of interpolation by MP that uses sine functions needs to solve a Lomb-Scargle periodogram at each iteration that may need to have many computations. Due the lots of works that have been done on this subject, today multi-dimension and multi-component seismic data set can be interpolated using sine functions at MP. Other functions that can be used as MP’s base vectors are Fourier coefficients. Here, after some brief explanation about MP’s algorithm and formulations we use Fourier coefficients as the base vectors of MP, interpolate and reconstruct some synthetic and real two and three dimensional seismic data. Despite of some random noises that are due to calculation and other estimations,the traces are reproduced acceptably. The results show that amplitude and frequency contents of events are well preserved. The noticeable point is that the traces that reproduced at original sampling points are nearly identical to original traces. This property and ability to interpolate data with completely non-uniform sampling grid separates Fourier MP from many of previous interpolation methods. Cautiously picking of several base function simultaneously is proposed to reduce needed iterations and speed up the algorithm. Windowing the input data and using an antialiasing mask are proposed to achieve the assumption of sparse frequency content and linearity of events and remove aliasing effects.

Refraction profiles usually need to be between five and ten times as long as the required depth of investigation (Keary, 2002). By increasing the investigation depth seismic source waves travel long trajectory and because of attenuation cannot record strong head-waves. Sometimes it is not possible to use the appropriate source according to profile length. Whenever the seismic source energy is underestimated for long seismic refraction profiles/deeper targets or in case of highly absorptive media because of wave attenuation, the identification of first breaks becomes complicated particularly at far offsets. This can lead to inaccurate estimates of the deeper velocity distribution. Supervirtual refraction interferometry (SVI) based method is proposed to improve first arrival detection. The benefits are that traveltime picking errors can be greatly reduced for noisy head-wave arrivals, velocity and depth calculations will be correct subsequently. Application of the method will improve the signal to noise ratio of the first arrivals by a factor of ,where  is the number of postcritical source points used in SVI. In this study, supervirtual interferometry is applied to a numerical example and the field data from the south-west of Iran and its efficiency in enhancing of signal-to-noise ratio (SNR) of the first arrivals is presented.    Interferometry formula in x-ω domain in crroscorrelation method, based on wavefield reciprocity and time reverse invariance principle (Wapenaar and Fokkema, 2006), is: where  is position vector of source/receiver in Cartesian coordinates, A and B are sources in the media surrounded by  and  are Green’s function between the receiver A and the source B, Green’s function between receivers AB and source x with angular frequency ω respectively. C, ρ,  and  denote compressional wave velocity, density, real part and complex conjugate, respectively.    The product of Green’s function complex conjugate at  and Green’s function at  in frequency domain is equivalent to correlation in time domain. Thus by integrating cross correlation of recorded wavefield at  and  around the closed surface , which the sources are located on it,  becomes recoverable. There are two stages to apply SVI: Correlation every receiver record with the first to generate virtual refracted traces (correlations gather) and stacking the result for all post critical sources to enhance their quality.    Convoluting the first receiver data with the virtual traces to create supervirtual head- wave arrivals for each source. Since the arrival time as travel geometry of virtual head-waves for all post critical sources is the same, stacking can improve them and attenuate other waves. Because of random nature of incoherent noise, they are reduced by summation of virtual refracted traces. The difference in arrival times makes coherent noise weaker and migrates.  Thecomparison the SNR graphs of artificial and real data before and after using SVI provides the evidence for the success of the method. All the supervirtual traces SNRs are bigger than one and have sensible difference with initial traces especially for far offsets receivers.    In the interferometry formula the sources/receivers take place all around the closed surface , but in this work the sources are linear. The linear sources arrangement and their limited number cause unwanted effect on supervirtual records.

Refractor ambiguities are big problem in seismic refraction method especially in seismic engineering. There can be hidden subsurface geological phenomena such as hidden faults and shear zones which are not simply predicted by the travel-time graph or some geophysical methods. Head wave amplitudes are used to show the resolution of refractor ambiguities and the existence of anisotropy in complex geological area. Wave amplitude is proportional to the square root of energy density; it decays as 1/r. In practice, velocity usually increases with depth, and causes further divergence of the wave front and a more rapid decay in amplitudes with distance. Amplitudes measured from first peak to first trough and corrected for geometric spreading, can be demonstrated some subsurface information such as anisotropy. Meanwhile, amplitudes are not commonly study by researchers in seismic refraction studies, because of being the very large geometric spreading components due to variations related to wave speeds in the undulated refractor. The variations in amplitudes are described with the transmission coefficient of the Zoeppritz equations. This variation in velocity and density produces head wave amplitude and head coefficient changes in refractor, even with refractors exhibiting large variations in depth and wave speeds. The head coefficient can be approximately calculated by the ratio of the specific acoustic impedance in the overburden layer in the refractor. This study shows that there is a relationship between the amplitude and the seismic velocity which the lower the contrast in seismic velocity and/or density, the higher the amplitude and vice versa.

Discrete Fourier Transform (DFT) is the basic part of various algorithms of signal processing in many fields of science and technology. For analysis of signals with DFT, the length of discrete signal must be finite so signal to be analyzed must be divided to some windows. Consequently, spectrum leakage appears in frequency domain. Energy leakage of DFT spectrum can also occur due to non-uniform sampling in time or spatial domain and usually is more serious. The leakage can hide smaller spikes among actual spectrum and is an important factor that affects spectrum estimation. In practice, we should try to reduce the energy leakage of DFT spectrum to improve the resolution of frequency spectrum. For evenly sampled signals, suppressing approaches of spectrum leakage are diverse; most common method among them is windowing method. Study on how to suppress spectrum leakage of non-uniform Fourier transform is important both in theory and practice. Here we introduce and apply an anti leakage Fourier transform (ALFT) algorithms for suppressing spectrum leakage of non-uniform Fourier transform, improving the resolution of temporal frequency spectrum or spatial wave number spectrum. One of the areas of study that has the same problem is seismic exploration technology. Seismic data sets are generally irregularly sampled in inline midpoints, cross-line midpoints, offset and azimuth. This irregular sampling can limit the effectiveness of high end 3D de-multiple and imaging algorithms such as 3D surface related multiple elimination, wave equation pre-stack depth migration and many other processes. To overcome this issue, it is common in seismic data processing to use regularization and interpolation. Interpolation processes fill the missing traces and regularization transfer traces from their irregular recorded location to locations on a regular grid. We apply ALFT for seismic data interpolation and regularization that leads to reconstruction of seismic data on a regular grid. ALFT is an iterative algorithm that acts on frequency slices and reconstruct each temporal frequency spectrum along spatial dimensions. For an input data with N_p known samples, the original algorithm of ALFT can be performed as follow: 1- Computing Fourier components of the data using equation 1. (1) 2- Selecting the largest coefficient and adding it to the precomputed coefficients. 3- Updating data by subtracting the contribution of selected coefficient (equation 2) from input data (equation 3). (2) (3) 4- Iterating steps 2 and 3 until reaching the threshold. The idea of ALFT is simple and intuitive: first seismic data will be transformed to f -x domain, by applying DFT the f -k spectrum of data will be estimated. The largest Fourier coefficient is selected and subtracted from the input data. In the subsequent iterations, successive maximum components are subtracted until the norm of the residual is negligible. This iterative processes is able to recover a sparse spectrum that, when evaluated at sampling points, approximates regularly sampled data. This method relies on the common assumption that sparsely sampled data can be represented by a few Fourier components. ALFT can handle pure non-uniform seismic data and uniform seismic data with gap and missing traces. For regular data sets, by applying an anti-alias mask ALFT can handle steep dips. Generalization of ALFT to higher dimensions is simple and straightforward and for high dimension data ALFT can reconstruct very sparse data sets. Performance of the method was tested on both synthetic and real seismic data. We applied ALFT algorithm to reconstruction of synthetic and real seismic data sets. The results show the effectiveness of ALFT in interpolation and regularization of input data on any desired regular sampling grid. Compared to those interpolation methods that use FFT, ALFT has a slow procedure. However, computing the DFT’s in small windows of data sets, greatly reduces the computational cost of the algorithm. On the other hand, when the input data sets are sampled on a regular grid which has missing traces or gaps, one can use FFT instead of DFT to compute Fourier transform. ALFT reconstruction method suffers much less from edge effects and gibs phenomenon. The sequence of computing Fourier coefficients from maximum energy to minimum energy, and subtraction of contribution of them from remained data, plays a key rule in ALFT algorithm.