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1 
Post and prestack attribute analysis and inversion of Blackfoot 3Dseismic datasetSwisi, Abdulsalam Amer 19 October 2009
The objective of this research is comparative analysis of several standard and one new seismic post and prestack inversion methods and Amplitude Variation with Offset (AVO) attribute analysis in application to the CREWES Blackfoot 3D dataset. To prepare the data to the inversion, I start with processing the dataset by using ProMAX software. This processing, in general, includes static and refraction corrections, velocity analysis and stacking the data. The results show good quality images, which are suitable for inversion.<p>
Five types of inversion methods are applied to the dataset and compared. Three of these methods produce solutions for the poststack Acoustic Impedance (AI) and are performed by using the industrystandard HampsonRussell software. The fourth method uses our inhouse algorithm called SILC and implemented in IGeoS seismic processing system. In the fifth approach, the prestack gathers are inverted for elastic impedance by rangelimited stacking of the commonmidpoint (CMP) gathers in offsets and/or angles and then performing independent inversion of angle stack. Further, simultaneous inversion is applied to prestack seismic data to invert for both the P and Swave impedances. These impedances are used to extract the Lamé parameters multiplied by density (LMR), and used to extract the ratios between the P and Swave velocities. In addition, CMP gathers are used to produce AVO attribute images, which are good indicators of gas reservoirs. Finally, the results of the different inversion techniques are interpreted and correlated with welllog data and used to characterize the reservoir.<p>
The different inversion results show clearly the reservoir with its related low impedance within the channel. The poststack inversion gives the best results; in particular, the modelbased inversion shows smoothed images of it while SILC provides a different, higherresolution image. The elastic impedance also gives results similar to the poststack inversion. Prestack inversion and AVO attributes give reasonable results in cross sections near the center of study area. In other areas, performance of prestack inversion is poorer, apparently because of reflection aperture limitations.

2 
Post and prestack attribute analysis and inversion of Blackfoot 3Dseismic datasetSwisi, Abdulsalam Amer 19 October 2009 (has links)
The objective of this research is comparative analysis of several standard and one new seismic post and prestack inversion methods and Amplitude Variation with Offset (AVO) attribute analysis in application to the CREWES Blackfoot 3D dataset. To prepare the data to the inversion, I start with processing the dataset by using ProMAX software. This processing, in general, includes static and refraction corrections, velocity analysis and stacking the data. The results show good quality images, which are suitable for inversion.<p>
Five types of inversion methods are applied to the dataset and compared. Three of these methods produce solutions for the poststack Acoustic Impedance (AI) and are performed by using the industrystandard HampsonRussell software. The fourth method uses our inhouse algorithm called SILC and implemented in IGeoS seismic processing system. In the fifth approach, the prestack gathers are inverted for elastic impedance by rangelimited stacking of the commonmidpoint (CMP) gathers in offsets and/or angles and then performing independent inversion of angle stack. Further, simultaneous inversion is applied to prestack seismic data to invert for both the P and Swave impedances. These impedances are used to extract the Lamé parameters multiplied by density (LMR), and used to extract the ratios between the P and Swave velocities. In addition, CMP gathers are used to produce AVO attribute images, which are good indicators of gas reservoirs. Finally, the results of the different inversion techniques are interpreted and correlated with welllog data and used to characterize the reservoir.<p>
The different inversion results show clearly the reservoir with its related low impedance within the channel. The poststack inversion gives the best results; in particular, the modelbased inversion shows smoothed images of it while SILC provides a different, higherresolution image. The elastic impedance also gives results similar to the poststack inversion. Prestack inversion and AVO attributes give reasonable results in cross sections near the center of study area. In other areas, performance of prestack inversion is poorer, apparently because of reflection aperture limitations.

3 
Novel stochastic inversion methods and workflow for reservoir characterization and monitoringXue, Yang, active 2013 18 February 2014 (has links)
Reservoir models are generally constructed from seismic, well logs and other related datasets using inversion methods and geostatistics. It has already been recognized by the geoscientists that such a process is prone to nonuniqueness. Practical methods for estimation of uncertainty still remain elusive. In my dissertation, I propose two new methods to estimate uncertainty in reservoir models from seismic, well logs and well production data. The first part of my research is aimed at estimating reservoir impedance models and their uncertainties from seismic data and well logs. This constitutes an inverse problem, and we recognize that multiple models can fit the measurements. A deterministic inversion based on minimization of the error between the observation and forward modeling only provides one of the bestfit models, which is usually bandlimited. A complete solution should include both models and their uncertainties, which requires drawing samples from the posterior distribution. A global optimization method called very fast simulated annealing (VFSA) is commonly used to approximate posterior distribution with fast convergence. Here I address some of the limitations of VFSA by developing a new stochastic inference method, named Greedy Annealed Importance Sampling (GAIS). GAIS combines VFSA with greedy importance sampling (GIS), which uses a greedy search in the important regions located by VFSA to attain fast convergence and provide unbiased estimation. I demonstrate the performance of GAIS on post and prestack data from real fields to estimate impedance models. The results indicate that GAIS can estimate both the expectation value and the uncertainties more accurately than using VFSA alone. Furthermore, principal component analysis (PCA) as an efficient parameterization method is employed together with GAIS to improve lateral continuity by simultaneous inversion of all traces. The second part of my research involves estimation of reservoir permeability models and their uncertainties using quantitative joint inversion of dynamic measurements, including synthetic production data and timelapse seismic related data. Impacts from different objective functions or different data sets on the model uncertainty and model predictability are investigated as well. The results demonstrate that joint inversion of production data and timelapse seismic related data (water saturation maps here) reduces model uncertainty, improves model predictability and shows superior performance than inversion using one type of data alone. / text

4 
Full Waveform Inversion Using Oriented Time Migration MethodZhang, Zhendong 12 April 2016 (has links)
Full waveform inversion (FWI) for reflection events is limited by its linearized update requirements given by a process equivalent to migration. Unless the background velocity model is reasonably accurate the resulting gradient can have an inaccurate update direction leading the inversion to converge into what we refer to as local minima of the objective function. In this thesis, I first look into the subject of full model wavenumber to analysis the root of local minima and suggest the possible ways to avoid this problem. And then I analysis the possibility of recovering the corresponding wavenumber components through the existing inversion and migration algorithms. Migration can be taken as a generalized inversion method which mainly retrieves the high wavenumber part of the model. Conventional impedance inversion method gives a mapping relationship between the migration image (high wavenumber) and model parameters (full wavenumber) and thus provides a possible cascade inversion strategy to retrieve the full wavenumber components from seismic data. In the proposed approach, consider a mild lateral variation in the model, I find an analytical Frechet derivation corresponding to the new objective function. In the proposed approach, the gradient is given by the oriented timedomain imaging method. This is independent of the background velocity. Specifically, I apply the oriented timedomain imaging (which depends on the reflection slope instead of a background velocity) on the data residual to obtain the geometrical features of the velocity perturbation. Assuming that density is constant, the conventional 1D impedance inversion method is also applicable for 2D or 3D velocity inversion within the process of FWI. This method is not only capable of inverting for velocity, but it is also capable of retrieving anisotropic parameters relying on linearized representations of the reflection response. To eliminate the crosstalk artifacts between different parameters, I utilize what I consider being an optimal parameterization. To do so, I extend the prestack timedomain migration image in incident angle dimension to incorporate angular dependence needed by the multiparameter inversion. For simple models, this approach provides an efficient and stable way to do full waveform inversion or modified seismic inversion and makes the anisotropic inversion more practical. Results based on synthetic data of isotropic and anisotropic case examples illustrate the benefits and limitations of this method.

5 
Methods for Bayesian inversion of seismic dataWalker, Matthew James January 2015 (has links)
The purpose of Bayesian seismic inversion is to combine information derived from seismic data and prior geological knowledge to determine a posterior probability distribution over parameters describing the elastic and geological properties of the subsurface. Typically the subsurface is modelled by a cellular grid model containing thousands or millions of cells within which these parameters are to be determined. Thus such inversions are computationally expensive due to the size of the parameter space (being proportional to the number of grid cells) over which the posterior is to be determined. Therefore, in practice approximations to Bayesian seismic inversion must be considered. A particular, existing approximate workflow is described in this thesis: the socalled twostage inversion method explicitly splits the inversion problem into elastic and geological inversion stages. These two stages sequentially estimate the elastic parameters given the seismic data, and then the geological parameters given the elastic parameter estimates, respectively. In this thesis a number of methodologies are developed which enhance the accuracy of this approximate workflow. To reduce computational cost, existing elastic inversion methods often incorporate only simplified prior information about the elastic parameters. Thus a method is introduced which transforms such results, obtained using prior information specified using only twopoint geostatistics, into new estimates containing sophisticated multipoint geostatistical prior information. The method uses a socalled deep neural network, trained using only synthetic instances (or `examples') of these two estimates, to apply this transformation. The method is shown to improve the resolution and accuracy (by comparison to well measurements) of elastic parameter estimates determined for a real hydrocarbon reservoir. It has been shown previously that socalled mixture density network (MDN) inversion can be used to solve geological inversion analytically (and thus very rapidly and efficiently) but only under certain assumptions about the geological prior distribution. A socalled prior replacement operation is developed here, which can be used to relax these requirements. It permits the efficient MDN method to be incorporated into general stochastic geological inversion methods which are free from the restrictive assumptions. Such methods rely on the use of Markovchain MonteCarlo (MCMC) sampling, which estimate the posterior (over the geological parameters) by producing a correlated chain of samples from it. It is shown that this approach can yield biased estimates of the posterior. Thus an alternative method which obtains a set of noncorrelated samples from the posterior is developed, avoiding the possibility of bias in the estimate. The new method was tested on a synthetic geological inversion problem; its results compared favourably to those of Gibbs sampling (a MCMC method) on the same problem, which exhibited very significant bias. The geological prior information used in seismic inversion can be derived from real images which bear similarity to the geology anticipated within the target region of the subsurface. Such socalled training images are not always available from which this information (in the form of geostatistics) may be extracted. In this case appropriate training images may be generated by geological experts. However, this process can be costly and difficult. Thus an elicitation method (based on a genetic algorithm) is developed here which obtains the appropriate geostatistics reliably and directly from a geological expert, without the need for training images. 12 experts were asked to use the algorithm (individually) to determine the appropriate geostatistics for a physical (target) geological image. The majority of the experts were able to obtain a set of geostatistics which were consistent with the true (measured) statistics of the target image.

6 
Interferometric Imaging and its Application to 4D ImagingSinha, Mrinal 03 1900 (has links)
This thesis describes new interferometric imaging methods for migration and waveform
inversion. The key idea is to use reflection events from a known reference reflector
to ”naturally redatum” the receivers and sources to the reference reflector.
Here, ”natural redatuming” is a datadriven process where the redatuming Green’s
functions are obtained from the data. Interferometric imaging eliminates the statics
associated with the noisy overburden above the reference reflector.
To mitigate the defocussing caused by overburden errors I first propose the use
of interferometric leastsquares migration (ILSM) to estimate the migration image.
Here, a known reflector is used as the reference interface for ILSM, and the data
are naturally redatumed to this reference interface before imaging. Numerical results
on synthetic and field data show that ILSM can significantly reduce the defocussing
artifacts in the migration image.
Next, I develop a waveform tomography approach for inverting the velocity model
by mitigating the velocity errors in the overburden. Unresolved velocity errors in the
overburden velocity model can cause conventional fullwaveform inversion to get stuck
in a local minimum. To resolve this problem, I present interferometric fullwaveform
inversion (IFWI), where conventional waveform tomography is reformulated so a velocity
model is found that minimizes the objective function with an interferometric
crosscorrelogram misfit. Numerical examples show that IFWI, compared to FWI,
computes a significantly more accurate velocity model in the presence of a nearsurface
with unknown velocity anomalies.
I use IFWI and ILSM for 4D imaging where seismic data are recorded at different
times over the same reservoir. To eliminate the timevarying effects of the near
surface both data sets are virtually redatumed to a common reference interface before
migration. This largely eliminates the overburdeninduced statics errors in both data
sets. Results with synthetic and field data show that ILSM and IFWI can suppress
the artifacts caused by nonrepeatability in timelapse surveys. This can lead to a
much more accurate characterization of the movement of fluids over time. In turn,
this information can be used to optimize the extraction of resources in enhanced oil
recovery (EOR) operations.

7 
Developing and utilizing the wavefield kinematics for efficient wavefield extrapolationWaheed, Umair bin 08 1900 (has links)
Natural gas and oil from characteristically complex unconventional reservoirs, such
as organic shale, tight gas and oil, coalbed methane; are transforming the global energy market. These conventional reserves exist in complex geologic formations where conventional seismic techniques have been challenged to successfully image the subsurface. To acquire maximum benefits from these unconventional reserves, seismic anisotropy must be at the center of our modeling and inversion workflows.
I present algorithms for fast traveltime computations in anisotropic media. Both raybased and finitedifference solvers of the anisotropic eikonal equation are developed. The proposed algorithms present novel techniques to obtain accurate traveltime solutions for anisotropic media in a costefficient manner. The traveltime computation algorithms are then used to invert for anisotropy parameters. Specifically, I develop inversion techniques by using diffractions and diving waves in the seismic data. The diffractionbased inversion algorithm can be combined with an isotropic fullwaveform inversion (FWI) method to obtain a highresolution model for the anellipticity anisotropy parameter. The inversion algorithm based on diving waves is useful for building initial anisotropic models for depthmigration and FWI. I also develop the idea of 'effective elliptic models' for obtaining solutions of the anisotropic twoway wave equation. The proposed technique offers a viable alternative for wavefield computations in anisotropic media using a computationally cheaper wave propagation operator.
The methods developed in the thesis lead to a direct cost savings for imaging and inversion projects, in addition to a reduction in turnaround time. With an eye on the next generation inversion methods, these techniques allow us to incorporate more accurate physics into our modeling and inversion framework.

8 
Multiparameter Analysis and Inversion for Anisotropic Media Using the Scattering Integral MethodDjebbi, Ramzi 24 October 2017 (has links)
The main goal in seismic exploration is to identify locations of hydrocarbons reservoirs and give insights on where to drill new wells. Therefore, estimating an Earth model that represents the right physics of the Earth's subsurface is crucial in identifying these targets. Recent seismic data, with long offsets and wide azimuth features, are more sensitive to anisotropy. Accordingly, multiple anisotropic parameters need to be extracted from the recorded data on the surface to properly describe the model. I study the prospect of applying a scattering integral approach for multiparameter inversion for a transversely isotropic model with a vertical axis of symmetry. I mainly analyze the sensitivity kernels to understand the sensitivity of seismic data to anisotropy parameters. Then, I use a frequency domain scattering integral approach to invert for the optimal parameterization.
The scattering integral approach is based on the explicit computation of the sensitivity kernels. I present a new method to compute the traveltime sensitivity kernels for wave equation tomography using the unwrapped phase. I show that the new kernels are a better alternative to conventional crosscorrelation/Rytov kernels. I also derive and analyze the sensitivity kernels for a transversely isotropic model with a vertical axis of symmetry. The kernels structure, for various opening/scattering angles, highlights the tradeoff regions between the parameters. For a surface recorded data, I show that the normal moveout velocity vn, ƞ and δ parameterization is suitable for a simultaneous inversion of diving waves and reflections. Moreover, when seismic data is inverted hierarchically, the horizontal velocity vh, ƞ and ϵ is the parameterization with the least tradeoff. In the frequency domain, the hierarchical inversion approach is naturally implemented using frequency continuation, which makes vh, ƞ and ϵ parameterization attractive.
I formulate the multiparameter inversion using the scattering integral method. Application to various synthetic and real data examples show accurate inversion results. I show that a good background ƞ model is required to accurately recover vh. For 3D problems, I promote a hybrid approach, where efficient ray tracing is used to compute the sensitivity kernels. The proposed method highly reduces the computational cost.

9 
Testing the Feasibility of Using PERM to Apply ScatteringAngle Filtering in the ImageDomain for FWI ApplicationsAlzahrani, Hani Ataiq 09 1900 (has links)
Full Waveform Inversion (FWI) is a nonlinear optimization problem aimed to estimating subsurface parameters by minimizing the misfit between modeled and recorded seismic data using gradient descent methods, which are the only practical choice because of the size of the problem. Due to the high nonlinearity of the problem, gradient methods will converge to a local minimum if the starting model is not close to the true one. The accuracy of the longwavelength components of the initial model controls the level of nonlinearity of the inversion. In order for FWI to converge to the global minimum, we have to obtain the long wavelength components of the model before inverting for the short wavelengths. Ultralow temporal frequencies are sensitive to the smooth (long wavelength) part of the model, and can be utilized by waveform inversion to resolve that part. Unfortunately, frequencies in this range are normally missing in field data due to data acquisition limitations. The lack of low frequencies can be compensated for by utilizing wideaperture data, as they include arrivals that are especially sensitive to the long wavelength components of the model. The higher the scattering angle of a 5 recorded event, the higher the model wavelength it can resolve. Based on this property, a scatteringangle filtering algorithm is proposed to start the inversion process with events corresponding to the highest scattering angle available in the data, and then include lower scattering angles progressively. The large scattering angles will resolve the smooth part of the model and reduce the nonlinearity of the problem, then the lower ones will enhance the resolution of the model. Recorded data is first migrated using Prestack Exploding Reflector Migration (PERM), then the resulting prestack image is transformed into angle gathers to which an angle filtering process is applied to remove events below a certain cutoff angle. The filtered prestack image cube is then demigrated (forward modeled) to produce filtered surface data that can be used in waveform inversion. Numerical tests confirm the feasibility of the proposed filtering algorithm. However, the accuracy of the filtered section is limited by PERM’s singularity for horizontallytraveling waves, which in turn is dependent on the velocity model used for migration and demigration

10 
[en] 1D SEISMIC INVERSION USING SIMULATED ANNEALING / [pt] A INVERSÃO SÍSMICA 1D USANDO O SIMULATED ANNEALINGJORGE MAGALHAES DE MENDONCA 25 November 2005 (has links)
[pt] O problema de Inversão Sísmica envolve a determinação
das
propriedades físicas da superfície a partir de dados
amostrados na superfície. A construção de um modelo
matemático da resposta da subsuperfície à excitação de
uma
fonte sísmica, tendo como parâmetros as propriedades
físicas da subsuperfície, fornece um modelo sintético
desta resposta para determinados valores dos parâmetros.
Isto permite comparar dados amostrados e modelos
sintético. A perturbação do modelo pela variação dos
seus
parâmetros pode aproximar dados amostrados e sintéticos
e
colocar o problema da Inversão como um problema de
minimização de uma função de erro que os ajuste de forma
adequada. Usualmente, os métodos que tentam minimizar a
medida a medida de erro supõem um comportamento linear
entre a perturbação do modelo e esta medida. Na maioria
dos problemas geofísicos, esta medida apresenta um alto
grau de não linearidade e uma grande quantidade de
mínimos
locais. Isto torna estes métodos baseados em
aproximações
lineares muito sensíveis à escolha de uma boa solução
inicial, o que nem sempre está disponível.
Como resolver este problema sem uma boa solução
inicial? A teoria da Inferência Bayesiana oferece uma
solução pelo uso de informação a priori sob o espaço dos
parâmetros. O problema de Inversão volta então a ser um
problema de otimização onde se precisa maximizar a
probabilidade a posteriori dos parâmetros assumirem um
certo valor dado que se obteve o resultado da amostragem
dos dados. Este problema é resolvido pelo método do
Simulated Annealing (SA), método de otimização global
que
faz uma busca aleatória direcionada no espaço de
solução.
Este método foi proposto por uma analogia entre o
recozimento física de sólidos e problemas de otimização.
O SA, na sua variante Very Fast Simulated
Annealing (VFSA), é aplicado na solução de problemas de
Inversão Sísmica 1 D para modelos acústico e elásticos
gerados sinteticamente. A avaliação do desempenho do SA
usando medidas de erro com diferentes normas é realizada
para um modelo elástico adicionado de ruído aleatório. / [en] The seismic inverse problem involves determining the
subsurface physical properties from data sampled at
Earth`s surface. A mathematical model of the response of
the subsurface excited by a seismic source, having
physical properties as parameters, provides a synthetic
model for this response. This makes possible to compare
sampled and synthetic data. The perturbation in the model
due to the variation of its parameters can approximate
these data and states the inversion problem as the
minimization of an error function that fits them
adequately. Usually, the methods which attempt to minimize
this error assume that a perturbation in the model is
linearly relates with a perturbation in the measured
response. Most geophysical inverse problems are highly
nonlinear and are rife with local minima. Therefore these
methods are very sensitive to the choice of the initial
model and good starting solutions may not be available.
What should be done, if there is no basis for an
initial guess? The theory of Bayesian inference provides
an answer to this question taking into account the prior
information about the parameter space. The inverse problem
can then be stated as an optimization problem whose goal
is to maximize the posterior probability that the set of
parameters has a certain value once given the result of
the sample. This problem is solved by the Simulated
Annealing method, a global optimization method that
executes a oriented random search in the solution space.
This method comes from an analogy between the physical
annealing of solids and optimization problems.
The Very Fast Simulated Annealing (VFSA), a
variant of SA, is applied to the solution of 1 D seismic
inverse problems generated synthetically by acoustic and
alastic done by a elastic model with additive noise.

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