Subsurface channels are stratigraphic features in seismic data that can act as reservoirs or conduits for hydrocarbons. However, detecting and characterizing these channels is challenging due to the limitations of seismic resolution and the complexity of the subsurface geology. seismic inversion is a technique that can enhance the seismic data by transforming the seismic traces into quantitative estimates such as acoustic impedance (AI), which is a key reservoir rock property. AI inversion can help to identify and delineate the subsurface channels by providing more contrast and detail of the channel geometry, fill, and surrounding sediments. seismic inversion is often challenged by the non-uniqueness, ambiguity and uncertainty of the inversion results due to noise and band-limited data. This paper uses a fuzzy model-based seismic inversion method that integrates prior information and fuzzy clustering constraints to produce more realistic and reliable AI models. This method assigns data points to multiple clusters with varying degrees of membership, which can capture the overlapping of AI values of different geological formations. The method is applied to the 3D Poseidon seismic data from the Browse Basin, offshore Western Australia, and the results are compared with those of conventional model-based inversion. Since there is no well-data in an interest channel zone, a qualitative evaluation with seismic attributes is performed. The subsurface structures are further interpreted by various seismic attributes. The comparison shows that the fuzzy model-based inversion method can improve the resolution, contrast and stability of the AI models and reveal more detail of the subsurface geology.

We present a fast implementation of a 1D elastic full-waveform inversion for reconstructing the elastic structure of the subsurface. The FWI is an inversion algorithm that directly models the full seismic wavefield by solving a semi-analytical form of the elastic wave equation for a 1D layered earth model known as the reflectivity method. The input seismic data are pre-conditioned angle-gathers. The inversion is done using a stochastic algorithm known as PSOES, a fast hybrid stochastic optimization algorithm. Our work primarily contributes to accelerating the computation times required for the inversion, with the goal of developing a strategy that enables code implementation at production scales. Additionally, we are working on creating a framework for conducting joint inversions with potential field data. The computational cost of the FWI is directly proportional to the number of unknowns in the inversion problem, which correlates with the vertical resolution of model (i.e., the layer thicknesses in the 1D Earth model) and the maximum depth of the study. However, the relationship is not linear because increasing the number of unknowns affects the run time of both the forward and inverse problems. On the other hand, the quality of the solution highly depends on the vertical resolution since modeling the higher frequencies in the data requires a relatively small vertical thickness. An optimum implementation of the FWI could result in calculating 1D elastic profiles of the subsurface which could be used for constraining the inversion of potential field data over sedimentary basins.

Introduction: There are several methods of seismic migration and the main objective of those is to place the reflectors in their true positions. One way for seismic migration is the algorithms that directly apply imaging conditions; on the other hand, the inversion-based imaging method implemented through different strategies to obtain a better depth model that fits the observed data. One of these inversion methods named least square migration solves the inverse problem through direct migration and demigration. The least squares migration has the main advantage that it can gradually reduce errors caused by initial migration. In this paper, particularly the reverse time migration (RTM) is used as an operator of migration and demigration. Therefore, two numerical schemes are developed to implement least-squares migration with the reverse time migration method. Methodology and Approaches: The Helmholtz equation is used to derive the forward modeling operators named reverse time migration (RTM) operator with the Born approximation that is donated as linear inversion. Thus, the linear least square reverse time migration (LSRTM) is the inversion procedure to obtain the final image. LSRTM uses the RTM results as the initial reflectivity model and Born modeling to simulate the seismic data. The reflectivity model is updated by calculating the differences between observed and calculated data through the conventional an adaptive gradient. After multiple iterations, the differences are minimized and this is taken to suggest that the fi nal refl ectivity model refl ects the real subsurface interface. Results and Conclusions: The results indicate that the LSRTM through an adaptive gradient procedure can successfully produce the subsurface migrated image free of artifacts including the steep dip structures during a reasonable computational cost.

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.

Today, quantitative evaluation and interpretation of seismic data are of particular importance to better understand the reservoir characteristics of oil and gas fields. One of the important stages of evaluating a reservoir is describing the reservoir facies and distinguishing the different lithologies of the study area. Therefore, amplitude versus offset (AVO) analysis is one of the most important advanced methods for the analysis of geological facies, lithology, and fluid detection. In this study, simultaneous seismic inversion has been used in one of the oil fields in southern Iran on the Asmari formation. This study aims to separate the seismic facies using seismic attributes obtained from simultaneous seismic inversion, including acoustic impedance (IP), shear impedance (IS), and compression wave velocity to shear wave velocity ratio ( v_p/〖 v〗_s ). The results of this study demonstrated that if the quality of the data is good and the simultaneous inversion is performed with high accuracy, the analysis of pre-stack seismic attributes can lead to the separation of seismic facies with a high resolution.

Porosity is one of the most important petrophysical parameters, studied in the subject of reservoir characterization. Determining porosity and how it changes in hydrocarbon reservoirs is an important issue that has been addressed in various researches. In this research, Poro-Acoustic Impedance (PAI) is introduced as an extended form of Acoustic Impedance (AI). The difference between PAI and AI is related porosity that is directly involved in the PAI. The inclusion of porosity data in the PAI formula made porosity effective in forward modeling and inversion of seismic data. The use of PAI in the forward modeling of synthetic models increases the contrast between the subsurface layers, and the contrast increases twice as compared to the AI. Band Limited Recursive inversion (BLRI) algorithm is used for inversion of synthetic seismograms and model-based algorithm is used for real seismic data inversion. For real data, due to the existence of well data, seismic horizons and geological information, using the basic model method for inversion is more accurate. The main difference between inversion using PAI and AI is that changes in porosity can be seen directly in the results of PAI inversion. The correlation of porosity with PAI and AI is -0.93 and -0.85, respectively, which shows that porosity has a stronger relationship with PAI. The use of PAI can be a quick and simple solution to understand porosity changes in hydrocarbon reservoirs and increase the accuracy of porosity determination in reservoirs to a great extent.

In a multilayer earth model, if the thickness of a layer is smaller than a theoretical limit, its refracted signals would not be recorded as first arrivals and the layer will be hidden. This event is known as blind zone. Also in a multilayer earth model if velocity of a layer is smaller than the surrounding layers (below and above layers), no refracted signal of their interfaces would appear as first arrivals. This event is called velocity inversion and the layer that has this property is said hidden layer. Due to these conditions, some errors would occur in calculation of depth and velocity of underlaying layer in seismic surveys. In this study it was shown that lateral arrivals such as postcritical reflections are useful for identifying blind zone and its parameters. Results of using this method on synthetic data are presented. It was also shown that if there is any undulation on the surface of refractor, it could be used for identifying hidden layer and calculating its parameter by determination of the horizontal spacing between effects of these undulations on travel-time curve.

Nowadays, the city’s vulnerability to earthquakes is world’s major problem that is expert’s concern in various fields. Technical and historical studies of earthquake in Iran shows that devastating earthquakes along the Dorouneh fault, in many cases leads to deterioration and death of hundreds and sometimes thousands of people.Bardaskan, due to the presence of several active faults around and within it, is in a high risk of earthquakes. The purpose of this study is to determine the vulnerability of urban elements, using models and methods in reducing the vulnerability of this city against earthquake. To reach this purpose, 16 physical and spatial factors affecting the vulnerability of urban space were identified at the global level then using location data, structural elements description, building behavior data, and determining the impact of each criteria in vulnerability level, an appropriate estimation of city’s vulnerability to earthquake were presented in the framework of integrated and planning models such as fuzzy and inversion Hierarchical Weight Process. We also discussed building damage modeling and micro-zoning against earthquake by presenting earthquake scenarios at different intensities. The results of this study show that by moving from the south to the north of Bardaskan, the vulnerability of the building’s block are increased. Earthquake scenarios modeling in the intensity of 6 and 8 Mercalli shows the vulnerability of the building were based on the 3, 1, 6, 5, 4 and 2 section respectively. The main reasons for this state are locating near the fault, peak ground acceleration, high density and low quality of building construction and building materials.