One of the leading challenges in hydrocarbon recovery is predicting fluid distribution throughout the reservoir, using dispersion of seismic elastic parameters to solve this problem is a method with great potential. Previous studies reveal that predicting frequency-dependent seismic elastic parameters is difficult because of their sensitivity to seismic wave amplitude. The frequency-dependent AVO inversion schemes are widely used to estimate the dispersion gradient attributes for fluid prediction. However, these methods strongly depend on the advanced spectral decomposition and the wavelet overprint effect in time-frequency information. For this reason, this study presents an innovative technique that combines prestack AVO inversion and linear Bayesian inversion algorithm to predict directly frequency-dependent P-wave velocity of multilayered medium from seismic reflection data, which can quantitatively describe the change of P-wave velocity in seismic frequency band. Furthermore, frequency-dependent elastic parameters were used to define a dispersion factor for fluid prediction in thin sandstone reservoirs. The novelty of the study is that the proposed approach introduces prestack AVO inversion to provide reliable initial model and constructs dispersive P-wave velocity inversion framework of layered medium for the first time. Additionally, the dispersive elastic parameters have more potential applications than the dispersion gradient attributes. Tests on the synthetic and real data demonstrate that the frequency-dependent P-wave velocity of multilayered medium can be estimated reasonably and stably. In this application, we use a test well to assess locally the performance of the technique.

As a significant step to characterize reservoir, reservoir properties estimation play an essential role to link elastic parameter with physical property parameter. Most conventional-used estimation methods are implemented in sequential tactics. However, to predict the rock and fluid properties from inverted seismic elastic attributes doesn’t only add cumulative error but also increase uncertainty and computational cost of inversion. We have developed a direct seismic petrophysical inversion method intended for disadvantages of sequential rock inversion methods. The method incorporates the Keys-Xu approximate model into the seismic forward operator, to built a direct correlation between reservoir properties and observed seismic data. Reservoir parameters want to retrieve can be obtained from prestack seismic data based on rock-physics model and exact Zoeppritz equation. The inversion process combines Bayesian theory and Gaussian prior distribution, by which the estimation of petrophysical properties, such as porosity, clay volume, and fluid saturation, can be expressed as a posterior probability density function. The optimal solution is the random value corresponding to the maximum posterior probability density. Theoretical model test and real data sets application show that this method can obtain accurate results. The main advantage of this method is the cumulative error of the two-step method is decreased and the uncertainty in the inversion process is reduced. The use of exact Zoeppritz equation aviod approximation error of other approximations, like Aki-Richards, Shuey, etc, and makes the method applicable to far offset seismic data.

One of the main objectives in the hydrocarbon reservoir characterization is determining rock and fluid properties that rely extensively on inference from seismic observations. In this letter, we present a novel Bayesian prestack inversion method using frequency-dependent amplitude versus offset (AVO) analysis with the goal to directly estimate gas saturation and porosity of a target thin reservoir zone. The proposed methodology is based on an improved Markov chain Monte Carlo (MCMC) sampling algorithm, which is computationally very coefficient due to its satisfactory acceptance probability and the convergence speed of Markov chains. Using a nonlinear rock physics model (RPM), properly calibrated for the investigating area, and a seismic forward operator based on the frequency-domain propagator matrix approach in the Bayesian inversion framework, we then evaluate the full posterior probability distribution of petrophysical parameters conditioned to seismic data and available prior information, using the MCMC algorithm in which we iteratively sample within the petrophysical property space. The proposed inversion approach is validated through applications to a synthetic reservoir model and the real seismic data from gas-bearing reservoirs with strong velocity dispersion.

Seismic spectral decomposition applications are often restricted to the magnitude component of time–frequency spectra because it has been very challenging to make a meaningful interpretation of the phase information. In seismically thin layers, nevertheless, phase decomposition can be useful for the enhanced delineation of subsurface lateral variations, as seismic phase singularities are believed to tightly relate to the geologic features and geofluid effects. In this letter, we introduce an improved phase decomposition approach based on a high-resolution complex-spectra decomposition technique for better thin reservoir characterizations. The proposed method is applied to decompose seismic responses into the various phase components that sum to reconstruct the original traces. Via assuming the 0° phase seismic data, results from the synthetic and physical modeling examples imply that reflection anomalies associated with reservoir hydrocarbons can be magnified on the specific phase components. This magnification thus allows the reservoir geofluid variations to be better discriminated from certain lithologic influences that also substantially affect the total seismic reflection amplitudes, which are otherwise buried in the broadband responses.

Studies of frequency dependence of seismic data anomalies on partially gas-saturated reservoir have been performed for many years. Essentially, the frequency-dependent seismic signature represents a potential and largely untapped source of information for the detections of subsurface target properties. Through analyzing the anomalous feathers of amplitude variations with the angle of incidence and frequency (AVAF), both theoretically and algorithmically, it is possible to discriminate hydrocarbon from variations in other reservoir properties. For a layered structure model, however, it can be challenging to employ the conventional Zoeppritz equation-based method that may not accurately describe complex reflections considering the effects of both the layered structure of a reservoir and the attenuative and dispersive property of rocks. We introduce a novel hydrocarbon detection approach based on Bayesian inversion of frequency- and angle-dependent reflection signatures from a tight gas sandstone reservoir having strong attenuation and velocity dispersion. The proposed inversion scheme employs the propagator matrix method as a description of seismic responses for the stratified model and spectral decomposition technique to obtain multifrequency amplitude information. The synthetic test and real application show the proposed inversion approach has the potential to be useful in detections of hydrocarbon accumulation.

Phase reversal is an important feather that can often be observed in seismic amplitude variation versus offset (AVO) analysis. A proper description of phase shift behaviors from a reservoir with multiple pore-fluids, nevertheless, should consider influences of attenuation and velocity dispersion. We propose a novel method here for estimating phase reversals in cases where frequency-dependent attenuation and dispersion are present. Numerical results from the proposed method illustrate that there exist significant variations in terms of magnitude and phase of seismic reflections, between the elastic and anelastic cases. In particular, we observe a gradually continuous phase shift with the angle in place of a phase jump in the elastic model. Numerical results analysis of a frequency-dependent rock physics model indicates a change in gas saturation and fluid patch scale causes apparent impacts on the phase variations from waveform data using the Hilbert transform. The recognition of such phenomena from seismic AVO responses thus offers an obvious potential to strengthen our abilities of detecting hydrocarbon reservoirs. In addition, the proposed method may be of great importance to unconventional reservoir exploration and CO2changes monitoring in brine-filled reservoirs.

At seismic and sonic frequencies, the major cause of wave attenuation and dispersion in fluid-saturated rocks might be the wave-induced fluid flow on microscopic and mesoscopic scales. However, it is challenging to assess these effects as the attenuation mechanisms related to both heterogeneities that cannot be expected to be independent. This is due to the fact that, fluid flow taking placing at the mesoscopic scale may be impacted by squirt flow mechanism in the presence of microscopic heterogeneities via modifying the dry rock to be frequency-dependent complex moduli. Understanding the integrated effects, related to microscopic squirt flow and wave-induced fluid flow of mesoscopic heterogeneities, would be important for quantifying the relative contribution of the interdependent energy loss mechanisms. We introduce a procedure in this letter to estimate the frequency-dependent seismic attenuation and dispersion by considering the combined presence of microscopic and mesoscopic heterogeneities. The corresponding seismic reflections of a finely stratified model with a dispersive reservoir are calculated using a propagator matrix method in the frequency domain to study the sensitivity of seismic signatures to pore-fluid mobility and rock heterogeneities.