The migration process removes the effect of wave propagation from the recorded seismic data. As an application of this operator, seismic events will move towards their correct positions, diffractions have to collapse and dipping events have to move to their correct subsurface locations. In this article different algorithms of the migration operator have been considered and applied on real data. Based on the results, the Kirchhoff migration, which is the most common algorithm, was chosen and used to apply for pre-stack depth migration on real data. The data obtained from an oil field in the southern part of Iran. Subsequently the obtained results were compared.

The prestack seismic inversion converts seismic data to the physical properties of a rock such as sonic and shear impedance and density. It provides accurate information for predicting lithology changes and fluid types. In this paper, well logging data is used to construct synthetic seismogram. In the final stage, by transforming offset domain to angle domain and using the well information to the prestack seismic inversion, the extracted petro-physical parameters are discussed. The applied average wavelets of 7 wells in prestack inversion were in the angles of 5-9, 9-13, 13-17, 17-21 and 21-25 degrees. After wavelet extraction, low frequency acoustic impedance models and shear impedance and density were made as one of the inversion inputs. We built these, low frequency initial models using sonic log, shear impedance log and density log from well data. There are two techniques for doing the pre-stack seismic inversion: simultaneous and elastic inversion. These technics require wavelets and background model. Simultaneous pre-stacking inversion is defined by seismic trace angle, logarithms of P-impedance and S-impedance, and extracted wavelet but Elastic inversion uses a transformation of the Zeoppritz equations In other words, by performing the prestack inversion, the sonic and shear impedance and density are calculated using above mentioned equations. In this paper, prestack seismic inversion method was carried out in one of Iranian oil fields in Ghar-Asmari Reservoir and Jahrum Reservoir formations. The results showed that the presence of oil and gas in the Ghar-Asmari zone caused the reduction of the sonic and shear impedance and density. From Ghar-Asmari zone to Jahrum, the amount of the sonic and shear impedance and density increased. Also, the results of sonic impedance cross-plots versus the ratio of sonic-to-shear wave velocity were determined based on porosity variations and water saturation changes. In Ghar-Asmari zone, porosity is high and water saturation is low because of the presence of gas and oil in this section. From Ghar-Asmari zone to Jahrum, water saturation increases and porosity decreases. Hence, using simultaneous inversion, the hydrocarbon reservoir was identified.

Summary In this study, a novel approach for linearized amplitude versus offset (AVO) inversion in a Bayesian framework is presented. Objective is to estimate the posterior distribution of three elastic parameters, P wave velocity, S wave velocity and rock density. The methodology is based on the convolutional model and a weak contrast linearized approximation of Zoeppritz equation for PP waves. In this study, assuming a priori Gaussian distribution for input parameters, and also, a Gaussian distribution for seismic misfit function, Bayesian equation also yields a Gaussian distribution for posterior parameters, which can be analytically computed. This analytic solution is a rather fast approach for inversion of elastic parameters along with their uncertainty distribution. This methodology is tested on both synthetic and field data sets and in both cases yields reasonable solutions. In the current study, assuming a weak contrast model for rock properties, a linearized AVO approximation of Zoeppritz equation is used in a Bayesian framework to invert prestack seismic data for the above-mentioned three elastic parameters. The methodology is tested on both synthetic and field data sets. The results show preferably good matches with the true data. Introduction Inversion of seismic AVO is a way to estimate elastic parameters from prestack seismic data. This technique can be solved in both nonlinear (Dahl and Ursin, 1991) and linear (Smith and Gildow, 1987) approaches. Lortzer and Berkhout (1993) also used the same methodology as presented here, but they used the relative contrast of elastic parameters instead of their absolute values. Methodology and Approaches Assuming a Gaussian distribution for elastic parameters and seismic noise, and also, a linearized formulation for forward modeling, distribution for posterior parameters will also be Gaussian. Based on this methodology, the mean and covariance matrices of prior distribution are estimated from well data. Then using the linearized formulation of AVO, the mean and covariance matrices of observed seismic data are estimated. Having the statistical parameters of prior and likelihood functions, the statistical parameters of posterior distribution is analytically yielded based on Bayesian formulation. The covariance matrix of posterior distribution gives an estimate of the uncertainty in the elastic parameters. Results and Conclusions A Bayesian AVO inversion method was proposed and tested on both synthetic and field data sets. In case of synthetic data, the estimated parameters fitted the true values almost exactly. The result for the field dataset was also reasonable and matched the well log data relatively well except in some locations where prestack seismic data were not preconditioned very well. The initial models used for this methodology does not need to be detailed at all and very simple initial models such as constant or linear values lead to good estimation of the posterior distribution. Therefore, this approach can be a good choice for generation of pseudowells where not a rich dataset is available.

The time for easy oil discovery and production for National Iranian Oil Company (NIOC) is over. This means that the oil is no longer discovered in structurally simple, i. e., almost flat environments like south of Khuzestan province (south west of Iran). This comes along with the fact that Iran’ s biggest oil reservoirs are in this area, and they are passing half of their life cycle. These giant reservoirs are still producing more than half of oil production in Iran. There is significant uncertainty in future of Iran oil and gas exploration. In fact, the rate of drilling a successful well by NIOC exploration directorate is dramatically reduced in past few decades. The reason is that nowadays NIOC is drilling in structurally complex areas such as Zagros thrust belt mountains with rough topography and complex geology. Uncertainty in the drilling arises mainly from many factors, one of which is the processing tools in NIOC repository that belongs to at least 30 years ago. These tools belong to the time when NIOC used to drill for easy oil. In addition to the processing, other parts such as exploration techniques need to be revised in structurally complex environments. This article briefly looks at processing tools, which are critical to be used by NIOC to be able to significantly improve the drilling success rate.

Migration is a process that reconstructs an image of the earth's reflecting structure from elastic wavefield energy recorded at the surface in seismic traces. In seismic imaging, conventional processing is concentrates on producing a stacked section from common mid point gathers and is followed by a poststack migration, while in a prestack migration, first the events are migrated one by one, then those are stacked and the energy the common reflection point gathers goes to the surface of the earth. In this paper the effects of both post-and pre-stack time migration methods are compared by use of the Kirchhoff summation method on a seismic line in the central Iran area. The method of data collection is offend and the foldage is 48. The source of energy in acquisition is dynamite. The work is implemented by using PROMAX 6.0 software on ULTRA SUN system. The results are as follows: Continuity of reflectors will be improved after prestack migration. Velocity analysis after prestack migration is improved and provides a better harmony of the velocities. Because of the increasing of the reflector's amplitudes continuity, some geology structures appeared after prestack migration.      

Fractures are the main flow channels for oil and gas in the Permian Jiamuhe Formation volcanic reservoir in the Jinlong 2 Oilfield at the northwestern margin of the Junggar Basin. According to core, thin section and image logging data analyses, the fractures in this area are dominantly semifilled or unfilled high-angle fractures, followed by semifilled low-angle oblique fractures, and vertical fractures. The image logging results show that the fractures are oriented nearly east–west, approximately parallel to the direction of the present-day maximum principal in situ stress, and they have good flow effectiveness. The volcanic reservoir fracture development is mainly affected by structure and lithology. The fractures are mostly distributed in strips along the faults. The closer to the fault, the greater the structural curvature and the more developed the fractures. The fractures of intermediate–acid volcanic lava and pyroclastic lava are well developed in the study area. Additionally, the fracture development characteristics of a single well are determined by calculating the fracture density, fracture dip angle, and fracture porosity. Combined with the prestack seismic prediction method, i.e., amplitude versus azimuth (AVAZ), the attenuation initial frequency attribute is selected to predict the fracture distribution characteristics of the Jiamuhe Formation volcanic reservoir.

PSPC or split step Fourier migration is a method based on downward continuation of seismic wave field in frequency space and frequency-wave number domains. Compared with the phase shift method, this method is a superior algorithm with the ability to handle lateral variations in the velocity field. Therefore, the method is a depth migration, which works in the post stack domain. In this study, the ability, precision and validity of PSPC migration in the processing of real data sets with strong lateral velocity variations is verified. In order to achieve this objective, the velocity section used by migration algorithms has been improved in different steps by the iterative method of residual move-out analysis through applying pre stack Kirchhoff time migration. The improved velocity section was then used to apply pre stack Kirchhoff depth migration to original CMP gathers and PSPC migration to the stack section and the results were compared.    

Poststack seismic inversion generally transmutes seismic amplitude to P-wave acoustic impedance, which lacks low-frequency component due to the stacking process. This component should be compensated using well logs as a priori constraint. If this low-frequency trend is known with adequate accuracy, poststack inversion could produce precise results. Nevertheless, in most cases, the mentioned information are far from the true model. In such cases, poststack inversion results could have high uncertainty. Because there is no mode conversion at normal incidence, postsatck inversion is completely acoustic, hence P-wave impedance is the only information which can be extracted from poststack inversion of P-wave data. In simulations prestack inversion, in addition to the P-wave acoustic impedance, S-wave information, density, and Poisson’ s ratio can also be derived from prestack data. Thus, prestack inversion can be used to get more information than poststack inversion. The two-step process of acoustic impedance and shear impedance by model-based inversion is replaced by one-step pre-stack simultaneous inversion. In order to apply simultaneous inversion method to our prestack seismic data, the data should be transformed from offset domain to angle domain as the first step. A useful approach is to calculate offset as a function of incidence angle, using Snell’ s Law to follow the ray path through the layers if velocity information is available. The next step is to build initial models of acoustic impedance, shear impedance, and density. We built these initial models using sonic log, Delta-Time Shear (DTSM) log and RHOB log which were available in the interest area. There are two relationships that should hold for these wet rocks. The first relationship uses this fact that in wet clastics the ratio of the s-wave velocity over p-wave velocity should be constant within a rock layer. After reformulation of the mentioned trend, one can understand that the natural logarithm of shear impedance has a linear relationship with the natural logarithm of acoustic impedance. The second fact uses Gardner equation. After reformulation of the Gardner relation, it is understandable that the natural logarithm of density has a linear relationship with the natural logarithm of acoustic impedance, too. We determined k, kc, m and mc which respectively are slope of the natural logarithm of shear impedance against natural logarithm of acoustic impedance, intercept of the natural logarithm of shear impedance against natural logarithm of acoustic impedance, slope of the natural logarithm of density against natural logarithm of acoustic impedance, intercept of the natural logarithm of shear impedance against natural logarithm of acoustic impedance. Besides, we need a set of angle-dependent wavelets which are derived from angle stacks. Hence, we built three angle stacks; near-angle stack (0 to 11 degrees), middle-angle stack (11 to 20 degrees) and far-angle stack (20 to 29 degrees). Using these angle stacks, we built three statistical angle-dependent wavelets from three angle stacks. Finally, with log information, we built an initial model for acoustic impedance and tried to solve the inversion matrix using conjugate gradient method. Solving the equation, we can derive acoustic impedance, shear impedance, and density sections simultaneously from prestack data. Using simultaneous inversion, we identified hydrocarbon reservoir.

This work used the prestack seismic data of Penobscot reservoir in Canada to identify gas-bearing layers using amplitude versus offset (AVO) processing. Prestack seismic data was used in ten different angles from 3 to 30 degrees to estimate the reflection coefficients of compressional velocity, shear velocity, density and acoustic impedance. Least square and Tikhonov regularization solutions were applied to invert the Aki & Richards equation. Model covariance matrix showed that Tikhonov regularization had less variance of estimation in comparison to least square solution. Therefore, Tikhonov method was used to calculate the seismic attributes in the studied cross-line. The results showed that reflection coefficient of shear velocity and density could accurately differentiate gas-bearing layers. Introduction The information driven from wells are capable of determining the reservoir characteristics more preciously and distinctly than seismic data, but such data show much lower resolution horizontally. Therefore, seismic data have gained attention due to their better lateral resolution and their ability to provide the structural information for geologists. The objective of seismic AVO (amplitude variation with offset) inversion is to achieve the physical and elastic properties of subsurface layers using the prestack gathers. Its theory is based on Zoeppritz equations, which provides exact descriptions of the AVO phenomena at elastic interfaces. However, it is too complicated to obtain stable seismic inversion results. Thus, the approximation proposed by Aki and Richards is widely used in inversions. Methodology and Approaches Aki and Richards approximation has three unknown seismic attributes (the reflection coefficients of compressional velocity, shear velocity and density), which should be derived from seismic data. This study applies the algebra technique to obtain the unknown parameters. A typical feature of inverse problems is that they are ill-posed, and a unique solution may not exist and small errors in the data may cause large variations in the estimation of inverse modeling parameters. One of the best known and most used regularization methods is Tikhonov regularization. This research work estimates the seismic attributes in the Penobscot region using Tikhonov regularization. Results and Conclusions Well L30 was drilled in Cross-line 1153 and gas-bearing layers were found in time range of 2000-2036 ms. In general, gas layers could decrease P-wave velocity and slightly increase S-wave velocity. Therefore, the variation of the P-wave and S-wave reflection with offset can be used as a direct hydrocarbon indicator. The reflection coefficient of shear velocity and density could detect the gas layers with high accuracy.

Nowadays, quantitative methods for seismic data interpretation are mostly used instead of qualitative evaluations for the description of reservoir from exploration to production phase and, over time, it will be more important in upstream oil industry. One of the most prosperous methods is AVO analysis that considers the variation of reflection coefficients versus offset between the source and the receiver. This method uses prestack data to identify hydrocarbon in reservoir and can be used as a direct hydrocarbon indicator in clastic rocks. In this paper, AVO analysis method has been described to detect hydrocarbon reservoir and AVO analysis was applied to the Ghar sandstone reservoir in Hendijan oil field. This study is carried out using 3D prestack data and well logging data. At first, forward modeling has been performed using well logging data from wells. Class 1 AVO anomaly has been recognized for this reservoir. After the forward modeling step, fluid replacement modeling (FRM) in reservoir zone has been applied and the best attributes, which had a great sensitivity to fluid, were distinguished. But FRM shows that the attributes are not sensitive to changes in fluid type. AVO anomaly on upper boundary of Ghar sandstone is affected by changes in lithology. Also, intercept-gradient cross-plot was produced by attributes. Reservoir area and zones has been discriminated with intercept-gradient cross-plots. For seismic data, various AVO attributes and cross-plots have been extracted by different methods and have delineated. Unfortunately, attributes and cross-plots were not able to detect fluids boundaries and fluid boundaries, because of the poor quality of seismic data. The results not only help to select new drilling locations more accurately, but the reservoir parameters can also be modeled with less uncertainty.