scholarly journals Estimation of rock physics properties from seismic attributes — Part 1: Strategy and sensitivity analysis

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. M35-M53 ◽  
Author(s):  
Bastien Dupuy ◽  
Stéphane Garambois ◽  
Jean Virieux
Keyword(s):  
Input Data ◽  
Reservoir Characterization ◽  
Quantitative Estimation ◽  
A Priori ◽  
Rock Physics ◽  
Fluid Phase ◽  
Quality Factors ◽  
Simultaneous Inversion ◽  
Fluid Saturation ◽  
Wide Range

The quantitative estimation of rock physics properties is of great importance in any reservoir characterization. We have studied the sensitivity of such poroelastic rock physics properties to various seismic viscoelastic attributes (velocities, quality factors, and density). Because we considered a generalized dynamic poroelastic model, our analysis was applicable to most kinds of rocks over a wide range of frequencies. The viscoelastic attributes computed by poroelastic forward modeling were used as input to a semiglobal optimization inversion code to estimate poroelastic properties (porosity, solid frame moduli, fluid phase properties, and saturation). The sensitivity studies that we used showed that it was best to consider an inversion system with enough input data to obtain accurate estimates. However, simultaneous inversion for the whole set of poroelastic parameters was problematic due to the large number of parameters and their trade-off. Consequently, we restricted the sensitivity tests to the estimation of specific poroelastic parameters by making appropriate assumptions on the fluid content and/or solid phases. Realistic a priori assumptions were made by using well data or regional geology knowledge. We found that (1) the estimation of frame properties was accurate as long as sufficient input data were available, (2) the estimation of permeability or fluid saturation depended strongly on the use of attenuation data, and (3) the fluid bulk modulus can be accurately inverted, whereas other fluid properties have a low sensitivity. Introducing errors in a priori rock physics properties linearly shifted the estimations, but not dramatically. Finally, an uncertainty analysis on seismic input data determined that, even if the inversion was reliable, the addition of more input data may be required to obtain accurate estimations if input data were erroneous.

scholarly journals Stochastic reservoir characterization using prestack seismic data

Geophysics ◽  
2004 ◽  
Vol 69 (4) ◽  
pp. 978-993 ◽  
Author(s):  
Jo Eidsvik ◽  
Per Avseth ◽  
Henning Omre ◽  
Tapan Mukerji ◽  
Gary Mavko
Keyword(s):  
Reservoir Characterization ◽  
A Priori ◽  
Rock Physics ◽  
Real Data ◽  
Spatial Coupling ◽  
The North ◽  
Amplitude Versus Offset ◽  
Likelihood Model ◽  
Avo Attributes ◽  
The Impact

Reservoir characterization must be based on information from various sources. Well observations, seismic reflection times, and seismic amplitude versus offset (AVO) attributes are integrated in this study to predict the distribution of the reservoir variables, i.e., facies and fluid filling. The prediction problem is cast in a Bayesian setting. The a priori model includes spatial coupling through Markov random field assumptions and intervariable dependencies through nonlinear relations based on rock physics theory, including Gassmann's relation. The likelihood model relating observations to reservoir variables (including lithology facies and pore fluids) is based on approximations to Zoeppritz equations. The model assumptions are summarized in a Bayesian network illustrating the dependencies between the reservoir variables. The posterior model for the reservoir variables conditioned on the available observations is defined by the a priori and likelihood models. This posterior model is not analytically tractable but can be explored by Markov chain Monte Carlo (MCMC) sampling. Realizations of reservoir variables from the posterior model are used to predict the facies and fluid‐filling distribution in the reservoir. A maximum a posteriori (MAP) criterion is used in this study to predict facies and pore‐fluid distributions. The realizations are also used to present probability maps for the favorable (sand, oil) occurrence in the reservoir. Finally, the impact of seismic AVO attributes—AVO gradient, in particular—is studied. The approach is demonstrated on real data from a turbidite sedimentary system in the North Sea. AVO attributes on the interface between reservoir and cap rock are extracted from 3D seismic AVO data. The AVO gradient is shown to be valuable in reducing the ambiguity between facies and fluids in the prediction.

Dynamic to Static Modeling

Geophysics ◽  
2021 ◽  
pp. 1-43
Author(s):  
Javad Sharifi
Keyword(s):  
Reservoir Characterization ◽  
Triaxial Compression ◽  
Rock Physics ◽  
Regression Equations ◽  
Compression Tests ◽  
In Situ Tests ◽  
Factors Affecting ◽  
Static Modulus ◽  
Wide Range ◽  
Static Modeling

Dynamic-to-static modulus conversion has long been recognized as a complicated and challenging task in reservoir characterization and seismic geomechanics, and many single- and two-variable regression equations have been proposed. In practice however, the form and constants of the regression equation are variable from case to case. I introduce a methodology for estimating the static moduli called dynamic-to-static modeling (DTS). The methodology was validated by laboratory tests (ultrasonic and triaxial compression tests) to obtain dynamic and quasi-static bulk and Young’s (elasticity) moduli. Next, rock deformation phenomena were simulated considering different parameters affecting the process. The dynamic behavior was further modeled using rock physics methods. Unlike the conventional dynamic-to-static conversion procedures, the method considers a wide range of factors affecting the relationship between the dynamic and static moduli, including strain amplitude, dispersion, rock failure mechanism, pore shape, crack parameters, poromechanics, and upscaling. A comparison between the data from laboratory and in-situ tests and the estimation results indicated promising findings. The accuracy of the results was assessed by the analysis of variance (ANOVA). In addition to modeling the static moduli, DTS can be used to verify the static and dynamic moduli values with appropriate accuracy when core data is not available.

scholarly journals Simultaneous Seismic Inversion for Reservoir Characterization at Poseidon Field, Browse Basin, Australia

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Suleman Mauritz Sihotang ◽  
Ida Herawati
Keyword(s):  
Oil And Gas ◽  
Reservoir Characterization ◽  
Research Area ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Well Log ◽  
Gas Field ◽  
Simultaneous Inversion ◽  
Fluid Saturation ◽  
Oil And Gas Field

Seismic inversion method has been widely used to obtain reservoir property in an oil and gas field. In this research, one of inversion methods known as simultaneous inversion is used to analyze reservoir characterization at Poseidon Field, Browse Basin. Simultaneous inversion is applied to partial angle stack data and result in volume of Acoustic Impedance (AI), Shear Impedance (SI) and Lame parameter (LMR). The objective of this study is to determine distribution of sandstone lithology with gas saturated in Plover reservoir formation. Sensitivity analysis is done by cross-plotting elastic and Lame parameter from five well log data and analyzing lithology type and fluid saturation. Based on those cross-plots, lithological type can be identified from AI, λρ, µρ and λ/µ parameters. Meanwhile, the presence of gas can be discriminated using SI, λρ, and λ/µ parameters. Gas-saturated sandstone presence is characterized by Lambda-Rho value less than 50 GPa g cc-1 and Lambda over Mu value less than 0.8 GPa g cc-1. Maps of each parameter are generated at reservoir interval. Based on those maps, it can be concluded that gas sand spread out in the eastern and western areas of research area.

Direct updating of rock physics properties using elastic full-waveform inversion

Geophysics ◽  
2021 ◽  
pp. 1-64
Author(s):  
Qi Hu ◽  
Scott Keating ◽  
Kristopher A. Innanen ◽  
Huaizhen Chen
Keyword(s):  
Reservoir Characterization ◽  
Seismic Velocity ◽  
Water Saturation ◽  
Quantitative Estimation ◽  
Waveform Inversion ◽  
Rock Physics ◽  
Full Waveform Inversion ◽  
Full Waveform ◽  
Physics Model ◽  
Rock Physics Model

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

scholarly journals Probabilistic approach to rock physics modeling

Geophysics ◽  
2014 ◽  
Vol 79 (2) ◽  
pp. D123-D143 ◽  
Author(s):  
Dario Grana
Keyword(s):  
Probability Distribution ◽  
Reservoir Characterization ◽  
Probability Distributions ◽  
Probabilistic Approach ◽  
Rock Physics ◽  
Random Variable ◽  
Rock Properties ◽  
Fluid Saturation ◽  
Rock Physics Modeling ◽  
Physics Modeling

Rock physics modeling aims to provide a link between rock properties, such as porosity, lithology, and fluid saturation, and elastic attributes, such as velocities or impedances. These models are then used in quantitative seismic interpretation and reservoir characterization. However, most of the geophysical measurements are uncertain; therefore, rock physics equations must be combined with mathematical tools to account for the uncertainty in the data. We combined probability theory with rock physics modeling to make predictions of elastic properties using probability distributions rather than definite values. The method provided analytical solutions of rock physics models in which the input is a random variable whose exact value is unknown but whose probability distribution is known. The probability distribution derived with this approach can be used to quantify the uncertainty in rock physics model predictions and in rock property estimation from seismic attributes. Examples of fluid substitution and rock physics modeling were studied to illustrate the application of the method.

The uncertainty in layered models from wide-angle seismic data

Geophysics ◽  
2013 ◽  
Vol 78 (3) ◽  
pp. WB31-WB36 ◽  
Author(s):  
M. Majdański
Keyword(s):  
Quantitative Estimation ◽  
A Priori ◽  
Forward Modeling ◽  
Small Error ◽  
Head Wave ◽  
Wide Angle ◽  
Simultaneous Inversion ◽  
Analytical Estimation ◽  
Modeling Strategy ◽  
Layered Models

The analytical method of estimating the uncertainty in layered models is addressed to models obtained using a layer-stripping modeling strategy or forward modeling. It is based on a simple principle of small error propagation. There are two variants of the method: a simplified one that includes refraction and vertical reflections and one that also includes wide-angle reflections. Both give a quantitative estimation for the existing models. To allow for a simple analytical estimation, refracted waves are described using a head-wave approximation in constant velocity layers; wide angle reflection paths are also simplified. In the case of trial and error forward modeling, this method can help determine how well the used parameterization is reflected in the data and avoid over-fitting the structures. This is especially important because the forward modeling is very subjective and there is no method to assess the parameterization without generating alternative models. For inversion problems using the layer-stripping method, the analysis allows for a correct propagation of errors and will help to evaluate the effect of including a priori information with known uncertainty. As a result, the layer-stripping modeling strategy is worse than simultaneous inversion for layered models because it gives larger uncertainties.

scholarly journals Prestack inversion and multiattribute analysis for porosity, shale volume, and sand probability in the Havert Formation of the Goliat field, southwest Barents Sea

2017 ◽  
Vol 5 (3) ◽  
pp. SL69-SL87 ◽  
Author(s):  
Honore Dzekamelive Yenwongfai ◽  
Nazmul Haque Mondol ◽  
Jan Inge Faleide ◽  
Isabelle Lecomte ◽  
Johan Leutscher
Keyword(s):  
Barents Sea ◽  
A Priori ◽  
Rock Physics ◽  
Lower Triassic ◽  
Effective Porosity ◽  
Distribution Functions ◽  
Simultaneous Inversion ◽  
Shale Volume ◽  
Prestack Inversion ◽  
Shaly Sand

An integrated innovative multidisciplinary approach has been used to estimate effective porosity (PHIE), shale volume ([Formula: see text]), and sand probability from prestack angle gathers and petrophysical well logs within the Lower Triassic Havert Formation in the Goliat field, Southwest Barents Sea. A rock-physics feasibility study revealed the optimum petrofacies discriminating ability of extended elastic impedance (EEI) tuned for PHIE and [Formula: see text]. We then combined model-based prestack inversion outputs from a simultaneous inversion and an EEI inversion into a multilinear attribute regression analysis to estimate absolute [Formula: see text] and PHIE seismic attributes. The quality of the [Formula: see text] and PHIE prediction is shown to increase by integrating the EEI inversion in the workflow. Probability distribution functions and a priori petrofacies proportions extracted from the well data are then applied to the [Formula: see text] and PHIE volumes to obtain clean and shaly sand probabilities. A tectonic-controlled point-source depositional model for the Havert Formation sands is then inferred from the extracted sand bodies and the seismic geomorphological character of the different attributes.

scholarly journals Reservoir characterization based upon the onset of time-lapse amplitude changes

Geophysics ◽  
2015 ◽  
Vol 80 (1) ◽  
pp. M1-M14 ◽  
Author(s):  
Donald W. Vasco ◽  
Andrey Bakulin ◽  
Hyoungsu Baek ◽  
Lane R. Johnson
Keyword(s):  
Large Scale ◽  
Reservoir Characterization ◽  
Reference Model ◽  
Rock Physics ◽  
Time Lapse ◽  
Seismic Velocities ◽  
Reservoir Properties ◽  
Fluid Saturation ◽  
Seismic Amplitudes ◽  
Physics Model

Time-lapse geophysical monitoring has potential as a tool for reservoir characterization, that is, for determining reservoir properties such as permeability. Onset times, the calendar times at which geophysical observations begin to deviate from their initial or background values, provide a useful basis for such characterization. We found that, in contrast to time-lapse amplitude changes, onset times were not sensitive to the exact method used to related changes in fluid saturation to changes in seismic velocities. As a consequence of this, we found that an inversion for effective permeability based upon onset times was robust with respect to variations in the rock-physics model. In particular, inversions of synthetic onset times calculated using Voigt and Reuss averaging techniques, but inverted using sensitivities from Hill’s averaging method, resulted in almost identical misfit reductions and similar permeability models. All solutions based on onset times recovered the large-scale, resolvable features of the reference model. Synthetic tests indicated that reliable onset times can be obtained from noisy seismic amplitudes. Testing also indicated that large-scale permeability variations can be recovered even if we used onset times from seismic surveys that were spaced as much as 300 days apart.

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