scholarly journals Correlations of Rock-Physics Model Parameters From Bayesian Analysis: Pressure- and Porosity-Dependent Models Applied to Unconsolidated Sands

2022 ◽  
Vol 9 ◽  
Author(s):  
Kyle T. Spikes ◽  
Mrinal K. Sen
Keyword(s):  
Reservoir Characterization ◽  
Rock Physics ◽  
Model Parameters ◽  
Data Sets ◽  
Unconsolidated Sands ◽  
Bayesian Formulation ◽  
Input Parameters ◽  
Physics Model ◽  
Rock Physics Model ◽  
Prior Models

Correlations of rock-physics model inputs are important to know to help design informative prior models within integrated reservoir-characterization workflows. A Bayesian framework is optimal to determine such correlations. Within that framework, we use velocity and porosity measurements on unconsolidated, dry, and clean sands. Three pressure- and three porosity-dependent rock-physics models are applied to the data to examine relationships among the inputs. As with any Bayesian formulation, we define a prior model and calculate the likelihood in order to evaluate the posterior. With relatively few inputs to consider for each rock-physics model, we found that sampling the posterior exhaustively to be convenient. The results of the Bayesian analyses are multivariate histograms that indicate most likely values of the input parameters given the data to which the rock-physics model was fit. When the Bayesian procedure is repeated many times for the same data, but with different prior models, correlations emerged among the input parameters in a rock-physics model. These correlations were not known previously. Implications, for the pressure- and porosity-dependent models examined here, are that these correlations should be utilized when applying these models to other relevant data sets. Furthermore, additional rock-physics models should be examined similarly to determine any potential correlations in their inputs. These correlations can then be taken advantage of in forward and inverse problems posed in reservoir characterization.

scholarly journals Bayesian Analysis to Determine Relative Significance of Inputs of a Rock-Physics Model

2021 ◽  
Vol 9 ◽  
Author(s):  
Kyle T. Spikes ◽  
Mrinal K. Sen
Keyword(s):  
Bayesian Analysis ◽  
Reservoir Characterization ◽  
Likelihood Function ◽  
Rock Physics ◽  
Rock Properties ◽  
Model Parameters ◽  
Prior Distributions ◽  
Relative Significance ◽  
Physics Model ◽  
Rock Physics Model

Rock-physics models relate rock properties to elastic properties through non-unique relationships and often in the presence of seismic data that contain significant noise. A set of inputs define the rock-physics model, and any errors in that model map directly into uncertainty in target seismic-scale amplitudes, velocities, or inverted impedances. An important aspect of using rock-physics models in this manner is to determine and understand the significance of the inputs into a rock-physics model under consideration. Such analysis enables the design of prior distributions that are informative within a reservoir-characterization formulation. We use the framework of Bayesian analysis to find internal dependencies and correlations among the inputs. This process requires the assignments of prior distributions, and calculation of the likelihood function, whose product is the posterior distribution. The data are well-log data that come from a hydrocarbon-bearing set of sands from the Gulf of Mexico. The rock-physics model selected is the soft-sand model, which is applicable to the data from the reservoir sands. Results from the Bayesian algorithm are multivariate histograms that demonstrate the most frequent values of the inputs given the data. Four analyses are applied to different subsets of the reservoir sands, and each reveals some correlations among certain model inputs. This quantitative approach points out the significance of a singular or joint set of rock-physics model parameters.

Elastic mineral facies: Selecting site-specific elastic moduli of clay

Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. MR111-MR119
Author(s):  
Uri Wollner ◽  
Jack P. Dvorkin
Keyword(s):  
Elastic Moduli ◽  
Rock Physics ◽  
Data Sets ◽  
Depth Range ◽  
Chemical Formula ◽  
Rock Matrix ◽  
Well Control ◽  
Physics Model ◽  
Well Data ◽  
Rock Physics Model

The elastic moduli of the mineral constituents of the rock matrix are among the principal inputs in all rock-physics velocity-porosity-mineralogy models. Published experimental data indicate that the elastic moduli for essentially any mineral vary. The ranges of these variations are especially wide for clay. The question addressed here is how to select, based on well data, concrete values for clay’s elastic constants where those for other minerals are fixed. The approach is to find a rock-physics model for zero-clay intervals and then adjust the clay’s constants to describe the intervals dominated by clay using the same model. We examine three data sets from clastic environments, each represented by three wells, where the selected constants for clay were different between the fields but stable within each field. These constants can then be used for seismic forward modeling and interpretation in a specific field away from well control and within a depth range represented in the wells. In essence, we introduce the concept of elastic mineral facies where we identify clay as a mineral with certain elastic moduli rather than by its chemical formula.

Facies — The drivers for modern inversions

2020 ◽  
Vol 39 (2) ◽  
pp. 102-109
Author(s):  
John Pendrel ◽  
Henk Schouten
Keyword(s):  
Bayesian Analysis ◽  
Prior Information ◽  
Reservoir Characterization ◽  
Low Frequency ◽  
Rock Physics ◽  
Well Log ◽  
Data Set ◽  
Prior Probabilities ◽  
Physics Model ◽  
Rock Physics Model

It is common practice to make facies estimations from the outcomes of seismic inversions and their derivatives. Bayesian analysis methods are a popular approach to this. Facies are important indicators of hydrocarbon deposition and geologic processes. They are critical to geoscientists and engineers. The application of Bayes’ rule maps prior probabilities to posterior probabilities when given new evidence from observations. Per-facies elastic probability density functions (ePDFs) are constructed from elastic-log and rock-physics model crossplots, over which inversion results are superimposed. The ePDFs are templates for Bayesian analysis. In the context of reservoir characterization, the new information comes from seismic inversions. The results are volumes of the probabilities of occurrences of each of the facies at all points in 3D space. The concepts of Bayesian inference have been applied to the task of building low-frequency models for seismic inversions without well-log interpolation. Both a constant structurally compliant elastic trend approach and a facies-driven method, where models are constructed from per-facies trends and initial facies estimates, have been tested. The workflows make use of complete 3D prior information and measure and account for biases and uncertainties in the inversions and prior information. Proper accounting for these types of effects ensures that rock-physics models and inversion data prepared for reservoir property analysis are consistent. The effectiveness of these workflows has been demonstrated by using a Gulf of Mexico data set. We have shown how facies estimates can be effectively used to build reasonable low-frequency models for inversion, which obviate the need for well-log interpolation and provide full 3D variability. The results are more accurate probability-based net-pay estimates that correspond better to geology. We evaluate the workflows by using several measures including precision, confidence, and probabilistic net pay.

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.

scholarly journals Seismic characterization of geothermal sedimentary reservoirs: A field example from the Copenhagen area, Denmark

2020 ◽  
Vol 8 (2) ◽  
pp. T275-T291 ◽  
Author(s):  
Kenneth Bredesen ◽  
Esben Dalgaard ◽  
Anders Mathiesen ◽  
Rasmus Rasmussen ◽  
Niels Balling
Keyword(s):  
Reservoir Characterization ◽  
Energy Resource ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Amplitude Variation With Offset ◽  
Seismic Characterization ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
Physics Model ◽  
Rock Physics Model

We have seismically characterized a Triassic-Jurassic deep geothermal sandstone reservoir north of Copenhagen, onshore Denmark. A suite of regional geophysical measurements, including prestack seismic data and well logs, was integrated with geologic information to obtain facies and reservoir property predictions in a Bayesian framework. The applied workflow combined a facies-dependent calibrated rock-physics model with a simultaneous amplitude-variation-with-offset seismic inversion. The results suggest that certain sandstone distributions are potential aquifers within the target interval, which appear reasonable based on the geologic properties. However, prediction accuracy suffers from a restricted data foundation and should, therefore, only be considered as an indicator of potential aquifers. Despite these issues, the results demonstrate new possibilities for future seismic reservoir characterization and rock-physics modeling for exploration purposes, derisking, and the exploitation of geothermal energy as a green and sustainable energy resource.

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

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