Joint estimation of porosity and saturation by combining a rock‐physics model and constrained prestack seismic waveform inversion

Quantitative estimation of rock physics properties is an important part of reservoir characterization. Most current seismic workflows in this field are based on amplitude variation with offset. Building on recent work on high resolution multi-parameter inversion for reservoir characterization, we construct a rock-physics parameterized elastic full-waveform inversion (EFWI) scheme. Within a suitably-formed multi-parameter EFWI, in this case a 2D frequency-domain isotropic-elastic FWI with a truncated Gauss-Newton optimization, any rock physics model with a well-defined mapping between its parameters and seismic velocity/density can be examined. We select a three-parameter porosity, clay content, and water saturation (PCS) parameterization, and link them to elastic properties using three representative rock physics models: the Han empirical model, the Voigt-Reuss-Hill boundary model, and the Kuster and Toksöz inclusion model. Numerical examples suggest that conditioning issues, which make a sequential inversion (in which velocities and density are first determined through EFWI, followed by PCS parameters) unstable, are avoided in this direct approach. Significant variability in inversion fidelity is visible from one rock physics model to another. However, the response of the inversion to the range of possible numerical optimization and frequency selections, as well as acquisition geometries, varies widely. Water saturation tends to be the most difficult property to recover in all situations examined. This can be explained with radiation pattern analysis, where very low relative scattering amplitudes from saturation perturbations are observed. An investigation performed with a Bayesian approach illustrates that the introduction of prior information may increase the inversion sensitivity to water saturation

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Hydrate quantification: Integrating full-waveform inversion, seismic attributes, and rock physics

Interpretation ◽

10.1190/int-2015-0021.1 ◽

2016 ◽

Vol 4 (1) ◽

pp. SA55-SA71 ◽

Cited By ~ 3

Author(s):

P. Jaiswal

Keyword(s):

Velocity Field ◽

Waveform Inversion ◽

Rock Physics ◽

Well Logs ◽

Full Waveform Inversion ◽

Secondary Porosity ◽

Full Waveform ◽

Hydrate Saturation ◽

Physics Model ◽

Rock Physics Model

Hydrate quantification from seismic data is a two-pronged challenge. The first is creating a velocity field with high enough resolution and accuracy such that it is a meaningful representation of hydrate variability in the host sediments. The second is constructing a rock-physics model that accounts for the appropriate growth of the hydrate and allows for the interpretation of the velocity field in terms of hydrate saturation. In this paper, both challenges are addressed in a quantification workflow that uses 2D seismic and colocated well logs. The study area is situated in the Krishna-Godavari Basin, offshore eastern Indian coast, where hydrate was discovered in the National Gas Hydrate Program Expedition 01 (NGHP-01). The workflow hinges on a rock-physics model that expresses total hydrate saturation in terms of primary (matrix) and secondary (fractures, faults, voids, etc.) porosities and their respective primary and secondary saturations and incorporates hydrate-filled secondary porosity into the rock as an additional grain type using the Hashin-Shtrikman bounds. The model is first applied to a set of well logs at a colocated site, NGHP-01-10, following which the application is extended into the seismic domain by (1) the incoherency attribute as a proxy for secondary porosity and (2) a full-waveform inversion-based P-wave velocity ([Formula: see text]) model as a proxy for primary saturation. The remaining — the primary porosity and secondary saturation — are assumed to remain the same across the seismic profile as at the site NGHP-01-10. The resulting, seismically estimated, hydrate saturation compares well with saturations from core depressurization at colocated sites NGHP-01-10 and NGHP-01-13. The quantification workflow presented here is potentially adaptable to other geographical areas with the caveat that empirical relations between porosity, saturation, and seismic attributes may have to be locally established.

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Integration of Rock Physics Model in Reservoir Characterization - a Case Study from Malay Basin Malaysia

10.2523/iptc-18049-ms ◽

2014 ◽

Author(s):

Ravi Kant Pathak ◽

Zulfiqar Ali ◽

Rizal Bakar

Keyword(s):

Reservoir Characterization ◽

Rock Physics ◽

Physics Model ◽

Rock Physics Model

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Multivariate probabilistic rock physics models using Kumaraswamy distributions

Geophysics ◽

10.1190/geo2021-0124.1 ◽

2021 ◽

pp. 1-43

Author(s):

Dario Grana

Keyword(s):

Probability Density Function ◽

Probability Density ◽

Density Function ◽

Random Variables ◽

Rock Physics ◽

Petrophysical Properties ◽

Kumaraswamy Distribution ◽

Model Predictions ◽

Physics Model ◽

Rock Physics Model

Rock physics models are physical equations that map petrophysical properties into geophysical variables, such as elastic properties and density. These equations are generally used in quantitative log and seismic interpretation to estimate the properties of interest from measured well logs and seismic data. Such models are generally calibrated using core samples and well log data and result in accurate predictions of the unknown properties. Because the input data are often affected by measurement errors, the model predictions are often uncertain. Instead of applying rock physics models to deterministic measurements, I propose to apply the models to the probability density function of the measurements. This approach has been previously adopted in literature using Gaussian distributions, but for petrophysical properties of porous rocks, such as volumetric fractions of solid and fluid components, the standard probabilistic formulation based on Gaussian assumptions is not applicable due to the bounded nature of the properties, the multimodality, and the non-symmetric behavior. The proposed approach is based on the Kumaraswamy probability density function for continuous random variables, which allows modeling double bounded non-symmetric distributions and is analytically tractable, unlike the Beta or Dirichtlet distributions. I present a probabilistic rock physics model applied to double bounded continuous random variables distributed according to a Kumaraswamy distribution and derive the analytical solution of the posterior distribution of the rock physics model predictions. The method is illustrated for three rock physics models: Raymer’s equation, Dvorkin’s stiff sand model, and Kuster-Toksoz inclusion model.

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Porosity estimation based on rock skeleton general formula and comprehensive pore structure parameter An application to a tight-sand reservoir

Interpretation ◽

10.1190/int-2021-0001.1 ◽

2021 ◽

pp. 1-59

Author(s):

Kai Lin ◽

Xilei He ◽

Bo Zhang ◽

Xiaotao Wen ◽

Zhenhua He ◽

...

Keyword(s):

Aspect Ratio ◽

Pore Structure ◽

Seismic Data ◽

Rock Physics ◽

Structure Parameters ◽

Pore Structure Parameter ◽

Elastic Parameters ◽

Physics Model ◽

Rock Physics Model ◽

Porosity Estimation

Most of current 3D reservoirs porosity estimation methods are based on analyzing the elastic parameters inverted from seismic data. It is well-known that elastic parameters vary with pore structure parameters such as pore aspect ratio, consolidate coefficient, critical porosity, etc. Thus, we may obtain inaccurate 3D porosity estimation if the chosen rock physics model fails properly address the effects of pore structure parameters on the elastic parameters. However, most of current rock physics models only consider one pore structure parameter such as pore aspect ratio or consolidation coefficient. To consider the effect of multiple pore structure parameters on the elastic parameters, we propose a comprehensive pore structure (CPS) parameter set that is generalized from the current popular rock physics models. The new CPS set is based on the first order approximation of current rock physics models that consider the effect of pore aspect ratio on elastic parameters. The new CPS set can accurately simulate the behavior of current rock physics models that consider the effect of pore structure parameters on elastic parameters. To demonstrate the effectiveness of proposed parameters in porosity estimation, we use a theoretical model to demonstrate that the proposed CPS parameter set properly addresses the effect of pore aspect ratio on elastic parameters such as velocity and porosity. Then, we obtain a 3D porosity estimation for a tight sand reservoir by applying it seismic data. We also predict the porosity of the tight sand reservoir by using neural network algorithm and a rock physics model that is commonly used in porosity estimation. The comparison demonstrates that predicted porosity has higher correlation with the porosity logs at the blind well locations.

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