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.

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.

scholarly journals Probabilistic approach to rock physics modeling

Geophysics ◽  
2014 ◽  
Vol 79 (2) ◽  
pp. D123-D143 ◽  
Author(s):  
Dario Grana
Keyword(s):  
Probability Distribution ◽  
Reservoir Characterization ◽  
Probability Distributions ◽  
Probabilistic Approach ◽  
Rock Physics ◽  
Random Variable ◽  
Rock Properties ◽  
Fluid Saturation ◽  
Rock Physics Modeling ◽  
Physics Modeling

Rock physics modeling aims to provide a link between rock properties, such as porosity, lithology, and fluid saturation, and elastic attributes, such as velocities or impedances. These models are then used in quantitative seismic interpretation and reservoir characterization. However, most of the geophysical measurements are uncertain; therefore, rock physics equations must be combined with mathematical tools to account for the uncertainty in the data. We combined probability theory with rock physics modeling to make predictions of elastic properties using probability distributions rather than definite values. The method provided analytical solutions of rock physics models in which the input is a random variable whose exact value is unknown but whose probability distribution is known. The probability distribution derived with this approach can be used to quantify the uncertainty in rock physics model predictions and in rock property estimation from seismic attributes. Examples of fluid substitution and rock physics modeling were studied to illustrate the application of the method.

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.

Integrated VTI model building with seismic data, geologic information, and rock-physics modeling — Part 1: Theory and synthetic test

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. C177-C191 ◽  
Author(s):  
Yunyue Li ◽  
Biondo Biondi ◽  
Robert Clapp ◽  
Dave Nichols
Keyword(s):  
Seismic Data ◽  
Seismic Anisotropy ◽  
Model Building ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Additional Information ◽  
Synthetic Test ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
Geologic Information

Seismic anisotropy plays an important role in structural imaging and lithologic interpretation. However, anisotropic model building is a challenging underdetermined inverse problem. It is well-understood that single component pressure wave seismic data recorded on the upper surface are insufficient to resolve a unique solution for velocity and anisotropy parameters. To overcome the limitations of seismic data, we have developed an integrated model building scheme based on Bayesian inference to consider seismic data, geologic information, and rock-physics knowledge simultaneously. We have performed the prestack seismic inversion using wave-equation migration velocity analysis (WEMVA) for vertical transverse isotropic (VTI) models. This image-space method enabled automatic geologic interpretation. We have integrated the geologic information as spatial model correlations, applied on each parameter individually. We integrate the rock-physics information as lithologic model correlations, bringing additional information, so that the parameters weakly constrained by seismic are updated as well as the strongly constrained parameters. The constraints provided by the additional information help the inversion converge faster, mitigate the ambiguities among the parameters, and yield VTI models that were consistent with the underlying geologic and lithologic assumptions. We have developed the theoretical framework for the proposed integrated WEMVA for VTI models and determined the added information contained in the regularization terms, especially the rock-physics constraints.

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