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.

Estimating the amount of gas hydrate and free gas from marine seismic data

Geophysics ◽  
2000 ◽  
Vol 65 (2) ◽  
pp. 565-573 ◽  
Author(s):  
Christine Ecker ◽  
Jack Dvorkin ◽  
Amos M. Nur
Keyword(s):  
Gas Hydrate ◽  
Seismic Data ◽  
Rock Physics ◽  
Free Gas ◽  
Interval Velocity ◽  
Blake Ridge ◽  
Hydrate Saturation ◽  
Physics Model ◽  
Rock Physics Model ◽  
Marine Seismic

Marine seismic data and well‐log measurements at the Blake Ridge offshore South Carolina show that prominent seismic bottom‐simulating reflectors (BSRs) are caused by sediment layers with gas hydrate overlying sediments with free gas. We apply a theoretical rock‐physics model to 2-D Blake Ridge marine seismic data to determine gas‐hydrate and free‐gas saturation. High‐porosity marine sediment is modeled as a granular system where the elastic wave velocities are linked to porosity; effective pressure; mineralogy; elastic properties of the pore‐filling material; and water, gas, and gas‐hydrate saturation of the pore space. To apply this model to seismic data, we first obtain interval velocity using stacking velocity analysis. Next, all input parameters to the rock‐physics model, except porosity and water, gas, and gas hydrate saturation, are estimated from geologic information. To estimate porosity and saturation from interval velocity, we first assume that the entire sediment does not contain gas hydrate or free gas. Then we use the rock‐physics model to calculate porosity directly from the interval velocity. Such porosity profiles appear to have anomalies where gas hydrate and free gas are present (as compared to typical profiles expected and obtained in sediment without gas hydrate or gas). Porosity is underestimated in the hydrate region and is overestimated in the free‐gas region. We calculate the porosity residuals by subtracting a typical porosity profile (without gas hydrate and gas) from that with anomalies. Next we use the rock‐physics model to eliminate these anomalies by introducing gas‐hydrate or gas saturation. As a result, we obtain the desired 2-D saturation map. The maximum gas‐hydrate saturation thus obtained is between 13% and 18% of the pore space (depending on the version of the model used). These saturation values are consistent with those measured in the Blake Ridge wells (away from the seismic line), which are about 12%. Free‐gas saturation varies between 1% and 2%. The saturation estimates are extremely sensitive to the input velocity values. Therefore, accurate velocity determination is crucial for correct 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 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.

scholarly journals Rock Physics Model and Seismic Dispersion and Attenuation in Gas Hydrate-Bearing Sediments

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhiqi Guo ◽  
Xueying Wang ◽  
Jian Jiao ◽  
Haifeng Chen
Keyword(s):  
Gas Hydrate ◽  
Rock Physics ◽  
P Wave ◽  
Pore Filling ◽  
P Wave Velocity ◽  
Hydrate Saturation ◽  
Physics Model ◽  
Rock Physics Model ◽  
Hydrate Bearing Sediments ◽  
Dispersion And Attenuation

A rock physics model was established to calculate the P-wave velocity dispersion and attenuation caused by the squirt flow of fluids in gas hydrate-bearing sediments. The critical hydrate saturation parameter was introduced to describe different ways of hydrate concentration, including the mode of pore filling and the co-existence mode of pore filling and particle cementation. Rock physical modeling results indicate that the P-wave velocity is insensitive to the increase in gas hydrate saturation for the mode of pore filling, while it increases rapidly with increasing gas hydrate saturation for the co-existence mode of pore filling and particle cementation. Meanwhile, seismic modeling results show that both the PP and mode-converted PS reflections are insensitive to the gas hydrate saturation that is lower than the critical value, while they tend to change obviously for the hydrate saturation that is higher than the critical value. These can be interpreted that only when gas hydrate begins to be part of solid matrix at high gas hydrate saturation, it represents observable impact on elastic properties of the gas hydrate-bearing sediments. Synthetic seismograms are calculated for a 2D heterogeneous model where the gas hydrate saturation varies vertically and layer thickness of the gas hydrate-bearing sediment varies laterally. Modeling results show that larger thickness of the gas hydrate-bearing layer generally corresponds to stronger reflection amplitudes from the bottom simulating reflector.

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 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.

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