Bayesian linearized rock-physics inversion

Geophysics ◽  
2016 ◽  
Vol 81 (6) ◽  
pp. D625-D641 ◽  
Author(s):  
Dario Grana
Keyword(s):  
Granular Media ◽  
Computational Cost ◽  
Rock Physics ◽  
Real Data ◽  
Data Sets ◽  
New Approach ◽  
Amplitude Variation With Offset ◽  
Fluid Properties ◽  
Rock Physics Modeling ◽  
Physics Modeling

The estimation of rock and fluid properties from seismic attributes is an inverse problem. Rock-physics modeling provides physical relations to link elastic and petrophysical variables. Most of these models are nonlinear; therefore, the inversion generally requires complex iterative optimization algorithms to estimate the reservoir model of petrophysical properties. We have developed a new approach based on the linearization of the rock-physics forward model using first-order Taylor series approximations. The mathematical method adopted for the inversion is the Bayesian approach previously applied successfully to amplitude variation with offset linearized inversion. We developed the analytical formulation of the linearized rock-physics relations for three different models: empirical, granular media, and inclusion models, and we derived the formulation of the Bayesian rock-physics inversion under Gaussian assumptions for the prior distribution of the model. The application of the inversion to real data sets delivers accurate results. The main advantage of this method is the small computational cost due to the analytical solution given by the linearization and the Bayesian Gaussian approach.

Accuracy and effectiveness of three-dimensional controlled source electromagnetic data inversions

Geophysics ◽  
2015 ◽  
Vol 80 (2) ◽  
pp. E83-E95
Author(s):  
Richard T. Houck ◽  
Adrian Ciucivara ◽  
Scott Hornbostel
Keyword(s):  
Three Dimensional ◽  
Rock Physics ◽  
Hydrocarbon Reservoirs ◽  
Rock Properties ◽  
Data Sets ◽  
Controlled Source ◽  
Electromagnetic Data ◽  
Fluid Properties ◽  
Rock Physics Modeling ◽  
Physics Modeling

Unconstrained 3D inversion of marine controlled source electromagnetic data (CSEM) data sets produces resistivity volumes that have an uncertain relationship to the true subsurface resistivity at the scale of typical hydrocarbon reservoirs. Furthermore, CSEM-scale resistivity is an ambiguous indicator of hydrocarbon presence; not all resistivity anomalies are caused by hydrocarbon reservoirs, and not all hydrocarbon reservoirs produce a distinct resistivity anomaly. We have developed a method for quantifying the effectiveness of resistivities from CSEM inversion in detecting hydrocarbon reservoirs. Our approach uses probabilistic rock-physics modeling to update information from a preexisting prospect assessment, based on uncertain resistivities from CSEM. The result is an estimate the probability of hydrocarbon presence that accounts for uncertainty in the resistivity and in rock properties. Examples using synthetic and real CSEM data sets demonstrate that the effectiveness of CSEM inversion in identifying hydrocarbon reservoirs depends on the interaction between the uncertainty associated with the inversion-derived resistivity and the range of rock and fluid properties that were expected for the targeted prospect. Resistivity uncertainty that has a small effect on hydrocarbon probability for one set of rock property distributions may have a large effect for a different set of rock properties. Depending on the consequences of this interaction, resistivities from CSEM inversion might reduce the risk associated with predictions of hydrocarbon presence, but they cannot be expected to guarantee a specific well outcome.

Linearized AVO and poroelasticity

Geophysics ◽  
2011 ◽  
Vol 76 (3) ◽  
pp. C19-C29 ◽  
Author(s):  
Brian H. Russell ◽  
David Gray ◽  
Daniel P. Hampson
Keyword(s):  
Incidence Angle ◽  
Real Data ◽  
Data Sets ◽  
New Approach ◽  
Amplitude Variation With Offset ◽  
Fluid Properties ◽  
Seismic Amplitudes ◽  
New Equation ◽  
New Formulation

The technique of amplitude variation with offset (AVO) allows geoscientists to extract fluid and lithology information from the analysis of prestack seismic amplitudes. Various AVO parameterizations exist, all of which involve the sum of three weighted elastic-constant terms. In present-day AVO approaches, the weighting terms involve either knowledge of the incidence angle only, or knowledge of both the incidence angle and the in situ VP/VS ratio. We have used the theory of poroelasticity to derive a generalized AVO approximation that provides the estimation of fluid, rigidity, and density parameters. We have combined two previously independent AVO formulations, thus reducing, instead of adding to, the total number of formulations. This new approach requires knowledge of a third parameter to compute the weights: the dry-rock VP/VS ratio. We have derived a new equation and applied it to model and real data sets. The new formulation has allowed us to estimate fluid properties of the reservoir in a more direct manner than previous formulations.

Relative time seislet transform

Geophysics ◽  
2020 ◽  
Vol 85 (2) ◽  
pp. V223-V232 ◽  
Author(s):  
Zhicheng Geng ◽  
Xinming Wu ◽  
Sergey Fomel ◽  
Yangkang Chen
Keyword(s):  
Seismic Data ◽  
Computational Cost ◽  
Lifting Scheme ◽  
Real Data ◽  
Data Sets ◽  
Relative Time ◽  
New Approach ◽  
New Formulation ◽  
Wavelet Lifting ◽  
Seismic Images

The seislet transform uses the wavelet-lifting scheme and local slopes to analyze the seismic data. In its definition, the designing of prediction operators specifically for seismic images and data is an important issue. We have developed a new formulation of the seislet transform based on the relative time (RT) attribute. This method uses the RT volume to construct multiscale prediction operators. With the new prediction operators, the seislet transform gets accelerated because distant traces get predicted directly. We apply our method to synthetic and real data to demonstrate that the new approach reduces computational cost and obtains excellent sparse representation on test data sets.

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.

Rock-physics diagnostics of an offshore gas field

Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. MR121-MR132 ◽  
Author(s):  
Uri Wollner ◽  
Yunfei Yang ◽  
Jack P. Dvorkin
Keyword(s):  
Rock Physics ◽  
Rock Properties ◽  
Gas Field ◽  
Amplitude Variation With Offset ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
Using Data ◽  
Physics Model ◽  
Well Data ◽  
Rock Physics Model

Seismic reflections depend on the contrasts of the elastic properties of the subsurface and their 3D geometry. As a result, interpreting seismic data for petrophysical rock properties requires a theoretical rock-physics model that links the seismic response to a rock’s velocity and density. Such a model is based on controlled experiments in which the petrophysical and elastic rock properties are measured on the same samples, such as in the wellbore. Using data from three wells drilled through a clastic offshore gas reservoir, we establish a theoretical rock-physics model that quantitatively explains these data. The modeling is based on the assumption that only three minerals are present: quartz, clay, and feldspar. To have a single rock-physics transform to quantify the well data in the entire intervals under examination in all three wells, we introduced field-specific elastic moduli for the clay. We then used the model to correct the measured shear-wave velocity because it appeared to be unreasonably low. The resulting model-derived Poisson’s ratio is much smaller than the measured ratio, especially in the reservoir. The associated synthetic amplitude variation with offset response appears to be consistent with the recorded seismic angle stacks. We have shown how rock-physics modeling not only helps us to correct the well data, but also allows us to go beyond the settings represented in the wells and quantify the seismic signatures of rock properties and conditions varying in a wider range using forward seismic modeling.

A rock-physics investigation of unconsolidated saline permafrost: P-wave properties from laboratory ultrasonic measurements

Geophysics ◽  
2016 ◽  
Vol 81 (1) ◽  
pp. WA233-WA245 ◽  
Author(s):  
Shan Dou ◽  
Seiji Nakagawa ◽  
Douglas Dreger ◽  
Jonathan Ajo-Franklin
Keyword(s):  
Wave Attenuation ◽  
Rock Physics ◽  
P Wave ◽  
Data Sets ◽  
Pore Scale ◽  
Seismic Properties ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
Ice Content ◽  
Wave Properties

Saline permafrost is sensitive to thermal disturbances and is prone to subsidence, which renders it a major source of geohazard in Arctic coastal environments. Seismic methods could be used to map and monitor saline permafrost at scales of geotechnical interests because of the ice-content dependencies of seismic properties. We have developed a comprehensive study of the ultrasonic P-wave properties (i.e., velocity and attenuation) of synthetic saline permafrost samples for a range of salinities and temperatures, and measurements conducted on a fine-grained permafrost core obtained from Barrow, Alaska. The resulting data consist of P-wave properties presented as functions of temperature and salinity. Notable observations include the following: P-wave velocities showed marked reductions in the presence of dissolved salts and complex variations resulting from the water-to-ice phase transitions; strong P-wave attenuation was present in the temperature intervals in which the samples were partially frozen. When presented as functions of ice saturation, the data sets lead us to two key findings: (1) neither a purely cementing nor a purely pore-filling model of the pore-scale distributions of ice could adequately fit the observed velocity data and (2) although the velocities increase monotonically with increasing ice saturations, P-wave attenuation reaches a maximum at intermediate ice saturations—contrary to the ordinary expectation of decreasing attenuation with increasing velocities. The observed ice-content dependencies of P-wave properties, along with the implications on the probable pore-scale distributions of ice, provide a valuable basis for rock-physics modeling, which in turn could facilitate seismic characterizations of saline permafrost.

Geo-consistent depth trends: Honoring geology in siliciclastic rock-physics depth trends

2019 ◽  
Vol 38 (5) ◽  
pp. 379-384 ◽  
Author(s):  
Anders Dræge
Keyword(s):  
Seismic Response ◽  
Standard Method ◽  
Rock Physics ◽  
New Method ◽  
Temperature History ◽  
New Approach ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
Siliciclastic Rock ◽  
Well Data

A new method for modeling rock-physics depth trends called “geo-consistent depth trend modeling” is presented. No new rock-physics models are developed in this work, but existing models are put together in a new workflow. The workflow integrates rock-physics modeling with petrologic porosity models that account for burial, pressure, and temperature history. The new approach honors geologic trends, patterns, and cyclicity in the rocks. Examples based on well data are given to show how depositional trends can influence seismic response and depth trends. Geo-consistent depth trends are compared with the standard method for rock-physics depth trends, and differences are discussed. Geo-consistent depth trends can contribute to increased understanding of the subsurface and give input to risking of targets in exploration.

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