Tight carbonate reservoir characterization based on the modified rock physics model

2018 ◽  
Vol 159 ◽  
pp. 374-385 ◽  
Author(s):  
Bonan Li ◽  
Hui Shen ◽  
Shouli Qu ◽  
Ding Wang ◽  
Cai Liu
Keyword(s):  
Reservoir Characterization ◽  
Rock Physics ◽  
Carbonate Reservoir ◽  
Tight Carbonate ◽  
Physics Model ◽  
Rock Physics Model ◽  
Carbonate Reservoir Characterization

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 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.

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