IRANIAN JOURNAL OF GEOPHYSICS
Year:2011 | Volume:5 | Issue:1|Papers Count:11

Prediction of spatial distribution of porosity in a reservoir is an essential issue for estimating reserves and planning production operations. In most cases, however, lateral variations of porosity cannot be delineated from measurements made at sparsely located wells. The integration of 3D seismic data with petrophysical measurements can significantly improve the spatial description of porosity. In the last two decades, several methods have been developed for the estimation of reservoir porosity. A number of inversion methods are available in the industry to convert seismic amplitude into acoustic impedance. Acoustic impedance is indirectly related to porosity. Alternate integrative approaches for estimating porosity include geo-statistical methods, such as kriging and co-kriging using well and seismic data. One of the most accurate methods for estimating reservoir parameters is the application of seismic attributes by a nonlinear estimator, such as neural network or neuro-fuzzy model. Both neural networks and neuro-fuzzy models can be good estimators but the latter has the benefit of being interpretable. In this study, a neuro-fuzzy model called NEFPROX was used to estimate porosity in a gas reservoir located in the Gorgan Basin.NEFPROX is a Mamdani-type neuro-fuzzy model, so it has an advantage of being interpretable that makes it distinct from other type of neuro-fuzzy models. The timeconsuming characteristic of that method is irrelevant in this case because the prediction of porosity 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-Sand Group I, belongs to quaternary period. Below Clay-Sand Group I is a tertiary formation called Clay-Sand GroupII. A formation of Brown Beds is also a tertiary formation that lies below Clay-Sand GroupII. All of these formations consist mainly of shale and sand.The discovery of gas in the Brown Beds Formation has persuaded explorationists to increase their activities in the Gorgan Basin. The purpose of this study is to recognize shale and sand bodies in the Brown Beds formation that consists of alternative sand and shale layers with variable thickness.First, a list of 20 seismic attributes was prepared to extract from raw seismic data in the 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. These attributes, in the order of priority, are instantaneous frequency, amplitude weighted frequency, apparent polarity, and second derivative instantaneous amplitude. Then these four selected attributes were introduced into the neuro-fuzzy model as input to predict porosity as an output of the model.The neuro-fuzzy model was trained with the data of well GO3. Based on hydrocarbon core samples obtained from the Brown Beds formation and the potential of this formation as a probable reservoir, the data corresponding to this formation were selected as a training 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 sandy and shaly-sand channels in the Brown Beds formation. Lateral variations of these channels can clearly be recognized in these sections. The core samples available in well GO3 (containing hydrocarbon) confirm the existence of the two inferred channels. This clear image of channels is simply unidentifiable from raw seismic data. Hence, NEFPROX can be very helpful in supplying valuable information about extent, shape and lithological variation of a reservoir. Finally, compared comparison was made between the performance of the neuro-fuzzy model and regular neural networks in estimation of porosity. The comparison indicates that the accuracy of the NEFPROX estimation is equal to that of MLP and is greater than that of RBF.

Numerical weather prediction (NWP) models without initialization techniques may be result in unreal and inaccurate data. Many initialization methods, such as linear and nonlinear normal mode initialization, have been developed and applied in the field of NWP by atmospheric researchers and modelers. In application, such techniques are verycomplex and expensive. One of the most efficient and simple techniques which can be used in operational forecasting is digital filter initialization. Digital filter initialization methods are applied to eliminate non-physical and high frequency waves from NWP models. These unwanted waves can affect the results of the models and cause its results to depart 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 the other filters. This superiority can be verified using a digital filter initialization technique associated with the Dolf filter in the weather research and forecasting (WRF) model and investigating its results. Subsequently, the digital filter initialization methods provided in the WRF model are tested for the region of Iran. Three different digital filter initialization techniques, namely the digital filter launch, diabatic digital filter and twice digital filter initialization, with nine aforementioned filters were prepared in the WRF model. The WRF model was set with a 45-kilometer grid size for the region at 12-50oN and 12-87oS.The WRF model was run over this region with and without a digital filter initialization technique. In general, the initialization of the NWP models influences the first hours of prediction of the meteorological parameters. In this study, two parameters, including surface pressure and rainfall, were considered as indicators of the effects of digital filter initialization methods on the results of the WRF model. Therefore, the obtained results are investigated and compared for surface pressure fluctuation and rainfall.All results indicate that applying the digital filter initialization effectively liminates nonphysical waves from surface pressure fields, especially in the first hours of prediction.This was determined by studying three parameters, including surface pressure fluctuation in some points, derivative of surface pressure fluctuation in some points, and integrated derivative of surface pressure. It was found that the twice digital filter initialization associated 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 were compared with the results of WRF model without digital filter initialization and observed station data. This comparison showed that the twice digital filter initialization associated with the Dolph filter has its maximum effect during first three hours and in the second three hours has a minimum effect among other techniques. This means that unwanted fluctuations are eliminated properly during the first three hours. Also, a comparison of rainfall prediction results with observed station data indicates that the diabatic digital filter initialization associated with the Dolph filter has the minimum root mean square error. Among digital filter initialization techniques studied, the digital filter launch has sudden effects on the amount of rainfall predicted during the first three hours of prediction time, so this can induce significant errors in results of the model.

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Seismoelectric modeling is a prospecting method, based on seismic electromagnetism in which seismic sources are used to generate this phenomenon. When seismic waves are released within a fluid-saturated sedimentary material, a small amount of fluid-solid relative motion is induced. The seismic force causes this effect through a combination of relative gradient acceleration fluid and the pressure of seed waves. Most of the surface grain, 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 of mobile ions with opposite charges. The scattered ions in this layer are free to move through the fluid so that seismic waves create an electrical current flow. This induced seismic current flow acts as a current source in Maxwell's equations, which is the base of electromagnetic wave coupling with a seismic wave. The peaks and troughs that accumulate 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 homogeneous substance, the current flow is equal to convection so that the overall flow is zero; no magnetic or electromagnetic fields are created independently., Hence, the electric field that exists within the wave moves as a part of the reaction without spreading outside the wave. Therefore, a simple pair of electrodes can act as a geophone to measure the electric field inside the P-wave when it passes throughthem. The S shear wave does not separate charges 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 flow creates magnetic fields that, in turn, produce a small electric field. Thus, for S waves in a porous and homogeneous environment, a magnetic field is produced that moves as a part of the material reaction. Effluent but electromagnetic waves are not produced independently. Supposedly, a magnetic detector (which is insusceptible to mechanical vibration) can serve as a selector geophone on shear wave action and measure the magnetic 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 means of the pseudospectral time domain (PSTD) method, based on a set of governing equations for poroelastic media. This study focuses on the more general concept of the siesmoelecric coupling effect and the application of poroelastodynamics and Maxwell' s equation to seismic and electromagnetic waves. In this project, the magnitude effects of seismoelectric coupling are found to be characterized by charge density, electric conductivity, dielectric permittivity, fluid viscosity and zeta potential. The simulated poroelastic wave propagation and electric field vary with an existing background. A physical experiment was carried out in an oilfield using a DC electric field and the results were compared with those of the simluation. Estimations for solutions of differential equations 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 time range along the boundary conditions of this area. Overall, this is a serious problem and only a formula for this solution was analyzed. An alternate method with finite derivatives was used to replace the differential equation. The results show that the seismoelectric coupling in a wide range of the seismic frequency bands generated through a DC electric field can significantly affect the propagation of elastic energy.

Many of the problems faced in engineering and science can be effectively modeled mathematically. However, in constructing these models many assumptions have to be made which are often not true in the real world. For some applications, the sets that will have to be defined are easily identifiable. For other applications, they will have to be determined by knowledge acquired from an expert or group of experts. Once the names of fuzzy sets have been established, one must consider their associated membership functions. 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 a system that uses fuzzy sets to make decisions or draw conclusions.The approach adopted for acquiring the shape of any particular membership function is often depend on the application. In some applications, membership functions must be selected directly by a `statistical' approach or by an automatic generation of shapes. The determination of membership functions can be categorized as being either manual or automatic. The manual approaches rely only on the experience of an expert. All of the ‘manual’ approaches suffer from the fact that they rely on subjective interpretation of words.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 fracture detection with high precision in comparison with image logs in zones of interest. This method is very suitable for multiple well logs, where changes in the log-shapes are affected by the fractures. Therefore, the above method should be used correctly. Fuzzy membership of the log data serves also as an indicator for the classification of results and provides valuable information concerning the reliability of the fracture zones.The procedures of executing the fuzzy logic are as follows: First, based on the RockLog program (Ghassem Alaskari, 2005), the well log data on each zone of interest are analyzed and plotted in the log format. Second, anisotropic parameters necessary for the evaluation of highly fractured zones from image logs are determined and compared with the full data set. Third, using FLIS algorithm written for this purpose, fractures can be identified in each zone of interest. Fourth, the comparison between the results given in the third 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 reservoir zones in the South-Pars field from an open-hole well log data. A comparison between core 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 to determine 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 for the same region, where a full well data was available.

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Potential field data (gravity and magnetic data) are usually analyzed by means of linear transformations, 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 shape for buried structures, whereas the actual shape could be entirely different. The present study 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. This code 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 of the density distribution is reached. The stopping criteria in inversion algorithms are usually based on the fit between the observed data and theoretical data produced by the proposed model. Typically, a misfit function estimator is used.In the inversion of potential field data, the number of observations is often less than the number of unknowns (underdetermined problem). To overcome this problem, this study uses a new method proposed by Ekinci, wherein the density variation is used as a new stopping criterion to find the required number of iterations for convergence the model. The focusing inversion method proposed by Last and Kubik was modified in order to produce a compact final model. For this model, the difference between the block densities at the last successive iteration is minimal. This method minimizes the volume of deposit, 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 under consideration is located in the Zagros tectonic zone, in western Iran, where a search for Bitumen is under way. Layers of medium bedded limestone with intermediate marllimestone are the dominant formations in the area, and the hydrocarbon zone is one of the most important characteristics of the area.By using the method for noise-free and noise-corruption synthetic data, the present study produced a program for the Dehloran Bitumen map. Anomaly modeling was used because the anomaly value of the cross section, which is taken from the gravity anomaly map 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 10 meters to about 35 meters. This modeling was a satisfactory representation of the results of actual drilling in the region. The results of the drillings show that the lowest depth of the 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 vortical flows is investigated using the isentropic two-layer, primitive-equation model on the sphere. The contour-advective semi-Lagrangian (CASL) algorithm is used to solve the primitive equations in potential vorticity, velocity divergence, and acceleration divergence representation. The CASL algorithm consists of both Lagrangian and Eulerian parts. The Lagrangian part addresses the potential vorticity equation, which is solved by contour advection. The Eulerian part addresses the remainder of the model including the prognostic and diagnostic equations for the grid-based variables of velocity divergence, acceleration divergence and the depth. The Eulerian part is solved by the spectral transform in longitude, the forth-order compact differencing in latitude, and a three-timelevel semi-implicit scheme in time. The power of CASL rests in its ability to represent the fine-scale structures in potential vorticity. Therefore, using CASL, it is possible to determine more precisely the generation and propagation of the IGWs generated by vortical flows.The initial state of the numerical experiments is comprised of a balanced, zonal jet with a very small perturbation added to trigger instability. With regard to the balanced initial conditions used, the IGWs are generated mainly through spontaneous-adjustment emission. To determine the sensitivity of the IGWs generated to the degree of baroclinicty, four experiments were carried out in which the upper-layer potential temperature 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 the experiments. As a result of increasing the upper-layer potential temperature, the static stability increased and thus the baroclinicity decreased from the first to the fourth experiment. To identify the IGWs accurately, the Bolin–Charney potential vorticity inversion was used to decompose the flow into a balanced part representing vortical flow and an unbalanced part representing free IGWs.The analysis of the average growth rate of the baroclinic waves shows that the first experiment has the maximum average growth rate among the four experiments. Increase in static stability from the first to the fourth experiment led to a decrease in the average growth rate, but the time of occurrence of the peak in growth rate increased.The investigation of the unbalanced velocity divergence shows the generation of two wave packets of IGWs during the evolution of the vortical flow in the first experiment; one wave packet on the downstream side of the trough, similar to the mesoscale waves described by Zhang (2004), and the other wave packet on the upstream side of the trough, similar to the wave packet described by Plougonven and Snyder (2007) in idealized simulations of baroclinic life cycle dominated by cyclonic behavior. The intrinsic frequency of both wave packets is close to that found by Wang and Zhang (2007) for the low static-stability experiment. The upstream wave packet appears in the second experiment, but failed to reach noticeable amplitude in the third and fourth experiments.The effect of increasing static stability is even more dramatic on the downstream wave packet, which is identifiable only in the first experiment.Unbalanced linearized available energy (LAE) is used to quantify the strength of the IGWs. The results show that, consistent with the formation of two wave packets, only the first experiment that reached the maximum value of the unbalanced LAE. Due to increased static stability, there is a considerable reduction in peak value of unbalanced LAE. For example, the ratio of the peak of unbalanced LAE in the first experiment is 86 times as great as that of the fourth experiment. Another way to observe the substantial differences in the strength of the IGWs is to measure the maximum norm of the unbalanced velocity divergence. Before and after reaching the peak of the unbalanced LAE, the maximum norms of the unbalanced velocity divergence in the first experiment were between 15 and 21 times as great as the corresponding values in the fourth experiment.

Characterization of the detailed structure of the crust and upper mantel is an important continuing goal of geophysical studies. There are a variety of geophysical methods (seismic refraction, seismic vertical reflection and seismic tomography) to investigate subsurfaces. The teleseismic P Receiver Function (RF) method has become a popular technique 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 station contain a wealth of information on the earthquake source, the earth structure in the vicinity of both the source and receiver, and mantle propagation effects. The resulting RF is obtained by removing the effects of the source and mantle path. The basic aspect of this method is that a small percentage of the incident P wave energy from teleseismic events with significant and relatively sharp velocity discontinuities in the crust and upper mantle will be converted to S wave (Ps), and arrive at the station within the P wave coda directly after the direct P wave. To obtain P-RF, the following steps are generally used: to utilize data recorded with different types of seismometers, the instrument responses have to be deconvolved from the original records. ZNE components are then rotated into the local LQT ray-based coordinate system (using the theoretical back azimuth and incidence angle).To eliminate the influence of the source and ray path, an equalization procedure is applied by deconvolving the Q component seismogram with the P signal on the L component. The resulting Q component data are named P-RF. An advantages of the RF method is that, because the P-to-S conversion point is close to the station (usually within 10 km laterally), the estimation is less affected by lateral velocity variations. The estimation provides a good point measurement at the station because of the steep incidence angle of the teleseismic P wave. Since the direct P arrival is used as a reference time, 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 ratio beneath the East of Iran (Birjand) and map out the lateral variation of Moho depth under this region. We selected data from teleseismic events (Mb³5.5, 30o<D<95o), recorded from 2005 to 2009 at 4 three-component short period stations from Birjand Seismic Telemetry Network. These stations are equipped with SS-1 seismometers with a natural frequency of 1 HZ. The data is recorded at 50-samples-per-second. First of all, is calculated 247 P-RFs for TEG, KOO and DAH stations and then estimated the Moho depth 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 receiver functions 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. In practice, the determination of seismic anisotropy is not easy, but it has importantconsequences in enhancement of seismic data recording and processing. Anisotropy interacts with reflection seismology, acquisition, processing and interpretation. Ignoring anisotropy can lead to poor seismic imaging, misleading the seismic reflector responses, inaccurate location of well-ties, and incorrect interpretation of seismic arrival times and amplitudes 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 polarization directions (fast and slow) and a delay between their arrival times.The most common anisotropic models have been related to the framework of transverse isotropy or a hexagonal isotropy system. When the symmetry axis is aligned with the vertical axis, the model is called vertical transverse isotropy or VTI. For a VTI medium, there are five stiffness coefficients and three independent phase velocities.Thomsen (1986) replaced these stiffness coefficients with two vertical velocities (Vp0 and VS0) and three dimensionless anisotropy parameters (namely, e, g and d). Anisotropy parameters can be determined in several ways, including velocity measurements on core samples in a laboratory or from field data in a VSP experiment. A common form of anisotropy observed in many geological area (thinly horizontal layers or fractures). This involves the reference axis of symmetry being normal to the bedding surfaces. Thomsen (1986) introduced three anisotropic parameters ((e,g  and d) to describe weak anisotropy, which is believed to be the simple model of anisotropy. Thomsen parameters can be computed with the stiffness tensor considered in anisotropic media. Alkhalifah and Tsvankin (1995) showed that, for P-wave Moveout, there exists a range of kinematically equivalent models which are governed by the stacking velocity and introduced by the parameter h.The Dipole Shear Sonic Imager (DSI) is an example of devices that are used to obtain and analyze sonic measurements of formations surrounding a borehole. The DSI Imager can measure the components of shear slownesses in many directions in a plane perpendicular to a borehole axis. The DSI tool is a full waveform acoustic tool that delivers measurements of sonic waves in a wide variety of formations. In the conventional DSI logging tool, one can present compressional slowness, Dtc, shear slownesses, Dts, and Stoneley slowness, Dtst, each as a function of depth. The DSI tool can estimate the orientation and magnitude of stress from velocity dispersion. By inverting the dispersion curves from DSI logs, one can estimate the horizontal stresses. One type of these special dipole modes enables the recording of both inline and crossline (perpendicular) waveforms. These modes, both called cross receivers (BCR) which are used for anisotropy evaluation.In this paper, one of the anisotropy parameters of Thomsen (g) was determined by the use of S-wave velocities and their relationship with the DSI tool used in the Kangan and Dalan gas zones of the South Pars field. Subsequently, the g parameter was compared with the Gamma Ray log in depth. The results show anisotropy behavior in shaly zones of Kangan and Dalan Formations. It is found that the average of the g parameter for the Kangan and Dalan Formations are 0.015 and 0.02, respectively. Also, this parameter was compared with slowness based on anisotropy. A good correlation was observed between anisotropy parameter g and the slowness based on anisotropy (slowness vector).