Seismic Reflections of Rock Properties in a Clastic Environment

2021 ◽  
Author(s):  
Vagif Suleymanov ◽  
Abdulhamid Almumtin ◽  
Guenther Glatz ◽  
Jack Dvorkin
Keyword(s):  
Rock Physics ◽  
Pore Fluid ◽  
Rock Properties ◽  
Petrophysical Properties ◽  
Sound Waves ◽  
Reservoir Properties ◽  
Field Guide ◽  
Physics Model ◽  
Rock Physics Model ◽  
Seismic Reflections

Abstract Generated by the propagation of sound waves, seismic reflections are essentially the reflections at the interface between various subsurface formations. Traditionally, these reflections are interpreted in a qualitative way by mapping subsurface geology without quantifying the rock properties inside the strata, namely the porosity, mineralogy, and pore fluid. This study aims to conduct the needed quantitative interpretation by the means of rock physics to establish the relation between rock elastic and petrophysical properties for reservoir characterization. We conduct rock physics diagnostics to find a theoretical rock physics model relevant to the data by examining the wireline data from a clastic depositional environment associated with a tight gas sandstone in the Continental US. First, we conduct the rock physics diagnostics by using theoretical fluid substitution to establish the relevant rock physics models. Once these models are determined, we theoretically vary the thickness of the intervals, the pore fluid, as well as the porosity and mineralogy to generate geologically plausible pseudo-scenarios. Finally, Zoeppritz (1919) equations are exploited to obtain the expected amplitude versus offset (AVO) and the gradient versus intercept curves of these scenarios. The relationship between elastic and petrophysical properties was established using forward seismic modeling. Several theoretical rock physics models, namely Raymer-Dvorkin, soft-sand, stiff-sand, and constant-cement models were applied to the wireline data under examination. The modeling assumes that only two minerals are present: quartz and clay. The appropriate rock physics model appears to be constant-cement model with a high coordination number. The result is a seismic reflection catalogue that can serve as a field guide for interpreting real seismic reflections, as well as to determine the seismic visibility of the variations in the reservoir geometry, the pore fluid, and the porosity. The obtained reservoir properties may be extrapolated to prospects away from the well control to consider certain what-if scenarios like plausible lithology or fluid variations. This enables building of a catalogue of synthetic seismic reflections of rock properties to be used by the interpreter as a field guide relating seismic data to volumetric reservoir properties.

Rock-physics transforms and scale of investigation

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. MR75-MR88 ◽  
Author(s):  
Jack Dvorkin ◽  
Uri Wollner
Keyword(s):  
Elastic Properties ◽  
Rock Physics ◽  
Clay Content ◽  
Pore Fluid ◽  
Petrophysical Properties ◽  
Reflection Seismology ◽  
Physics Model ◽  
Well Data ◽  
Rock Physics Model ◽  
Seismic Scale

Rock-physics “velocity-porosity” transforms are usually established on sets of laboratory and/or well data with the latter data source being dominant in recent practice. The purpose of establishing such transforms is to (1) conduct forward modeling of the seismic response for various geologically plausible “what if” scenarios in the subsurface and (2) interpret seismic data for petrophysical properties and conditions, such as porosity, clay content, and pore fluid. Because the scale of investigation in the well is considerably smaller than that in reflection seismology, an important question is whether the rock-physics model established in the well can be used at the seismic scale. We use synthetic examples and well data to show that a rock-physics model established at the well approximately holds at the seismic scale, suggest a reason for this scale independence, and explore where it may be violated. The same question can be addressed as an inverse problem: Assume that we have a rock-physics transform and know that it works at the scale of investigation at which the elastic properties are seismically measured. What are the upscaled (smeared) petrophysical properties and conditions that these elastic properties point to? It appears that they are approximately the arithmetically volume-averaged porosity and clay content (in a simple quartz/clay setting) and are close to the arithmetically volume-averaged bulk modulus of the pore fluid (rather than averaged saturation).

Stochastic perturbation optimization for discrete-continuous inverse problems

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. M73-M83 ◽  
Author(s):  
Leonardo Azevedo ◽  
Dario Grana ◽  
Leandro de Figueiredo
Keyword(s):  
Seismic Data ◽  
Seismic Reflection ◽  
Stochastic Perturbation ◽  
Rock Physics ◽  
Rock Properties ◽  
Petrophysical Properties ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Physics Model ◽  
Rock Physics Model

Accurate subsurface modeling and characterization require the prediction of facies and rock properties within the reservoir model. This is commonly achieved by inverting geophysical data, such as seismic reflection data, using a two-step approach either in the discrete or the continuous domain. We have adopted an iterative simultaneous method, namely, stochastic perturbation optimization, to invert seismic reflection data jointly for facies and rock properties. Facies first are simulated according to a Markov chain model, and then rock properties are generated with stochastic sequential simulation and cosimulation conditioned to each facies. Elastic and seismic data are computed by applying a rock-physics model to the realizations of petrophysical properties and a seismic convolutional model. The similarity between observed and synthetic seismic data is used to update the solution by perturbing facies and rock properties until convergence. Coupling the discrete and continuous domains ensures a consistent perturbation of the reservoir models throughout the iterations. We have evaluated the method in a 1D synthetic example for the estimation of facies and porosity from zero-offset seismic data assuming a linear rock-physics model to demonstrate the validity of the method. Then, we apply the method to a real 3D data set from the North Sea for the joint estimation of facies and petrophysical properties from prestack seismic data. The results show spatially consistent rock and fluid inverted models in which the predicted facies reproduce the vertical ordering as observed in the well data.

Multivariate probabilistic rock physics models using Kumaraswamy distributions

Geophysics ◽  
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.

scholarly journals Rock Physics Modeling Assisted Reservoir Properties Prediction: Case Study in Malay Basin

2018 ◽  
Vol 7 (3.32) ◽  
pp. 24 ◽  
Author(s):  
Amir Abbas Babasafari ◽  
Deva Ghosh ◽  
Ahmed M. A. Salim ◽  
S Y. Moussavi Alashloo
Keyword(s):  
Oil And Gas ◽  
Rock Physics ◽  
Shear Velocity ◽  
Reservoir Modeling ◽  
Reservoir Properties ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
Well Log Analysis ◽  
Physics Model ◽  
Rock Physics Model

Shear velocity log is not measured at all wells in oil and gas fields, thus rock physics modeling plays an important role to predict this type of log. Therefore, seismic pre stack inversion is performed and elastic properties are estimated more accurately. Subsequently, a robust Petro-Elastic relationship arising from rock physics model leads to far more precise prediction of petrophysical properties. The more accurate rock physics modeling results in less uncertainty of reservoir modeling. Therefore, a valid rock physics model is intended to be built. For a better understanding of reservoir properties prediction, first of all rock physics modeling for each identified litho-facies classes should be performed separately through well log analysis.  

A predictive anisotropic rock-physics model for estimating elastic rock properties of unconventional shale reservoirs

2019 ◽  
Vol 38 (5) ◽  
pp. 358-365 ◽  
Author(s):  
Colin M. Sayers ◽  
Sagnik Dasgupta
Keyword(s):  
Accurate Determination ◽  
Rock Physics ◽  
Seismic Inversion ◽  
P Wave ◽  
Rock Properties ◽  
Anisotropic Rock ◽  
Rock Classification ◽  
Shale Reservoirs ◽  
Physics Model ◽  
Rock Physics Model

This paper presents a predictive rock-physics model for unconventional shale reservoirs based on an extended Maxwell scheme. This model accounts for intrinsic anisotropy of rock matrix and heterogeneities and shape-induced anisotropy arising because the dimensions of kerogen inclusions and pores are larger parallel to the bedding plane than perpendicular to this plane. The model relates the results of seismic amplitude variation with offset inversion, such as P- and S-impedance, to the composition of the rock and enables identification of rock classes such as calcareous, argillaceous, siliceous, and mixed shales. This allows the choice of locations with the best potential for economic production of hydrocarbons. While this can be done using well data, prestack inversion of seismic P-wave data allows identification of the best locations before the wells are drilled. The results clearly show the ambiguity in rock classification obtained using poststack inversion of P-wave seismic data and demonstrate the need for prestack seismic inversion. The model provides estimates of formation anisotropy, as required for accurate determination of P- and S-impedance, and shows that anisotropy is a function not only of clay content but also other components of the rock as well as the aspect ratio of kerogen and pores. Estimates of minimum horizontal stress based on the model demonstrate the need to identify rock class and estimate anisotropy to determine the location of any stress barriers that may inhibit hydraulic fracture growth.

Rock-physics diagnostics of a turbidite oil reservoir offshore northwest Australia

Geophysics ◽  
2017 ◽  
Vol 82 (1) ◽  
pp. MR1-MR13 ◽  
Author(s):  
Humberto S. Arévalo-López ◽  
Jack P. Dvorkin
Keyword(s):  
Seismic Data ◽  
Rock Physics ◽  
Rock Properties ◽  
Oil Reservoir ◽  
Controlled Experiments ◽  
Seismic Modeling ◽  
Physics Model ◽  
Rock Physics Model ◽  
Offshore Oil ◽  
Northwest Australia

Interpreting seismic data for petrophysical rock properties requires a rock-physics model that links the petrophysical rock properties to the elastic properties, such as velocity and impedance. Such a model can only be established from controlled experiments in which both groups of rock properties are measured on the same samples. A prolific source of such data is wellbore measurements. We use data from four wells drilled through a clastic offshore oil reservoir to perform rock-physics diagnostics, i.e., to find a theoretical rock-physics model that quantitatively explains the measurements. Using the model, we correct questionable well curves. Moreover, a crucial purpose of rock-physics diagnostics is to go beyond the settings represented in the wells and understand the seismic signatures of rock properties varying in a wider range via forward seismic modeling. With this goal in mind, we use our model to generate synthetic seismic gathers from perturbational modeling to address “what-if” scenarios not present in the wells.

Seismic reflections of gas hydrate from perturbational forward modeling

Geophysics ◽  
2006 ◽  
Vol 71 (6) ◽  
pp. F165-F171 ◽  
Author(s):  
Ingrid Cordon ◽  
Jack Dvorkin ◽  
Gary Mavko
Keyword(s):  
Reservoir Characterization ◽  
Rock Physics ◽  
Clay Content ◽  
The Arctic ◽  
Seismic Amplitude ◽  
Hydrate Reservoir ◽  
Hydrate Saturation ◽  
Physics Model ◽  
Rock Physics Model ◽  
Seismic Reflections

We perturb the elastic properties and attenuation in the Arctic Mallik methane-hydrate reservoir to produce a set of plausible seismic signatures away from the existing well. These perturbations are driven by the changes we impose on porosity, clay content, hydrate saturation, and geometry. The key is a data-guided, theoretical, rock-physics model that we adopt to link velocity and attenuation to porosity, mineralogy, and amount of hydrate. We find that the seismic amplitude is very sensitive to the hydrate saturation in the host sand and its porosity as well as the porosity of the overburden shale. However, changes to the amount of clay in the sand only weakly alter the amplitude. Attenuation, which may be substantial, must be taken into account during hydrate reservoir characterization because it lowers the amplitude to an extent that may affect the hydrate-volume prediction. The spatial structure of the reservoir affects the seismic reflection: A thinly-layered reservoir produces a noticeably different amplitude than a massive reservoir with the same hydrate volume.

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