مجله ژئوفیزیک ایران
سال پنجم شماره 1 (پیاپی 8، بهار 1390)

Prediction of spatial distribution of porosity in a reservoir is an essential issue forestimating reserves and planning production operations. In most cases, however, lateralvariations of porosity cannot be delineated from measurements made at sparsely located wells. The integration of 3D seismic data with petrophysical measurements cansignificantly improve the spatial description of porosity. In the last two decades, severalmethods have been developed for the estimation of reservoir porosity. A number ofinversion methods are available in the industry to convert seismic amplitude into acousticimpedance. Acoustic impedance is indirectly related to porosity. Alternate integrativeapproaches for estimating porosity include geo-statistical methods, such as kriging andco-kriging using well and seismic data. One of the most accurate methods for estimatingreservoir parameters is the application of seismic attributes by a nonlinear estimator, suchas neural network or neuro-fuzzy model. Both neural networks and neuro-fuzzy modelscan be good estimators but the latter has the benefit of being interpretable. In this study, aneuro-fuzzy model called NEFPROX was used to estimate porosity in a gas reservoirlocated in the Gorgan Basin.NEFPROX is a Mamdani-type neuro-fuzzy model, so it has an advantage of beinginterpretable that makes it distinct from other type of neuro-fuzzy models. The timeconsumingcharacteristic of that method is irrelevant in this case because the prediction ofporosity is an offline prediction problem.The Gorgan Basin is located in the northern part of Iran, southeast of the Caspian Sea.This area consists mainly of three formations:the upper formation, called Clay-SandGroup , belongs to quaternary period. Below Clay-Sand Group  is a tertiary formationcalled Clay-Sand Group. A formation of Brown Beds is also a tertiary formation thatlies below Clay-Sand Group. All of these formations consist mainly of shale and sand.The discovery of gas in the Brown Beds Formation has persuaded explorationists toincrease their activities in the Gorgan Basin. The purpose of this study is to recognizeshale and sand bodies in the Brown Beds formation that consists of alternative sand andshale layers with variable thickness.First, a list of 20 seismic attributes was prepared to extract from raw seismic data inthe location of wells. Stepwise regression was used to select four appropriate attributes.The maximum number of attributes was set at four to avoid the model complexity. Theseattributes, in the order of priority, are instantaneous frequency, amplitude weightedfrequency, apparent polarity, and second derivative instantaneous amplitude. Then thesefour selected attributes were introduced into the neuro-fuzzy model as input to predictporosity as an output of the model.The neuro-fuzzy model was trained with the data of well GO3. Based on hydrocarboncore samples obtained from the Brown Beds formation and the potential of this formationas a probable reservoir, the data corresponding to this formation were selected as atraining data. A model blind test was also conducted with the data of well GO5.Porosity sections generated as the output of the model showed two low porosity sandyand shaly-sand channels in the Brown Beds formation. Lateral variations of thesechannels can clearly be recognized in these sections. The core samples available in wellGO3 (containing hydrocarbon) confirm the existence of the two inferred channels. Thisclear image of channels is simply unidentifiable from raw seismic data. Hence,NEFPROX can be very helpful in supplying valuable information about extent, shape andlithological variation of a reservoir. Finally, compared comparison was made between theperformance of the neuro-fuzzy model and regular neural networks in estimation ofporosity. The comparison indicates that the accuracy of the NEFPROX estimation isequal to that of MLP and is greater than that of RBF.

Numerical weather prediction (NWP) models without initialization techniques may beresult in unreal and inaccurate data. Many initialization methods, such as linear andnonlinear normal mode initialization, have been developed and applied in the field ofNWP by atmospheric researchers and modelers. In application, such techniques are very complex and expensive. One of the most efficient and simple techniques which can beused in operational forecasting is digital filter initialization. Digital filter initializationmethods are applied to eliminate non-physical and high frequency waves from NWPmodels. These unwanted waves can affect the results of the models and cause its results todepart from real world and observed conditions.In this paper, different filters (1- uniform filter, 2- Lanczos filter, 3- Hamming filter, 4-Blackman filter, 5- Kaiser filter, 6- Potter filter, 7- Dolph [Dolph-Chebyshev] window, 8-Dolph filter and 9- Recursive High-Order filter) are theoretically investigated.The theoretical study of these filters shows that the Dolf filter works better than theother filters. This superiority can be verified using a digital filter initialization techniqueassociated with the Dolf filter in the weather research and forecasting (WRF) model andinvestigating its results. Subsequently, the digital filter initialization methods provided inthe WRF model are tested for the region of Iran. Three different digital filter initializationtechniques, namely the digital filter launch, diabatic digital filter and twice digital filterinitialization, with nine aforementioned filters were prepared in the WRF model. TheWRF model was set with a 45-kilometer grid size for the region at 12-50 oN and 12-87 oS.The WRF model was run over this region with and without a digital filter initializationtechnique. In general, the initialization of the NWP models influences the first hours ofprediction of the meteorological parameters. In this study, two parameters, includingsurface pressure and rainfall, were considered as indicators of the effects of digital filterinitialization methods on the results of the WRF model. Therefore, the obtained resultsare investigated and compared for surface pressure fluctuation and rainfall.All results indicate that applying the digital filter initialization effectively liminatesnonphysical waves from surface pressure fields, especially in the first hours of prediction.This was determined by studying three parameters, including surface pressure fluctuationin some points, derivative of surface pressure fluctuation in some points, and integratedderivative of surface pressure. It was found that the twice digital filter initializationassociated with the Dolph filter works better than the other techniques and filters.For rainfall, three- and six-hours predictions of cumulative rainfall were investigated.The results of rainfall prediction with WRF model using digital filter initialization werecompared with the results of WRF model without digital filter initialization and observedstation data. This comparison showed that the twice digital filter initialization associatedwith the Dolph filter has its maximum effect during first three hours and in the secondthree hours has a minimum effect among other techniques. This means that unwantedfluctuations are eliminated properly during the first three hours. Also, a comparison ofrainfall prediction results with observed station data indicates that the diabatic digitalfilter initialization associated with the Dolph filter has the minimum root mean squareerror. Among digital filter initialization techniques studied, the digital filter launch hassudden effects on the amount of rainfall predicted during the first three hours ofprediction time, so this can induce significant errors in results of the model.

Remote sensing is the science and art of the gathering information about an object, area or phenomena by means of a device that is not in contact with the object, area andphenomena under investigation. In remote sensing, the interaction between an energysource (electromagnetic) and surface features (trees, rocks, soil) is recorded. Imageprocessing can be considered as an image-to-information-mapping procedure, whichprovides the image for future analysis. Image processing techniques usually employ a filter for selecting desired information. Filters are used for spatial image enhancement, forexample, to reduce noise or to sharpen blurred images. Such a processing techniqueworks on pixel values and produces a filtered image which the pixel values of whichdepend on its former neighbors. In this regard, the sunshading, viewshed, edgeenhancement, and majority filters are most commonly used as image processing filters. Incontrast with remote sensing, geophysical methods employ human agents for datacollection and interpretation. Potential geophysical field data, such as aeromagnetic andgravity data are collected by both government Geological Surveys and mineralexploration companies for a wide range of purposes, including geological mapping andmineral exploration. Since the measured data are presented in the form of a contour map,it can be considered as an image and image processing can apply on it. In addition, theseimages possess a large number of geological features, such as dykes, faults, and folding,which can disappear with disturbing noises. Consequently, the use of image processingtechniques for the purposes of noise reduction and edge enhancement of differentgeological features is necessary. In geophysical literature, derivative filters and localphase filters are the most applicable filters applied on potential field images. The mainintent of this study is to develop remote sensing filters, mentioned above, on potentialfiles images.

Seismoelectric modeling is a prospecting method, based on seismic electromagnetism inwhich seismic sources are used to generate this phenomenon. When seismic waves arereleased within a fluid-saturated sedimentary material, a small amount of fluid-solidrelative motion is induced. The seismic force causes this effect through a combination ofrelative gradient acceleration fluid and the pressure of seed waves. Most of the surfacegrain, in contact with a liquid electrolytes, are chemically bound to the surface load. The thin layer of charged fluid around each grain is balanced with a scattered distribution ofmobile ions with opposite charges. The scattered ions in this layer are free to movethrough the fluid so that seismic waves create an electrical current flow. This inducedseismic current flow acts as a current source in Maxwell's equations, which is the base ofelectromagnetic wave coupling with a seismic wave. The peaks and troughs thataccumulate are P-type waves. The electric field is produced inside the wave (seismic),which is vertical on the wave sinciput. This flow creates convection. In a homogeneoussubstance, the current flow is equal to convection so that the overall flow is zero; nomagnetic or electromagnetic fields are created independently., Hence, the electric fieldthat exists within the wave moves as a part of the reaction without spreading outside thewave. Therefore, a simple pair of electrodes can act as a geophone to measure the electricfield inside the P-wave when it passes throughthem. The S shear wave does not separatecharges in a homogeneous substance without divergence, so they cause no fluid repletion.The relative motion of fluid to solid is due to seed accelerations. The induced current flowcreates magnetic fields that, in turn, produce a small electric field. Thus, for S waves in aporous and homogeneous environment, a magnetic field is produced that moves as a partof the material reaction. Effluent but electromagnetic waves are not producedindependently. Supposedly, a magnetic detector (which is insusceptible to mechanicalvibration) can serve as a selector geophone on shear wave action and measure themagnetic field N-S wave when the magnet passes the gauge. The effect of a direct current(DC) electric field on the propagation of seismic waves is modeled in this study by meansof the pseudospectral time domain (PSTD) method, based on a set of governing equationsfor poroelastic media. This study focuses on the more general concept of thesiesmoelecric coupling effect and the application of poroelastodynamics and Maxwellsequation to seismic and electromagnetic waves. In this project, the magnitude effects ofseismoelectric coupling are found to be characterized by charge density, electricconductivity, dielectric permittivity, fluid viscosity and zeta potential. The simulatedporoelastic wave propagation and electric field vary with an existing background. Aphysical experiment was carried out in an oilfield using a DC electric field and the resultswere compared with those of the simluation. Estimations for solutions of differentialequations are based on the function (or a number of separate estimates for this function)defined by a certain relationship between its various derivatives on a given place or timerange along the boundary conditions of this area. Overall, this is a serious problem andonly a formula for this solution was analyzed. An alternate method with finite derivativeswas used to replace the differential equation. The results show that the seismoelectriccoupling in a wide range of the seismic frequency bands generated through a DC electricfield can significantly affect the propagation of elastic energy.

Many of the problems faced in engineering and science can be effectively modeledmathematically. However, in constructing these models many assumptions have to bemade which are often not true in the real world. For some applications, the sets that willhave to be defined are easily identifiable. For other applications, they will have to bedetermined by knowledge acquired from an expert or group of experts. Once the names of fuzzy sets have been established, one must consider their associated membershipfunctions. Development of this idea has led to many successful implementations of fuzzy logic systems, also called Fuzzy Inference Systems (FIS). A Fuzzy Inference System is asystem that uses fuzzy sets to make decisions or draw conclusions.The approach adopted for acquiring the shape of any particular membership functionis often depend on the application. In some applications, membership functions must beselected directly by a `statistical' approach or by an automatic generation of shapes. Thedetermination of membership functions can be categorized as being either manual orautomatic. The manual approaches rely only on the experience of an expert. All of themanual' approaches suffer from the fact that they rely on subjective interpretation ofwords.A new indirect fracture detection technique called Fuzzy Logic Integrated System(FLIS) from well logs is presented in this paper. The FLIS can be widely used for fracturedetection with high precision in comparison with image logs in zones of interest. Thismethod is very suitable for multiple well logs, where changes in the log- shapes areaffected by the fractures. Therefore, the above method should be used correctly. Fuzzymembership of the log data serves also as an indicator for the classification of results andprovides valuable information concerning the reliability of the fracture zones.The procedures of executing the fuzzy logic are as follows: First, based on theRockLog program (Ghassem Alaskari, 2005), the well log data on each zone of interest areanalyzed and plotted in the log format. Second, anisotropic parameters necessary for theevaluation of highly fractured zones from image logs are determined and compared withthe full data set. Third, using FLIS algorithm written for this purpose, fractures can beidentified in each zone of interest. Fourth, the comparison between the results given in thethird step with the core samples at the same intervals (the fracture density and fracture types) in each zone can be identified.The above procedure has been used successfully for determining fractured reservoirzones in the South-Pars field from an open-hole well log data. A comparison betweencore samples and image logs was done for the same intervals detected by this technique.As described earlier, a fuzzy set is fully defined by its membership function. How best todetermine the membership function is the main question that needs to be addressed. The degree of applicability of this technique is checked by image logs and core samples forthe same region, where a full well data was available.

In this paper, the flow and turbulence structure in the boundary layer of an urbanizedregion with complex topography (Tehran) was studied using data from a meteorologicalstation, Sodar (PA1 Model) for heights above 50m, on days 13 through 24 of August,2002, and an ultrasonic anemometer, located in Tehran University Geophysics Institute,in August 2005. Days for observation were selected such that they were without anyactive synoptic system in the region, the skies were clear, wind speed at 10 m did notexceeded 5 meters per second, and the relative humidity was low. The data used for thevertical profiles are 12-day averages for 4 local hours, namely: 09:30, 15:30, 21:30 and03:30.The study of turbulence using the gradient Richardson number shows that, during theday, the boundary layer is generally turbulent while, at nights, in addition to the reductionof turbulence intensity, the depth of the turbulent region of the boundary layer alsodecreases. Tests done by Monti et al. (2002) have shown that the Rig is not sensitive to thetime of averaging in range30 s  Tav  900 s. The Sodar data are also 15 minutes averagedof the measurements. Additionally, Sodar data are spatial average of 25-meter, and sothis averaging may filter out some turbulent layers in the profiles. This averaging maycause large Richardson numbers.Graphs of u s, v s and w s show that nearly continuous turbulence occurs during the night,but the values are weaker with respect to daytime turbulence. The TKE diagram showsthat this quantity has a daytime maximum value and a nighttime minimum, indicating theeffect of stability on turbulence generation. Turbulent intensity componentsusu andusvare almost the same, and are also four times that ofusw during the night and nearly threetimes ofusw during the day. This shows that turbulent kinetic energy production is acombination of buoyancy and shear effects during the day and is mostly due to the effectsof shear during the night.A study of the behavior of turbulent quantity (sw) / z 3, in association with buoyancyand mechanical turbulent kinetic energy source production terms, explains the thermaland roughness effects on the characteristics of TKE. Values of diurnal z (sw) / 3 arereduced relative to height at different hours due to the increased production of mechanicaland buoyancy turbulence in the surface layer. Values of these quantities are low even inthe presence of wind shear that can be due to radiation-induced effects on these quantitiesduring the day, especially in relatively low wind conditions.Wavelet analysis of wind speed using ultrasonic anemometer data with one-minuteaveraging in the surface layer shows mostly wavy and continuous structures with periodsof 6 to 90 minutes that are related to turbulent fluctuations and both regular and irregularinternal waves.It seems that the topography-induced flows (down slope, upslope and drainage flows)and urban effects (flows from thermal islands and their interactions with artificialtopography such as high buildings, roads and vegetation) cause important changes in thecirculation of the wind flows of the region when synoptic systems are absent. Local flowsin the region with the effects of complex terrains are generated by pressure gradients andthermal forcing. Urban flows span a wide range of space and time scales. These factorschange turbulence and vertical wind profiles.The time series of various quantities show approximately diurnal variations. Verticalprofiles of turbulent quantities show that the flow is stratified in the lower section of theboundary layer (under 500m). The depth over which the katabatic flow occurs reachesabout 200 meters. This stratified lower section of the boundary layer possibly caused bythe effects of complex topography, the urbanization of the region and their circulationinteractions (especially during the night, when they reinforce each other). The height ofthis layer doubles during daytime. The layering of the wind profile may be due to airintrusion from various slopes originating from different sources according to Monti et al.(2002), or to the structure of generated internal waves.

Potential field data (gravity and magnetic data) are usually analyzed by means of lineartransformations, spectral methods, inversion techniques and analytic signal methods.Nowadays, there are diverse methods of modeling the gravity data, but each has some limitations. One of the limitations of these methods is the assumption of a simple shapefor buried structures, whereas the actual shape could be entirely different. The presentstudy uses two-dimensional 4-sided polygons (prisms) to resolve these limitations,because with these it is possible to make any shape for an unknown underground structure by arranging these prisms.Within the context of this study, the 2D inversion method proposed by Last and Kubik(1983) is reused. For this purpose, a Matlab-based 2D inversion code was developed. Thiscode uses an iterative least squares procedure, which allows the weights to depend on the densities of the previous iteration. Therefore, the solution minimizes both the area of the underground structures or deposits and the weighted sum of squared residuals.According to Last and Kubik, the iterative procedure stops when a minimum area ofthe density distribution is reached. The stopping criteria in inversion algorithms areusually based on the fit between the observed data and theoretical data produced by theproposed model. Typically, a misfit function estimator is used.In the inversion of potential field data, the number of observations is often less thanthe number of unknowns (underdetermined problem). To overcome this problem, thisstudy uses a new method proposed by Ekinci, wherein the density variation is used as anew stopping criterion to find the required number of iterations for convergence themodel. The focusing inversion method proposed by Last and Kubik was modified in orderto produce a compact final model. For this model, the difference between the blockdensities at the last successive iteration is minimal. This method minimizes the volume ofdeposit, which is equivalent to maximizing its compactness. Here, the method for noisefree and noise-corruption synthetic data was used and after obtaining satisfactory results it was applied to real data.The Dehloran Bitumen map in Iran is chosen as a real data application. The area underconsideration is located in the Zagros tectonic zone, in western Iran, where a search forBitumen is under way. Layers of medium bedded limestone with intermediate marllimestoneare the dominant formations in the area, and the hydrocarbon zone is one of themost important characteristics of the area.By using the method for noise-free and noise-corruption synthetic data, the presentstudy produced a program for the Dehloran Bitumen map. Anomaly modeling was usedbecause the anomaly value of the cross section, which is taken from the gravity anomalymap of Dehloran Bitumen, is very close to those obtained from this method.The final result of these methods shows that the deposit starts from the depth of 10meters to about 35 meters. This modeling was a satisfactory representation of the resultsof actual drilling in the region. The results of the drillings show that the lowest depth ofthe deposit varies from 7 to 10 meters. This 2D modeling of gravity data with the compact inversion method and density variation can easily be applied for gravity, microgravity and magnetic data.

The generation of inertia–gravity waves (IGWs) in the idealized simulation of vorticalflows is investigated using the isentropic two-layer, primitive-equation model on thesphere. The contour-advective semi-Lagrangian (CASL) algorithm is used to solve theprimitive equations in potential vorticity, velocity divergence, and accelerationdivergence representation. The CASL algorithm consists of both Lagrangian and Eulerianparts. The Lagrangian part addresses the potential vorticity equation, which is solved bycontour advection. The Eulerian part addresses the remainder of the model including theprognostic and diagnostic equations for the grid-based variables of velocity divergenceacceleration divergence and the depth. The Eulerian part is solved by the spectraltransform in longitude, the forth-order compact differencing in latitude, and a three-timelevelsemi-implicit scheme in time. The power of CASL rests in its ability to represent thefine-scale structures in potential vorticity. Therefore, using CASL, it is possible todetermine more precisely the generation and propagation of the IGWs generated byvortical flows.The initial state of the numerical experiments is comprised of a balanced, zonal jetwith a very small perturbation added to trigger instability. With regard to the balancedinitial conditions used, the IGWs are generated mainly through spontaneous-adjustmentemission. To determine the sensitivity of the IGWs generated to the degree ofbaroclinicty, four experiments were carried out in which the upper-layer potentialtemperature was set to 310, 315, 320, and 325K from the first to the fourth experiment,respectively. The lower-layer potential temperature was set to 280K in all of theexperiments. As a result of increasing the upper-layer potential temperature, the staticstability increased and thus the baroclinicity decreased from the first to the fourthexperiment. To identify the IGWs accurately, the Bolin–Charney potential vorticityinversion was used to decompose the flow into a balanced part representing vortical flowand an unbalanced part representing free IGWs.

Characterization of the detailed structure of the crust and upper mantel is an importantcontinuing goal of geophysical studies. There are a variety of geophysical methods(seismic refraction, seismic vertical reflection and seismic tomography) to investigatesubsurfaces. The teleseismic P Receiver Function (RF) method has become a populartechnique to constrain crustal and upper mantle velocity under a seismic station.Teleseismic body waveforms recorded at a 3-component (Z, N-S, E-W) seismic stationcontain a wealth of information on the earthquake source, the earth structure in thevicinity of both the source and receiver, and mantle propagation effects. The resulting RFis obtained by removing the effects of the source and mantle path. The basic aspect of thismethod is that a small percentage of the incident P wave energy from teleseismic eventswith significant and relatively sharp velocity discontinuities in the crust and upper mantlewill be converted to S wave (Ps), and arrive at the station within the P wave coda directlyafter the direct P wave. To obtain P-RF, the following steps are generally used: to utilizedata recorded with different types of seismometers, the instrument responses have to bedeconvolved from the original records. ZNE components are then rotated into the localLQT ray-based coordinate system (using the theoretical back azimuth and incidenceangle).To eliminate the influence of the source and ray path, an equalization procedure isapplied by deconvolving the Q component seismogram with the P signal on the Lcomponent. The resulting Q component data are named P-RF. An advantages of the RFmethod is that, because the P-to-S conversion point is close to the station (usually within10 km laterally), the estimation is less affected by lateral velocity variations. Theestimation provides a good point measurement at the station because of the steepincidence angle of the teleseismic P wave. Since the direct P arrival is used as a referencetime, it can be shown that the result is not sensitive to crustal P velocity. We compute P receiver functions to investigate the crustal thickness and Vp/Vs ratiobeneath the East of Iran (Birjand) and map out the lateral variation of Moho depth underthis region. We selected data from teleseismic events (Mb ≥ 5.5, 30˚<Δ<95˚), recordedfrom 2005 to 2009 at 4 three-component short period stations from Birjand SeismicTelemetry Network. These stations are equipped with SS-1 seismometers with a naturalfrequency of 1 HZ. The data is recorded at 50-samples-per-second. First of all, iscalculated 247 P-RFs for TEG, KOO and DAH stations and then estimated the Mohodepth solely from the delay time of the Moho P-to-S conversion phases. Then, we used an H-Vp/Vs stacking algorithm to estimate crustal thickness and the Vp/Vs ratio under each station. The best value for the H and Vp/Vs ratio are found when the three phases (Ps and crustal multiples) are stacked coherently. The results obtained from the P receiverfunctions indicate clear conversions at the Moho boundary. A notable feature, which can be observed underneath all stations, is the presence of a significant sedimentary layer at about 0.7-1s delay time. The middle crustal layer at about 1.9-3.3s delay time can also be seen beneath all stations. The most coherent conversion, however, is the conversion at the Moho boundary arriving between 4.7-5.4s delay time. As a result of measurements using the Zhu and Kanamori (2000) method, the average Moho depth is found to be approximately 41 km and to vary from 38.5 to 44 km. The crust is relatively thin beneath the DAH station, whereas the thickest crust was observed beneath the KOO station, located southwest of the study area. The crust of Eastern Iran has an average Vp/Vs ratio of 1.76, with a higher ratio of 1.84 in the TEG station and lower ratio of 1.76 in the KOO station.

Anisotropy has an important role in exploration and reservoir characterization. Inpractice, the determination of seismic anisotropy is not easy, but it has importantconsequences in enhancement of seismic data recording and processing. Anisotropyinteracts with reflection seismology, acquisition, processing and interpretation. Ignoringanisotropy can lead to poor seismic imaging, misleading the seismic reflector responses,inaccurate location of well-ties, and incorrect interpretation of seismic arrival times andamplitudes for the determination of lithology and fluid content.Shear wave velocity anisotropy is commonly referred to as shear wave splitting,because a shear wave traveling in an heteregeneous medium splits into two shear waves.At a given receiver, shear waves are characterized by their orthogonal polarizationdirections (fast and slow) and a delay between their arrival times.The most common anisotropic models have been related to the framework oftransverse isotropy or a hexagonal isotropy system. When the symmetry axis is alignedwith the vertical axis, the model is called vertical transverse isotropy or VTI. For a VTImedium, there are five stiffness coefficients and three independent phase velocities.Thomsen (1986) replaced these stiffness coefficients with two vertical velocities (Vp0 andVS0) and three dimensionless anisotropy parameters (namely, ε, γ and δ). Anisotropyparameters can be determined in several ways, including velocity measurements on coresamples in a laboratory or from field data in a VSP experiment. A common form ofanisotropy observed in many geological area (thinly horizontal layers or fractures). Thisinvolves the reference axis of symmetry being normal to the bedding surfaces. Thomsen(1986) introduced three anisotropic parameters (ε, γ and δ) to describe weak anisotropy,which is believed to be the simple model of anisotropy. Thomsen parameters can becomputed with the stiffness tensor considered in anisotropic media. Alkhalifah andTsvankin (1995) showed that, for P-wave Moveout, there exists a range of kinematicallyequivalent models which are governed by the stacking velocity and introduced by theparameter η.The Dipole Shear Sonic Imager (DSI) is an example of devices that are used to obtainand analyze sonic measurements of formations surrounding a borehole. The DSI Imagercan measure the components of shear slownesses in many directions in a planeperpendicular to a borehole axis. The DSI tool is a full waveform acoustic tool thatdelivers measurements of sonic waves in a wide variety of formations. In theconventional DSI logging tool, one can present compressional slowness, Δtc, shearslownesses, Δts, and Stoneley slowness, Δtst, each as a function of depth. The DSI tool canestimate the orientation and magnitude of stress from velocity dispersion. By invertingthe dispersion curves from DSI logs, one can estimate the horizontal stresses. One type ofthese special dipole modes enables the recording of both inline and crossline(perpendicular) waveforms. These modes, both called cross receivers (BCR) which areused for anisotropy evaluation.In this paper, one of the anisotropy parameters of Thomsen (γ) was determined by theuse of S-wave velocities and their relationship with the DSI tool used in the Kangan andDalan gas zones of the South Pars field. Subsequently, the γ parameter was comparedwith the Gamma Ray log in depth. The results show anisotropy behavior in shaly zones ofKangan and Dalan Formations. It is found that the average of the γ parameter for theKangan and Dalan Formations are 0.015 and 0.02, respectively. Also, this parameter wascompared with slowness based on anisotropy. A good correlation was observed betweenanisotropy parameter γ and the slowness based on anisotropy (slowness vector).