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1
Amato del Monte, Alessandro. "Seismic rock physics." Leading Edge 36, no. 6 (June 2017): 523–25. http://dx.doi.org/10.1190/tle36060523.1.
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Rock physics studies the relationship between physical and elastic properties of rocks and is the basis of quantitative seismic interpretation. It has arguably given exploration geophysicists a solid quantitative basis to their interpretation of seismic data. It can be tackled at different scales of investigation by people with various backgrounds — from geophysicists to civil engineers, from mathematicians to petrophysicists.
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Avseth, Per, and Tor Veggeland. "Seismic screening of rock stiffness and fluid softening using rock-physics attributes." Interpretation 3, no. 4 (November 1, 2015): SAE85—SAE93. http://dx.doi.org/10.1190/int-2015-0054.1.
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We have developed a methodology to create easy-to-implement rock-physics attributes that can be used to screen for reservoir sandstones and hydrocarbon pore fill from seismic inversion data. Most seismic attributes are based on the empirical relationships between reservoir properties and seismic observables. We have honored the physical properties of the rocks by defining attributes that complied with calibrated rock-physics models. These attributes included the fluid saturation sensitive curved pseudo-elastic impedance (CPEI) and the rock stiffness/lithology attribute pseudo-elastic impedance for lithology (PEIL). We found that the CPEI attribute correlated nicely with saturation and resistivity, whereas the PEIL attribute in practice was a scaled version of the shear modulus and correlated nicely with porosity. We determined the use of these attributes on well log and seismic inversion data from the Norwegian Sea, and we successfully screened out reservoir rocks filled with either water or hydrocarbons.
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Yale, David P. "Recent advances in rock physics." GEOPHYSICS 50, no. 12 (December 1985): 2480–91. http://dx.doi.org/10.1190/1.1441879.
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The need to extract more information about the subsurface from geophysical and petrophysical measurements has led to a great interest in the study of the effect of rock and fluid properties on geophysical and petrophysical measurements. Rock physics research in the last few years has been concerned with studying the effect of lithology, fluids, pore geometry, and fractures on velocity; the mechanisms of attenuation of seismic waves; the effect of anisotropy; and the electrical and dielectric properties of rocks. Understanding the interrelationships between rock properties and their expression in geophysical and petrophysical data is necessary to integrate geophysical, petrophysical, and engineering data for the enhanced exploration and characterization of petroleum reservoirs. The use of amplitude offsets, S‐wave seismic data, and full‐waveform sonic data will help in the discrimination of lithology. The effect of in situ temperatures and pressures must be taken into account, especially in fractured and unconsolidated reservoirs. Fluids have a strong effect on seismic velocities, through their compressibility, density, and chemical effects on grain and clay surfaces. S‐wave measurements should help in bright spot analysis for gas reservoirs, but theoretical considerations still show that a deep, consolidated reservoir will not have any appreciable impedance contrast due to gas. The attenuation of seismic waves has received a great deal of attention recently. The idea that Q is independent of frequency has been challenged by experimental and theoretical findings of large peaks in attenuation in the low kHz and hundreds of kHz regions. The attenuation is thought to be due to fluid‐flow mechanisms and theories suggest that there may be large attenuation due to small amounts of gas in the pore space even at seismic frequencies. Models of the effect of pores, cracks, and fractures on seismic velocity have also been studied. The thin‐crack velocity models appear to be better suited for representing fractures than pores. The anisotropy of seismic waves, especially the splitting of polarized S‐waves, may be diagnostic of sets of oriented fractures in the crust. The electrical properties of rocks are strongly dependent upon the frequency of the energy and logging is presently being done at various frequencies. The effects of frequency, fluid salinity, clays, and pore‐grain geometry on electrical properties have been studied. Models of porous media have been used extensively to study the electrical and elastic properties of rocks. There has been great interest in extracting geometrical parameters about the rock and pore space directly from microscopic observation. Other models have focused on modeling several different properties to find relationships between rock properties.
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Grana, Dario, and Ernesto Della Rossa. "Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion." GEOPHYSICS 75, no. 3 (May 2010): O21—O37. http://dx.doi.org/10.1190/1.3386676.
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A joint estimation of petrophysical properties is proposed that combines statistical rock physics and Bayesian seismic inversion. Because elastic attributes are correlated with petrophysical variables (effective porosity, clay content, and water saturation) and this physical link is associated with uncertainties, the petrophysical-properties estimation from seismic data can be seen as a Bayesian inversion problem. The purpose of this work was to develop a strategy for estimating the probability distributions of petrophysical parameters and litho-fluid classes from seismics. Estimation of reservoir properties and the associated uncertainty was performed in three steps: linearized seismic inversion to estimate the probabilities of elastic parameters, probabilistic upscaling to include the scale-changes effect, and petrophysical inversion to estimate the probabilities of petrophysical variables andlitho-fluid classes. Rock-physics equations provide the linkbetween reservoir properties and velocities, and linearized seismic modeling connects velocities and density to seismic amplitude. A full Bayesian approach was adopted to propagate uncertainty from seismics to petrophysics in an integrated framework that takes into account different sources of uncertainty: heterogeneity of the real data, approximation of physical models, measurement errors, and scale changes. The method has been tested, as a feasibility step, on real well data and synthetic seismic data to show reliable propagation of the uncertainty through the three different steps and to compare two statistical approaches: parametric and nonparametric. Application to a real reservoir study (including data from two wells and partially stacked seismic volumes) has provided as a main result the probability densities of petrophysical properties and litho-fluid classes. It demonstrated the applicability of the proposed inversion method.
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Das, Agnibha, and Madhumita Sengupta. "Introduction to this special section: Rock physics." Leading Edge 40, no. 9 (September 2021): 644. http://dx.doi.org/10.1190/tle40090644.1.
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In simple terms, rock physics provides the much-needed link between measurable elastic properties of rocks and their intrinsic properties. This enables us to connect seismic data, well logs, and laboratory measurements to minerology, porosity, permeability, fluid saturations, and stress. Rock-physics relationships/models are used to understand seismic signatures in terms of reservoir properties that help in exploration risk mitigation. Traditionally, rock physics has played an irreplaceable role in amplitude variation with offset (AVO) modeling and inversion, 3D/4D close-the-loop studies, and seismic time-lapse analysis and interpretation. Today, rock-physics research and application have influenced a much wider space that spans digital rock physics, microseismic, and distributed acoustic sensing (DAS) data analysis. In this special section, we have included papers that cover much of these advanced methods, providing us with a better understanding of subsurface elastic and transport properties, thereby reducing bias and uncertainties in quantitative interpretation.
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Li, Yunyue, Biondo Biondi, Robert Clapp, and Dave Nichols. "Integrated VTI model building with seismic data, geologic information, and rock-physics modeling — Part 2: Field data test." GEOPHYSICS 81, no. 5 (September 2016): C205—C218. http://dx.doi.org/10.1190/geo2015-0593.1.
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Velocity model building is the first step of seismic inversion and the foundation of the subsequent processing and interpretation workflow. Velocity model building from surface seismic data only becomes severely underdetermined and nonunique when more than one parameter is needed to characterize the velocity anisotropy. The traditional seismic processing workflow sequentially performs seismic velocity model building, structural imaging/interpretation, and lithologic inversion, modifying the subsurface model in each step without verifications against the previously used data. We have developed an integrated model building scheme that uses all available information: seismic data, geologic structural information, well logs, and rock-physics knowledge. We have evaluated the accuracy of the anisotropic model in the image space, in which structural information is estimated. The lithologic inversion results from well logs and the dynamic seismic information (amplitude versus angle) are also fed back to the kinematic seismic inversion via a cross-parameter covariance matrix, which is a multivariate Gaussian approximation to the numerical distribution modeled from stochastic rock-physics modeling. The procedure of building the rock-physics prior information and the improvements using these extra constraints were tested on a Gulf of Mexico data set. The inverted vertical transverse isotropic model not only better focused the seismic image, but it also satisfied the geologic and rock-physics principles.
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Chen, Jinsong, and G. Michael Hoversten. "Joint inversion of marine seismic AVA and CSEM data using statistical rock-physics models and Markov random fields." GEOPHYSICS 77, no. 1 (January 2012): R65—R80. http://dx.doi.org/10.1190/geo2011-0219.1.
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Joint inversion of seismic AVA and CSEM data requires rock-physics relationships to link seismic attributes to electric properties. Ideally, we can connect them through reservoir parameters (e.g., porosity and water saturation) by developing physical-based models, such as Gassmann’s equations and Archie’s law, using nearby borehole logs. This could be difficult in the exploration stage because information available is typically insufficient for choosing suitable rock-physics models and for subsequently obtaining reliable estimates of the associated parameters. The use of improper rock-physics models and the inaccuracy of the estimates of model parameters may cause misleading inversion results. Conversely, it is easy to derive statistical relationships among seismic and electric attributes and reservoir parameters from distant borehole logs. In this study, we developed a Bayesian model to jointly invert seismic AVA and CSEM data for reservoir parameters using statistical rock-physics models; the spatial dependence of geophysical and reservoir parameters were carried out by lithotypes through Markov random fields. We applied the developed model to a synthetic case that simulates a CO2 monitoring application. We derived statistical rock-physics relations from borehole logs at one location and estimated seismic P- and S-wave velocity ratio, acoustic impedance, density, electric resistivity, lithotypes, porosity, and water saturation at three different locations by conditioning to seismic AVA and CSEM data. Comparison of the inversion results with their corresponding true values showed that the correlation-based statistical rock-physics models provide significant information for improving the joint inversion results.
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Shen, Yi, Jack Dvorkin, and Yunyue Li. "Improving seismic QP estimation using rock-physics constraints." GEOPHYSICS 83, no. 3 (May 1, 2018): MR187—MR198. http://dx.doi.org/10.1190/geo2016-0665.1.
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Our goal is to accurately estimate attenuation from seismic data using model regularization in the seismic inversion workflow. One way to achieve this goal is by finding an analytical relation linking [Formula: see text] to [Formula: see text]. We derive an approximate closed-form solution relating [Formula: see text] to [Formula: see text] using rock-physics modeling. This relation is tested on well data from a clean clastic gas reservoir, of which the [Formula: see text] values are computed from the log data. Next, we create a 2D synthetic gas-reservoir section populated with [Formula: see text] and [Formula: see text] and generate respective synthetic seismograms. Now, the goal is to invert this synthetic seismic section for [Formula: see text]. If we use standard seismic inversion based solely on seismic data, the inverted attenuation model has low resolution and incorrect positioning, and it is distorted. However, adding our relation between velocity and attenuation, we obtain an attenuation model very close to the original section. This method is tested on a 2D field seismic data set from Gulf of Mexico. The resulting [Formula: see text] model matches the geologic shape of an absorption body interpreted from the seismic section. Using this [Formula: see text] model in seismic migration, we make the seismic events below the high-absorption layer clearly visible, with improved frequency content and coherency of the events.
9
Yan, Fuyong, De-Hua Han, and Qiuliang Yao. "Rock-physics constrained seismic anisotropy parameter estimation." GEOPHYSICS 86, no. 4 (July 1, 2021): MR247—MR253. http://dx.doi.org/10.1190/geo2019-0153.1.
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Compared with isotropic media, at least two extra parameters are involved in common P-wave seismic data processing and interpretation for transversely isotropic media. Previous synthetic model testing has shown that it is challenging to estimate anisotropy parameters even using extremely low noise level seismic data from a simple geologic setting. Although theoretically independent, anisotropy parameters are not free variables for organic-rich mudrocks whose elastic properties are often approximated by transverse isotropy. One potential approach to improve the accuracy in the estimated anisotropy parameters is to consider the physical relationships between them during the inversion process. To test this proposition, we first modify a commonly used nonhyperbolic reflection moveout equation as a function of the interval anisotropy velocities so that rock-physics constraints could be effectively applied to each layer. The rock-physics constraints are established from data analysis of selected laboratory anisotropy measurement data. The laboratory data are then used to parameterize hundreds of 15-layer transverse isotropy models using a Monte Carlo simulation. The synthetic model testing indicates that the accuracy of the estimated anisotropy parameters can be improved if the relationships between the anisotropy parameters are considered during the inversion process.
10
Wollner, Uri, Yunfei Yang, and Jack P. Dvorkin. "Rock-physics diagnostics of an offshore gas field." GEOPHYSICS 82, no. 4 (July 1, 2017): MR121—MR132. http://dx.doi.org/10.1190/geo2016-0390.1.
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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.
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Handoyo, Handoyo, M. Rizki Sudarsana, and Restu Almiati. "Rock Physics Modeling and Seismic Interpretation to Estimate Shally Cemented Zone in Carbonate Reservoir Rock." Journal of Geoscience, Engineering, Environment, and Technology 1, no. 1 (December 1, 2016): 45. http://dx.doi.org/10.24273/jgeet.2016.11.6.
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Carbonate rock are important hydrocarbon reservoir rocks with complex texture and petrophysical properties (porosity and permeability). These complexities make the prediction reservoir characteristics (e.g. porosity and permeability) from their seismic properties more difficult. The goal of this paper are to understanding the relationship of physical properties and to see the signature carbonate initial rock and shally-carbonate rock from the reservoir.
To understand the relationship between the seismic, petrophysical and geological properties, we used rock physics modeling from ultrasonic P- and S- wave velocity that measured from log data. The measurements obtained from carbonate reservoir field (gas production). X-ray diffraction and scanning electron microscope studies shown the reservoir rock are contain wackestone-packstone content. Effective medium theory to rock physics modeling are using Voigt, Reuss, and Hill.
It is shown the elastic moduly proposionally decrease with increasing porosity. Elastic properties and wave velocity are decreasing proporsionally with increasing porosity and shally cemented on the carbonate rock give higher elastic properties than initial carbonate non-cemented. Rock physics modeling can separated zones which rich of shale and less of shale.
12
Allo, Fabien. "Consolidating rock-physics classics: A practical take on granular effective medium models." Leading Edge 38, no. 5 (May 2019): 334–40. http://dx.doi.org/10.1190/tle38050334.1.
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Granular effective medium (GEM) models rely on the physics of a random packing of spheres. Although the relative simplicity of these models contrasts with the complex texture of most grain-based sedimentary rocks, their analytical form makes them easier to apply than numerical models designed to simulate more complex rock structures. Also, unlike empirical models, they do not rely on data acquired under specific physical conditions and can therefore be used to extrapolate beyond available observations. In addition to these practical considerations, the appeal of GEM models lies in their parameterization, which is suited for a quantitative description of the rock texture. As a result, they have significantly helped promote the use of rock physics in the context of seismic exploration for hydrocarbon resources by providing geoscientists with tools to infer rock composition and microstructure from sonic velocities. Over the years, several classic GEM models have emerged to address modeling needs for different rock types such as unconsolidated, cemented, and clay-rich sandstones. We describe how these rock-physics models, pivotal links between geology and seismic data, can be combined into extended models through the introduction of a few additional parameters (matrix stiffness index, cement cohesion coefficient, contact-cement fraction, and laminated clays fraction), each associated with a compositional or textural property of the rock. A variety of real data sets are used to illustrate how these parameters expand the realm of seismic rock-physics diagnostics by increasing the versatility of the extended models and facilitating the simulation of plausible geologic variations away from the wells.
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Spikes, Kyle, Tapan Mukerji, Jack Dvorkin, and Gary Mavko. "Probabilistic seismic inversion based on rock-physics models." GEOPHYSICS 72, no. 5 (September 2007): R87—R97. http://dx.doi.org/10.1190/1.2760162.
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A site-specific rock-physics transform from porosity, mineralogy, and pore fluid to elastic-wave velocities is used to invert seismic amplitude data for clay content, total porosity, and saturation. The implementation is Bayesian and produces probabilistic values of the reservoir properties from seismic measurements and well data. This method focuses on an exploration setting where minimal data exist. Two key assumptions reduce the problem and keep the prior information as noncommittal as possible. First, a prior interpretation of the seismic data is required that provides a geobody on which to perform the inversion. Second, the reservoir thickness is assumed to be constant, as are the rock properties within the reservoir. The prior distributions of the reservoir properties are assumed to be uncorrelated and independent, although this is not an essential assumption. Central to theinversion is the generation of a complete set of earth models derived from the prior distribution. A site-specific rock-physics model translates these properties (clay content, porosity, and saturation) into the elastic domain. A complete set of forward seismic models accompanies the earth models, and these seismic models are compared to the real data on a trace-by-trace basis. The reservoir properties corresponding to the seismic models that match the real data within predefined errors are used to construct the posterior. This method was tested on well and seismic data from offshore western South Africa. Initial results at calibration and test wells indicate an overprediction of porosity and uncertain predictions of clay content and saturation. This is a result of the constant-thickness assumption. However, a highly negative correlation between porosity and thickness is predicted, which manifests the success of this method.
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Johansen, Tor Arne, Erling Hugo Jensen, Gary Mavko, and Jack Dvorkin. "Inverse rock physics modeling for reservoir quality prediction." GEOPHYSICS 78, no. 2 (March 1, 2013): M1—M18. http://dx.doi.org/10.1190/geo2012-0215.1.
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Seismic reservoir characterization requires a transform of seismically derived properties such as P- and S-wave velocities, acoustic impedances, elastic impedances, or other seismic attributes into parameters describing lithology and reservoir conditions. A large number of different rock physics models have been developed to obtain this link. Their relevance is, however, constrained by the type of lithology, porosity range, textural complexity, saturation conditions, and the dynamics of the pore fluid. Because the number of rock physics parameters is often higher than the number of seismic parameters, this is known to be an underdetermined problem with nonunique solutions. We have studied the framework of inverse rock physics modeling which aims at direct quantitative prediction of lithology and reservoir quality from seismic parameters, but where nonuniqueness and data error propagation are also handled. The procedure is based on a numerical reformulation of rock physics models so that the seismic parameters are input and the reservoir quality data are output. The modeling procedure can be used to evaluate the validity of various rock physics models for a given data set. Furthermore, it provides the most robust data parameter combinations to use for either porosity, lithology, and pore fluid prediction, whenever a specific rock physics model has been selected for this cause.
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Arévalo-López, Humberto S., and Jack P. Dvorkin. "Rock-physics diagnostics of a turbidite oil reservoir offshore northwest Australia." GEOPHYSICS 82, no. 1 (January 1, 2017): MR1—MR13. http://dx.doi.org/10.1190/geo2016-0083.1.
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Interpreting seismic data for petrophysical rock properties requires a rock-physics model that links the petrophysical rock properties to the elastic properties, such as velocity and impedance. Such a model can only be established from controlled experiments in which both groups of rock properties are measured on the same samples. A prolific source of such data is wellbore measurements. We use data from four wells drilled through a clastic offshore oil reservoir to perform rock-physics diagnostics, i.e., to find a theoretical rock-physics model that quantitatively explains the measurements. Using the model, we correct questionable well curves. Moreover, a crucial purpose of rock-physics diagnostics is to go beyond the settings represented in the wells and understand the seismic signatures of rock properties varying in a wider range via forward seismic modeling. With this goal in mind, we use our model to generate synthetic seismic gathers from perturbational modeling to address “what-if” scenarios not present in the wells.
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Shen, Yi, Kui Bao, Doug Foster, Dhananjay Kumar, Kris Innanen, Mark Chapman, Wenyi Hu, et al. "Frequency-dependent seismic analysis: Data processing, modeling, and interpretation." Leading Edge 38, no. 7 (July 2019): 556–57. http://dx.doi.org/10.1190/tle38070556.1.
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A one-day postconvention workshop held during the 2018 SEG Annual Meeting in Anaheim, California, focused on seismic attenuation model building and compensation through imaging in the morning and on frequency-dependent seismic interpretation and rock physics in the afternoon. The workshop was organized by Dhananjay Kumar (BP), Yi Shen (Shell), Kui Bao (Shell), Mark Chapman (University of Edinburgh), Doug Foster (The University of Texas at Austin), Wenyi Hu (Advanced Geophysical Tech Inc.), and Tieyuan Zhu (Pennsylvania State University). The main topics discussed were: attenuation and Q model building using seismic, vertical seismic profiling, well-log and core data, seismic attenuation compensation, rock-physics modeling, seismic modeling, and frequency-dependent seismic interpretation.
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González, Ezequiel F., Tapan Mukerji, and Gary Mavko. "Seismic inversion combining rock physics and multiple-point geostatistics." GEOPHYSICS 73, no. 1 (January 2008): R11—R21. http://dx.doi.org/10.1190/1.2803748.
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A novel inversion technique combines rock physics and multiple-point geostatistics. The technique is based on the formulation of the inverse problem as an inference problem and incorporates multiple-point geostatistics and conditional rock physics to characterize previously known geologic information. The proposed implementation combines elements of sampling from conditional probabilities and elements of optimization. The technique provides multiple solutions, all consistent with the expected geology, well-log data, seismic data, and the local rock-physics transformations. A pattern-based algorithm was selected as the multiple-point geostatistics component. Rock-physics principles are incorporated at the beginning of the process, defining the links between reservoir properties (e.g., lithology, saturation) and physical quantities (e.g., compressibility, density), making it possible to predict situations not sampled by log data. Results for seismic lithofacies inversion on a synthetic test and a real data application demonstrate the validity and applicability of the proposed inversion technique.
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Mur, Alan, and Lev Vernik. "Testing popular rock-physics models." Leading Edge 38, no. 5 (May 2019): 350–57. http://dx.doi.org/10.1190/tle38050350.1.
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In the spirit of classic rock physics, and as an ideal foundation for conventional quantitative interpretation workflows, we consider several popular models relating elastic rock properties to their composition, microstructure, and effective stress on the background of a worldwide log data set, incorporating sands and shales characterized by the maximum dynamic impedance range. We demonstrate that the patchy cement model, ellipsoidal inclusion model, and siliciclastic diagenesis model may be calibrated successfully against the world data set and used in seismic rock property log restoration/editing. We also demonstrate that some of these models present obvious challenges in terms of the information derived from quantitative seismic interpretation. Notably, the key input parameters used in these rock-physics models may show little resemblance to the rock parameters actually observed in geologic studies. Replacing the true rock parameters with the effective ones may do disservice to the science of rock physics in general.
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Prasad, Manika, Stanislav Glubokovskikh, Thomas Daley, Similoluwa Oduwole, and William Harbert. "CO2 messes with rock physics." Leading Edge 40, no. 6 (June 2021): 424–32. http://dx.doi.org/10.1190/tle40060424.1.
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Seismic techniques are the main monitoring tools for CO2 storage projects, especially in saline aquifers with good porosity. The majority of existing commercial and pilot CO2 injections have resulted in clear time-lapse seismic anomalies that can be used for leakage detection as well as refinement of the reservoir models to conform with the monitoring observations. Both tasks are legal requirements imposed on site operators. This paper revisits the rock-physics effects that may play an important role in the quantitative interpretation of seismic data. First, we briefly describe a standard approach to the rock-physics modeling of CO2 injections: Gassmann-type fluid substitution accounts for the presence of compressible CO2 in the pore space, and dissolution/precipitation of the minerals changes the pore volume. For many geologic conditions and injection scenarios, this approach is inadequate. For example, dissolution of the carbonate cement may weaken the rock frame, wave-induced fluid flow between CO2 patches can vary the magnitude of the seismic response significantly for the same saturation, the fluid itself might undergo change, and the seal might act as a sink for CO2. Hence, we critically review the effects of some recent advances in understanding CO2 behavior in the subsurface and associated rock-physics effects. Such a review should help researchers and practitioners navigate through the abundance of published work and design a rock-physics modeling workflow for their particular projects.
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Dvorkin, Jack, and Uri Wollner. "Rock-physics transforms and scale of investigation." GEOPHYSICS 82, no. 3 (May 1, 2017): MR75—MR88. http://dx.doi.org/10.1190/geo2016-0422.1.
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Rock-physics “velocity-porosity” transforms are usually established on sets of laboratory and/or well data with the latter data source being dominant in recent practice. The purpose of establishing such transforms is to (1) conduct forward modeling of the seismic response for various geologically plausible “what if” scenarios in the subsurface and (2) interpret seismic data for petrophysical properties and conditions, such as porosity, clay content, and pore fluid. Because the scale of investigation in the well is considerably smaller than that in reflection seismology, an important question is whether the rock-physics model established in the well can be used at the seismic scale. We use synthetic examples and well data to show that a rock-physics model established at the well approximately holds at the seismic scale, suggest a reason for this scale independence, and explore where it may be violated. The same question can be addressed as an inverse problem: Assume that we have a rock-physics transform and know that it works at the scale of investigation at which the elastic properties are seismically measured. What are the upscaled (smeared) petrophysical properties and conditions that these elastic properties point to? It appears that they are approximately the arithmetically volume-averaged porosity and clay content (in a simple quartz/clay setting) and are close to the arithmetically volume-averaged bulk modulus of the pore fluid (rather than averaged saturation).
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Grana, Dario. "Multivariate probabilistic rock-physics models using Kumaraswamy distributions." GEOPHYSICS 86, no. 5 (August 30, 2021): MR261—MR270. http://dx.doi.org/10.1190/geo2021-0124.1.
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Rock-physics models are physical equations that map petrophysical properties into geophysical variables, such as elastic properties and density. These equations are generally used in quantitative log and seismic interpretation to estimate the properties of interest from measured well logs and seismic data. Such models are generally calibrated using core samples and well-log data and result in accurate predictions of the unknown properties. Because the input data are often affected by measurement errors, the model predictions are often uncertain. Instead of applying rock-physics models to deterministic measurements, I have applied the models to the probability density function (PDF) of the measurements. This approach has been previously adopted in the literature using Gaussian distributions, but for petrophysical properties of porous rocks, such as volumetric fractions of solid and fluid components, the standard probabilistic formulation based on Gaussian assumptions is not applicable due to the bounded nature of the properties, the multimodality, and the nonsymmetric behavior. The proposed approach is based on the Kumaraswamy PDF for continuous random variables, which allows modeling double-bounded nonsymmetric distributions and is analytically tractable, unlike beta or Dirichlet distributions. I have developed a probabilistic rock-physics model applied to double-bounded continuous random variables distributed according to a Kumaraswamy distribution and derived the analytical solution of the probability distribution of the rock-physics model predictions. The method is evaluated for three rock-physics models: Raymer’s equation, Dvorkin’s stiff sand model, and Kuster-Toksöz’s inclusion model.
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Japsen, Peter, Anders Bruun, Ida L. Fabricius, and Gary Mavko. "Identification of hydrocarbons in chalk reservoirs from surface seismic data: South Arne field, North Sea." Geological Survey of Denmark and Greenland (GEUS) Bulletin 7 (July 29, 2005): 13–16. http://dx.doi.org/10.34194/geusb.v7.4823.
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Seismic data are mainly used to map out structures in the subsurface, but are also increasingly used to detect differences in porosity and in the fluids that occupy the pore space in sedimentary rocks. Hydrocarbons are generally lighter than brine, and the bulk density and sonic velocity (speed of pressure waves or P-wave velocity) of hydrocarbon-bearing sedimentary rocks are therefore reduced compared to non-reservoir rocks. However, sound is transmitted in different wave forms through the rock, and the shear velocity (speed of shear waves or S-wave velocity) is hardly affected by the density of the pore fluid. In order to detect the presence of hydrocarbons from seismic data, it is thus necessary to investigate how porosity and pore fluids affect the acoustic properties of a sedimentary rock. Much previous research has focused on describing such effects in sandstone (see Mavko et al. 1998), and only in recent years have corresponding studies on the rock physics of chalk appeared (e.g. Walls et al. 1998; Røgen 2002; Fabricius 2003; Gommesen 2003; Japsen et al. 2004). In the North Sea, chalk of the Danian Ekofisk Formation and the Maastrichtian Tor Formation are important reservoir rocks. More information could no doubt be extracted from seismic data if the fundamental physical properties of chalk were better understood. The presence of gas in chalk is known to cause a phase reversal in the seismic signal (Megson 1992), but the presence of oil in chalk has only recently been demonstrated to have an effect on surface seismic data (Japsen et al. 2004). The need for a better link between chalk reservoir parameters and geophysical observations has, however, strongly increased since the discovery of the Halfdan field proved major reserves outside four-way dip closures (Jacobsen et al. 1999; Vejbæk & Kristensen 2000).
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Dupuy, Bastien, Stéphane Garambois, Amir Asnaashari, Hadi M. Balhareth, Martin Landrø, Alexey Stovas, and Jean Virieux. "Estimation of rock physics properties from seismic attributes — Part 2: Applications." GEOPHYSICS 81, no. 4 (July 2016): M55—M69. http://dx.doi.org/10.1190/geo2015-0492.1.
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The estimation of quantitative rock physics properties is of great importance for reservoir characterization and monitoring in [Formula: see text] storage or enhanced oil recovery as an example. We have combined the high-resolution results of full-waveform inversion (FWI) methods with rock physics inversion. Because we consider a generic and dynamic rock physics model, our method is applicable to most kinds of rocks for a wide range of frequencies. The first step allows determination of viscoelastic effective properties, i.e., quantitative seismic attributes, whereas the rock physics inversion estimates rock physics properties (porosity, solid frame moduli, fluid phase properties, or saturation). This two-step workflow is applied to time-lapse synthetic and field cases. The sensitivity tests that we had previously carried out showed that it can be crucial to use multiparameter inputs to accurately recover fluid saturations and fluid properties. However, due to the limited data availability and difficulties in getting reliable multiparameter FWI results, we are limited to acoustic FWI results. The synthetic tests are conclusive even if they are favorable cases. For the first time-lapse fluid substitution synthetic case, we first characterize the rock frame parameters on the baseline model using P-wave velocity estimations obtained by acoustic FWI. Then, we obtain an accurate estimation of fluid bulk modulus from the time-lapse P-wave velocity. In the Marmousi synthetic case, the rock frame properties are accurately recovered for the baseline model, whereas the gas saturation change in the monitor model is not estimated correctly. On the field data example (time-lapse monitoring of an underground blowout in the North Sea), the estimation of rock frame properties gives results on a relatively narrow range, and we use this estimation as a starting model for the gas saturation inversion. We have found that the estimation of the gas saturation is not accurate enough, and the use of attenuation data is then required. However, the uncertainty on the estimation of baseline rock frame properties is not critical to monitor gas saturation changes.
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Will, Robert, Tom Bratton, William Ampomah, Samuel Acheampong, Martha Cather, and Robert Balch. "Time-Lapse Integration at FWU: Fluids, Rock Physics, Numerical Model Integration, and Field Data Comparison." Energies 14, no. 17 (September 2, 2021): 5476. http://dx.doi.org/10.3390/en14175476.
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We present the current status of time-lapse seismic integration at the Farnsworth (FWU) CO2 WAG (water-alternating-gas) EOR (Enhanced Oil Recovery) project at Ochiltree County, northwest Texas. As a potential carbon sequestration mechanism, CO2 WAG projects will be subject to some degree of monitoring and verification, either as a regulatory requirement or to qualify for economic incentives. In order to evaluate the viability of time-lapse seismic as a monitoring method the Southwest Partnership (SWP) has conducted time-lapse seismic monitoring at FWU using the 3D Vertical Seismic Profiling (VSP) method. The efficacy of seismic time-lapse depends on a number of key factors, which vary widely from one application to another. Most important among these are the thermophysical properties of the original fluid in place and the displacing fluid, followed by the petrophysical properties of the rock matrix, which together determine the effective elastic properties of the rock fluid system. We present systematic analysis of fluid thermodynamics and resulting thermophysical properties, petrophysics and rock frame elastic properties, and elastic property modeling through fluid substitution using data collected at FWU. These analyses will be framed in realistic scenarios presented by the FWU CO2 WAG development. The resulting fluid/rock physics models will be applied to output from the calibrated FWU compositional reservoir simulation model to forward model the time-lapse seismic response. Modeled results are compared with field time-lapse seismic measurements and strategies for numerical model feedback/update are discussed. While mechanical effects are neglected in the work presented here, complementary parallel studies are underway in which laboratory measurements are introduced to introduce stress dependence of matrix elastic moduli.
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Nurhandoko, B., M. Choliq, K. Triyoso, I. Soemantri, S. Praptono, and M. Nurcahyo. "Quantitative characterization of hydrocarbon reservoir using integrated seismic rock physics analysis: an integrated approach using seismic data, seismic rock physics of well-log and core." ASEG Extended Abstracts 2009, no. 1 (2009): 1. http://dx.doi.org/10.1071/aseg2009ab026.
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Yang, Xiuwei, and Peimin Zhu. "Reservoir Prediction Under Control of Sedimentary Facies." Journal of Computational Acoustics 25, no. 03 (September 2017): 1750022. http://dx.doi.org/10.1142/s0218396x17500229.
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Acoustic impedance (AI) from seismic inversion can indicate rock properties and can be used, when combined with rock physics, to predict reservoir parameters, such as porosity. Solutions to seismic inversion problem are almost nonunique due to the limited bandwidth of seismic data. Additional constraints from well log data and geology are needed to arrive at a reasonable solution. In this paper, sedimentary facies is used to reduce the uncertainty in inversion and rock physics modeling; the results not only agree with seismic data, but also conform to geology. A reservoir prediction method, which incorporates seismic data, well logs, rock physics and sedimentary facies, is proposed. AI was first derived by constrained sparse spike inversion (CSSI) using a sedimentary facies dependent low-frequency model, and then was transformed to reservoir parameters by sequential simulation, statistical rock physics and [Formula: see text]-model. Two numerical experiments using synthetic model and real data indicated that the sedimentary facies information may help to obtain a more reasonable prediction.
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Widyantoro, Adi, and Matthew Saul. "Shaly sand rock physics analysis and seismic inversion implication." APPEA Journal 54, no. 2 (2014): 503. http://dx.doi.org/10.1071/aj13076.
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The analysis of well data from the Enfield field of the Exmouth Sub-basin, WA, indicates that both cementation and pore-filling clay appear to have a stiffening effect on the reservoir sands. The elastic contrast between brine sand and the overlying shale is often small and the large amplitudes observed from seismic data are associated with hydrocarbon content. More detailed rock physics and depth trend analysis of elastic and petrophysical properties, however, indicate significant spatial variability in the cap rock shales across the field with different sand shale mixtures, causing changes in the elastic response of the rock. Areas where shales are softer produce weak seismic amplitude contrasts even with high hydrocarbon saturation; the amplitude response being similar to areas with stiffer shales and brine-filled sands. The variations in reservoir quality are, therefore, masked by the distribution of the brine, oil and gas, as well as the variations in the cap rock. The Enfield rock physics analysis provides an example of reducing amplitude ambiguity over lithology-fluid variation and improves the chance of successful interpretation of the results of seismic inversion.
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Durrani, Muhammad Z. A., Keith Willson, Jingyi Chen, Bryan Tapp, and Jubran Akram. "Rational Rock Physics for Improved Velocity Prediction and Reservoir Properties Estimation for Granite Wash (Tight Sands) in Anadarko Basin, Texas." International Journal of Geophysics 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/209351.
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Due to the complex nature, deriving elastic properties from seismic data for the prolific Granite Wash reservoir (Pennsylvanian age) in the western Anadarko Basin Wheeler County (Texas) is quite a challenge. In this paper, we used rock physics tool to describe the diagenesis and accurate estimation of seismic velocities of P and S waves in Granite Wash reservoir. Hertz-Mindlin and Cementation (Dvorkin’s) theories are applied to analyze the nature of the reservoir rocks (uncemented and cemented). In the implementation of rock physics diagnostics, three classical rock physics (empirical relations, Kuster-Toksöz, and Berryman) models are comparatively analyzed for velocity prediction taking into account the pore shape geometry. An empirical (VP-VS) relationship is also generated calibrated with core data for shear wave velocity prediction. Finally, we discussed the advantages of each rock physics model in detail. In addition, cross-plots of unconventional attributes help us in the clear separation of anomalous zone and lithologic properties of sand and shale facies over conventional attributes.
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Dutta, N. C. "Geopressure prediction using seismic data: Current status and the road ahead." GEOPHYSICS 67, no. 6 (November 2002): 2012–41. http://dx.doi.org/10.1190/1.1527101.
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The subject of seismic detection of abnormally high‐pressured formations has received a great deal of attention in exploration and production geophysics because of increasing exploration and production activities in frontier areas (such as the deepwater) and a need to lower cost without compromising safety and environment, and manage risk and uncertainty associated with very expensive drilling. The purpose of this review is to capture the “best practice” in this highly specialized discipline and document it. Pressure prediction from seismic data is based on fundamentals of science, especially those of rock physics and seismic attribute analysis. Nonetheless, since the first seismic application in the 1960s, practitioners of the technology have relied increasingly on empiricism, and the fundamental limitations of the tools applied to detect such hazardous formations were lost. The most successful approach to seismic pressure prediction is one that combines a good understanding of rock properties of subsurface formations with the best practice for seismic velocity analysis appropriate for rock physics applications, not for stacking purposes. With the step change that the industry has seen in the application of the modern digital computing technology to solving large‐scale exploration and production problems using seismic data, the detection of pressured formations can now be made with more confidence and better resolution. The challenge of the future is to break the communication and the “language barrier” that still exists between the seismologists, the rock physicists, and the drilling community.
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Grana, Dario. "Probabilistic approach to rock physics modeling." GEOPHYSICS 79, no. 2 (March 1, 2014): D123—D143. http://dx.doi.org/10.1190/geo2013-0333.1.
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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.
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Jiang, Lian, and John P. Castagna. "On the rock-physics basis for seismic hydrocarbon detection." GEOPHYSICS 85, no. 1 (November 18, 2019): MR25—MR35. http://dx.doi.org/10.1190/geo2018-0801.1.
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One of the primary fluid indicators for direct hydrocarbon detection in sandstones using seismic reflectivity is the difference between the saturated-rock P-wave impedance and the rock-frame impedance. This can be expressed in terms of the difference between the observed P-wave impedance squared and a multiplier times the square of the observed S-wave impedance. This multiplier is a fluid discrimination parameter that laboratory and log measurements suggest varies over a wide range. Theoretically, this parameter is related to the ratio of the frame bulk and shear moduli and the ratio of the frame and fluid-saturated rock densities. In practice, empirical determination of the fluid discrimination parameter may be required for a given locality. Given sufficient data for calibration, the parameter can be adjusted so as to best distinguish hydrocarbon-saturated targets from brine-saturated rocks. Using an empirically optimized fluid discrimination parameter has a greater impact on hydrocarbon detection success rate in the oil cases studied than for gas reservoirs, for which there is more latitude. Application to a wide variety of well-log and laboratory measurements suggests that the empirically optimized parameter may differ from direct theoretical calculations made using Gassmann’s equations. Combining laboratory and log measurements for sandstones having a broad range of frame moduli, varying from poorly consolidated to highly lithified, reveals a simple linear empirical relationship between the optimized fluid discrimination parameter and the squared velocity ratio of brine-saturated sandstones.
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Wei, Shuijian, Michael V. DeAngelo, and Bob A. Hardage. "Interpretation of multicomponent seismic data across Wister geothermal field, Imperial Valley, California." Interpretation 2, no. 2 (May 1, 2014): SE125—SE135. http://dx.doi.org/10.1190/int-2013-0083.1.
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Multicomponent seismic technology has been implemented across Wister geothermal field in southern California to evaluate the potential for further development of geothermal resources. The seismic survey was positioned atop the San Andreas fault system that extends southward from the Salton Sea. An interpretation of Wister Field geology was made using both P-P and P-SV seismic data. Two formation horizons, Canebrake/Olla/Diablo and Deguynos, were interpreted. Seismic time-structure maps were generated for each horizon. The objective of the study was to determine whether productive geothermal resources could be detected and mapped more reliably with multicomponent seismic data than with single-component P-P data. Complex faults associated with the regional San Andreas Fault system were interpreted across the [Formula: see text] 3D image space. The structural maps created are thought to be some of the most accurate depictions of subsurface structure publicly available in this area of the Imperial Valley. Particular attention was given to documenting faults that cut across deep strata. Both P-P and P-SV seismic showed evidence of such deep faults. Rock properties were analyzed from well logs. Log data showed that clastic rocks at this site exhibited measurable differences in [Formula: see text] velocity ratios for different rock types. Specifically, sand-prone intervals were associated with relatively low [Formula: see text] velocity ratios, and shale-dominated intervals had higher [Formula: see text] ratios. Using this rock physics behavior, [Formula: see text] values derived from seismic traveltime thicknesses were useful for recognizing lithological distributions and identifying favorable reservoir facies. Seismic data across Wister Field, like seismic data across many geothermal fields, have a low signal-to-noise character. We demonstrate that a unified and integrated interpretation of P and S data, even when seismic data quality is not as good as interpreters wish, can still yield valuable information for resource exploitation.
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Saul, Matthew, and David Lumley. "The combined effects of pressure and cementation on 4D seismic data." GEOPHYSICS 80, no. 2 (March 1, 2015): WA135—WA148. http://dx.doi.org/10.1190/geo2014-0226.1.
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Time-lapse seismology has proven to be a useful method for monitoring reservoir fluid flow, identifying unproduced hydrocarbons and injected fluids, and improving overall reservoir management decisions. The large magnitudes of observed time-lapse seismic anomalies associated with strong pore pressure increases are sometimes not explainable by velocity-pressure relationships determined by fitting elastic theory to core data. This can lead to difficulties in interpreting time-lapse seismic data in terms of physically realizable changes in reservoir properties during injection. It is commonly assumed that certain geologic properties remain constant during fluid production/injection, including rock porosity and grain cementation. We have developed a new nonelastic method based on rock physics diagnostics to describe the pressure sensitivity of rock properties that includes changes in the grain contact cement, and we applied the method to a 4D seismic data example from offshore Australia. We found that water injection at high pore pressure may mechanically weaken the poorly consolidated reservoir sands in a nonelastic manner, allowing us to explain observed 4D seismic signals that are larger than can be predicted by elastic theory fits to the core data. A comparison of our new model with the observed 4D seismic response around a large water injector suggested a significant mechanical weakening of the reservoir rock, consistent with a decrease in the effective grain contact cement from 2.5% at the time/pressure of the preinjection baseline survey, to 0.75% at the time/pressure of the monitor survey. This approach may enable more accurate interpretations and future predictions of the 4D signal for subsequent monitor surveys and improve 4D feasibility and interpretation studies in other reservoirs with geomechanically similar rocks.
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Li, Yunyue, Biondo Biondi, Robert Clapp, and Dave Nichols. "Integrated VTI model building with seismic data, geologic information, and rock-physics modeling — Part 1: Theory and synthetic test." GEOPHYSICS 81, no. 5 (September 2016): C177—C191. http://dx.doi.org/10.1190/geo2015-0592.1.
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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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Sayers, Colin M., and Sagnik Dasgupta. "A predictive anisotropic rock-physics model for estimating elastic rock properties of unconventional shale reservoirs." Leading Edge 38, no. 5 (May 2019): 358–65. http://dx.doi.org/10.1190/tle38050358.1.
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This paper presents a predictive rock-physics model for unconventional shale reservoirs based on an extended Maxwell scheme. This model accounts for intrinsic anisotropy of rock matrix and heterogeneities and shape-induced anisotropy arising because the dimensions of kerogen inclusions and pores are larger parallel to the bedding plane than perpendicular to this plane. The model relates the results of seismic amplitude variation with offset inversion, such as P- and S-impedance, to the composition of the rock and enables identification of rock classes such as calcareous, argillaceous, siliceous, and mixed shales. This allows the choice of locations with the best potential for economic production of hydrocarbons. While this can be done using well data, prestack inversion of seismic P-wave data allows identification of the best locations before the wells are drilled. The results clearly show the ambiguity in rock classification obtained using poststack inversion of P-wave seismic data and demonstrate the need for prestack seismic inversion. The model provides estimates of formation anisotropy, as required for accurate determination of P- and S-impedance, and shows that anisotropy is a function not only of clay content but also other components of the rock as well as the aspect ratio of kerogen and pores. Estimates of minimum horizontal stress based on the model demonstrate the need to identify rock class and estimate anisotropy to determine the location of any stress barriers that may inhibit hydraulic fracture growth.
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Azevedo, Leonardo, Dario Grana, and Leandro de Figueiredo. "Stochastic perturbation optimization for discrete-continuous inverse problems." GEOPHYSICS 85, no. 5 (July 28, 2020): M73—M83. http://dx.doi.org/10.1190/geo2019-0520.1.
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Accurate subsurface modeling and characterization require the prediction of facies and rock properties within the reservoir model. This is commonly achieved by inverting geophysical data, such as seismic reflection data, using a two-step approach either in the discrete or the continuous domain. We have adopted an iterative simultaneous method, namely, stochastic perturbation optimization, to invert seismic reflection data jointly for facies and rock properties. Facies first are simulated according to a Markov chain model, and then rock properties are generated with stochastic sequential simulation and cosimulation conditioned to each facies. Elastic and seismic data are computed by applying a rock-physics model to the realizations of petrophysical properties and a seismic convolutional model. The similarity between observed and synthetic seismic data is used to update the solution by perturbing facies and rock properties until convergence. Coupling the discrete and continuous domains ensures a consistent perturbation of the reservoir models throughout the iterations. We have evaluated the method in a 1D synthetic example for the estimation of facies and porosity from zero-offset seismic data assuming a linear rock-physics model to demonstrate the validity of the method. Then, we apply the method to a real 3D data set from the North Sea for the joint estimation of facies and petrophysical properties from prestack seismic data. The results show spatially consistent rock and fluid inverted models in which the predicted facies reproduce the vertical ordering as observed in the well data.
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Chopra, Satinder, Ritesh Kumar Sharma, Hossein Nemati, and James Keay. "Seismic reservoir characterization of Utica-Point Pleasant Shale with efforts at quantitative interpretation — A case study: Part 1." Interpretation 6, no. 2 (May 1, 2018): T313—T324. http://dx.doi.org/10.1190/int-2017-0134.1.
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The Utica Shale is one of the major source rocks in Ohio, and it extends across much of the eastern United States. Its organic richness, high content of calcite, and development of extensive organic porosity make it a perfect unconventional play, and it has gained the attention of the oil and gas industry. The primary target zone in the Utica Play includes the Utica Formation, Point Pleasant Formation, and Trenton Formation intervals. We attempt to identify the sweet spots within the Point Pleasant interval using 3D seismic data, available well data, and other relevant data. This has been done by way of organic richness and brittleness estimation in the rock intervals. The organic richness is determined by weight % of total organic carbon content, which is derived by transforming the inverted density volume. Core-log petrophysical modeling provides the necessary relationship for doing so. The brittleness is derived using rock-physics parameters such as the Young’s modulus and Poisson’s ratio. Deterministic simultaneous inversion along with a neural network approach are followed to compute the rock-physics parameters and density using seismic data. The correlation of sweet spots identified based on the seismic data with the available production data emphasizes the significance of integration of seismic data with all other relevant data.
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Glinsky, Michael E., Andrea Cortis, Jinsong Chen, Doug Sassen, and Howard Rael. "Geomechanical property estimation of unconventional reservoirs using seismic data and rock physics." Geophysical Prospecting 63, no. 5 (April 9, 2015): 1224–45. http://dx.doi.org/10.1111/1365-2478.12211.
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Alkaff, Mohammed, Boris Gurevich, Cedric Griffiths, and Mahyar Madadi. "Integration of Stratigraphic & Rock Physics Models to Generate Synthetic Seismic Data." ASEG Extended Abstracts 2015, no. 1 (December 2015): 1–4. http://dx.doi.org/10.1071/aseg2015ab151.
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Suman, Amit, and Tapan Mukerji. "Sensitivity study of rock-physics parameters for modeling time-lapse seismic response of Norne field." GEOPHYSICS 78, no. 6 (November 1, 2013): D511—D523. http://dx.doi.org/10.1190/geo2013-0045.1.
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Time-lapse seismic modeling is an important step in joint inversion of time-lapse seismic and production data of a field. Rock-physics analysis is the basis for modeling the time-lapse seismic data. However, joint inversion of both types of data for estimation of reservoir parameters is highly nonlinear and complex with uncertainties at each step of the process. So it is essential, before proceeding with large-scale history matching, to investigate sensitive rock-physics parameters in modeling the time-lapse seismic response of a field. We used the data set of the Norne field to investigate sensitive parameters in time-lapse seismic modeling. We first investigated sensitive parameters in the Gassmann’s equation. The investigated parameters include mineral properties, water salinity, pore pressure, and gas-oil ratio. Next, we investigated parameter sensitivity for time-lapse seismic modeling of the Norne field. The investigated rock-physics parameters are clay content, cement fraction, average number of contact grains per sand, pore pressure, and fluid mixing. We observed that the average number of contact grains per sand had the most impact on time-lapse seismic modeling of the Norne field. The clay content was the most sensitive parameter in fluid substitution for calculating seismic velocities of the Norne field. Salinity and pore pressure had minimal impact on fluid substitution for this case. This sensitivity analysis helps to select important parameters for time-lapse (4D) seismic history matching, which is an important aspect of joint inversion of production and time-lapse seismic modeling of a field.
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Dræge, Anders. "Geo-consistent depth trends: Honoring geology in siliciclastic rock-physics depth trends." Leading Edge 38, no. 5 (May 2019): 379–84. http://dx.doi.org/10.1190/tle38050379.1.
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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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Liu, Yangjun (Kevin), Jonathan Hernandez Casado, Mohamed El-Toukhy, and Shenghong Tai. "Basin-wide empirical rock-physics transform and its application in Campeche Basin." Leading Edge 40, no. 3 (March 2021): 178–85. http://dx.doi.org/10.1190/tle40030178.1.
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Rock properties in the subsurface are of major importance for evaluating the petroleum prospectivity of a sedimentary basin. The key rock properties to understand are porosity, density, temperature, effective stress, and pore pressure. These rock properties can be obtained or calculated when borehole data are available. However, borehole data are usually sparse, especially in frontier basins. We propose some simple rock-physics transforms for converting P-wave velocity to other rock properties. We found that these rock-physics transforms are predictive in the east and west sides of Campeche Basin. The proposed rock-physics transforms can be used to obtain laterally varying rock properties based on information derived from seismic data.
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Alvarez, Pedro, Amanda Alvarez, Lucy MacGregor, Francisco Bolivar, Robert Keirstead, and Thomas Martin. "Reservoir properties prediction integrating controlled-source electromagnetic, prestack seismic, and well-log data using a rock-physics framework: Case study in the Hoop Area, Barents Sea, Norway." Interpretation 5, no. 2 (May 31, 2017): SE43—SE60. http://dx.doi.org/10.1190/int-2016-0097.1.
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We have developed an example from the Hoop Area of the Barents Sea showing a sequential quantitative integration approach to integrate seismic and controlled-source electromagnetic (CSEM) attributes using a rock-physics framework. The example illustrates a workflow to address the challenges of multiphysics and multiscale data integration for reservoir characterization purposes. A data set consisting of 2D GeoStreamer seismic and towed streamer electromagnetic data that were acquired concurrently in 2015 by PGS provide the surface geophysical measurements that we used. Two wells in the area — Wisting Central (7324/8-1) and Wisting Alternative (7324/7-1S) — provide calibration for the rock-physics modeling and the quantitative integrated analysis. In the first stage of the analysis, we invert prestack seismic and CSEM data separately for impedance and anisotropic resistivity, respectively. We then apply the multi-attribute rotation scheme (MARS) to estimate rock properties from seismic data. This analysis verified that the seismic data alone cannot distinguish between commercial and noncommercial hydrocarbon saturation. Therefore, in the final stage of the analysis, we invert the seismic and CSEM-derived properties within a rock-physics framework. The inclusion of the CSEM-derived resistivity information within the inversion approach allows for the separation of these two possible scenarios. Results reveal excellent correlation with known well outcomes. The integration of seismic, CSEM, and well data predicts very high hydrocarbon saturations at Wisting Central and no significant saturation at Wisting Alternative, consistent with the findings of each well. Two further wells were drilled in the area and used as blind tests in this case: The slightly lower saturation predicted at Hanssen (7324/7-2) is related to 3D effects in the CSEM data, but the positive outcome of the well is correctly predicted. At Bjaaland (7324/8-2), although the seismic indications are good, the integrated interpretation result predicts correctly that this well was unsuccessful.
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Bedle, Heather. "Seismic attribute enhancement of weak and discontinuous gas hydrate bottom-simulating reflectors in the Pegasus Basin, New Zealand." Interpretation 7, no. 3 (August 1, 2019): SG11—SG22. http://dx.doi.org/10.1190/int-2018-0222.1.
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Gas hydrates in the oceanic subsurface are often difficult to image with reflection seismic data, particularly when the strata run parallel to the seafloor and in regions that lack the presence of a bottom-simulating reflector (BSR). To address and understand these imaging complications, rock-physics modeling and seismic attribute analysis are performed on modern 2D lines in the Pegasus Basin in New Zealand, where the BSR is not continuously imaged. Based on rock-physics and seismic analyses, several seismic attribute methods identify weak BSR reflections, with the far-angle stack data being particularly effective. Rock modeling results demonstrate that far-offset seismic data are critical in improving the imaging and interpretation of the base of the gas hydrate stability zone. The rock-physics modeling results are applied to the Pegasus 2009 2D data set that reveals a very weak seismic reflection at the base of the hydrates in the far-angle stack. This often-discontinuous reflection is significantly weaker in amplitude than typical BSRs associated with hydrates. These weak far-angle stack BSRs often do not appear clearly in full stack data, the most commonly interpreted seismic data type. Additional amplitude variation with angle (AVA) attribute analyses provide insight into identifying the presence of gas hydrates in regions lacking a strong BSR. Although dozens of seismic attributes were investigated for their ability to reveal weak reflections at the base of the gas hydrate stability zone, those that enhance class 2 AVA anomalies were most effective, particularly the seismic fluid factor attribute.
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Bachrach, Ran. "Joint estimation of porosity and saturation using stochastic rock-physics modeling." GEOPHYSICS 71, no. 5 (September 2006): O53—O63. http://dx.doi.org/10.1190/1.2235991.
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Sediment porosity and saturation affect bulk modulus, shear modulus, and density. Consequently, estimating hydrocarbon saturation and reservoir porosity from seismic data is a joint estimation problem: Uncertainty in porosity will lead to errors in saturation prediction, and vice versa. Porosity and saturation can be jointly estimated using stochastic rock-physics modeling and formal Bayesian estimation methodology. Knowledge of shear impedance reduces the uncertainty in porosity and thus also reduces uncertainty in saturation estimation. This study investigates joint estimation of porosity and saturation by using rock-physics, stochastic modeling, and Bayesian estimation theory to derive saturation and porosity maps of expected pay sands. In the field example, the uncertainty in porosity, quantified by the standard deviation (STD) associated with the posterior probability density function (pdf), derived from inversion of seismic data is much less than the uncertainty in the derived saturation. For a typical case, the STD associated with saturation is [Formula: see text] while porosity STD is about 1.34 porosity units given seismic-derived inversion attributes with reasonable accuracy. Comparison of these numbers with prior estimates showed that inversion of seismic data decreased the uncertainty in porosity to 15% of the prior uncertainty while saturation uncertainty was only reduced to 92% of the prior uncertainty. Although these results may vary from one location to another, the methodology is general and can be applied to other locations.
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Mavko, Gary, and Tapan Mukerji. "A rock physics strategy for quantifying uncertainty in common hydrocarbon indicators." GEOPHYSICS 63, no. 6 (November 1998): 1997–2008. http://dx.doi.org/10.1190/1.1444493.
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We present a strategy for quantifying uncertainties in rock physics interpretations by combining statistical techniques with deterministic rock physics relations derived from the laboratory and theory. A simple example combines Gassmann’s deterministic equation for fluid substitution with statistics inferred from log, core, and seismic data to detect hydrocarbons from observed seismic velocities. The formulation identifies the most likely pore fluid modulus corresponding to each observed seismic attribute and the uncertainty that arises because of natural variability in formation properties, in addition to the measurement uncertainties. We quantify the measure of information in terms of entropy and show the impact of additional data about S-wave velocity on the uncertainty of the hydrocarbon indicator. In some cases, noisy S data along with noisy P data can convey more information than perfect P data alone, while in other cases S data do not reduce the uncertainty. We apply the formulation to a well log example for detecting the most likely pore fluid and quantifying the associated uncertainty from observed sonic and density logs. The formulation offers a convenient way to implement deterministic fluid substitution equations in the realistic case when natural geologic variations cause the reference porosity and velocity to span a range of values.
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Mukuhira, Yusuke, Hiroshi Asanuma, Takatoshi Ito, and Markus O. Häring. "Physics-based seismic evaluation method: Evaluating possible seismic moment based on microseismic information due to fluid stimulation." GEOPHYSICS 81, no. 6 (November 2016): KS195—KS205. http://dx.doi.org/10.1190/geo2015-0648.1.
Full textAbstract:
The forecasting and risk assessment of induced seismicity associated with fluid injection have considerable importance for subsurface energy development. We have developed a seismic evaluation method called the possible seismic moment (PoSeMo) model to assess the potential seismic moment that could be released in the future based on current seismic activity. The PoSeMo model assumes the existence of a representative parameter that can describe the seismic characteristics of a given field. This parameter is defined as the seismic moment density, which quantifies the seismic moment able to be released per rock volume. The rock volume presumed to be in critical condition because of stimulation is defined as the stimulated rock volume. The current stimulation condition for the PoSeMo model can be estimated from the product of these two parameters. The difference between the output of the PoSeMo model and the observed cumulative seismic moment corresponds to the cumulative seismic moment that could be released in the future. This value can be transformed into the possible maximum magnitude that has clear physical meaning and that can be used as feedback on the stimulation operation for seismic hazard assessment. We have applied this model to a microseismic data set from the Basel engineered geothermal system project. We have successfully estimated reasonable values for seismic moment density and stimulated rock volume. The PoSeMo model performed well, and it provided reasonable estimates of seismic moment. The maximum magnitude estimated by the PoSeMo model was almost identical to the largest event that had occurred previously. Thus, it was concluded that the PoSeMo model satisfactorily demonstrated its feasibility as a real-time seismic evaluation method, based on physical parameters derived from microseismic information.
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Dupuy, Bastien, Stéphane Garambois, and Jean Virieux. "Estimation of rock physics properties from seismic attributes — Part 1: Strategy and sensitivity analysis." GEOPHYSICS 81, no. 3 (May 2016): M35—M53. http://dx.doi.org/10.1190/geo2015-0239.1.
Full textAbstract:
The quantitative estimation of rock physics properties is of great importance in any reservoir characterization. We have studied the sensitivity of such poroelastic rock physics properties to various seismic viscoelastic attributes (velocities, quality factors, and density). Because we considered a generalized dynamic poroelastic model, our analysis was applicable to most kinds of rocks over a wide range of frequencies. The viscoelastic attributes computed by poroelastic forward modeling were used as input to a semiglobal optimization inversion code to estimate poroelastic properties (porosity, solid frame moduli, fluid phase properties, and saturation). The sensitivity studies that we used showed that it was best to consider an inversion system with enough input data to obtain accurate estimates. However, simultaneous inversion for the whole set of poroelastic parameters was problematic due to the large number of parameters and their trade-off. Consequently, we restricted the sensitivity tests to the estimation of specific poroelastic parameters by making appropriate assumptions on the fluid content and/or solid phases. Realistic a priori assumptions were made by using well data or regional geology knowledge. We found that (1) the estimation of frame properties was accurate as long as sufficient input data were available, (2) the estimation of permeability or fluid saturation depended strongly on the use of attenuation data, and (3) the fluid bulk modulus can be accurately inverted, whereas other fluid properties have a low sensitivity. Introducing errors in a priori rock physics properties linearly shifted the estimations, but not dramatically. Finally, an uncertainty analysis on seismic input data determined that, even if the inversion was reliable, the addition of more input data may be required to obtain accurate estimations if input data were erroneous.
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Grana, Dario. "Bayesian linearized rock-physics inversion." GEOPHYSICS 81, no. 6 (November 2016): D625—D641. http://dx.doi.org/10.1190/geo2016-0161.1.
Full textAbstract:
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.
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de Figueiredo, Leandro Passos, Dario Grana, Fernando Luis Bordignon, Marcio Santos, Mauro Roisenberg, and Bruno B. Rodrigues. "Joint Bayesian inversion based on rock-physics prior modeling for the estimation of spatially correlated reservoir properties." GEOPHYSICS 83, no. 5 (September 1, 2018): M49—M61. http://dx.doi.org/10.1190/geo2017-0463.1.
Full textAbstract:
The joint inversion of seismic data for elastic and petrophysical properties is an inverse problem with a nonunique solution. There are several factors that impact the accuracy of the results, such as the statistical rock-physics relations and observation errors. We have developed a general methodology to incorporate a linearized rock-physics model in a multivariate multimodal prior distribution for Bayesian seismic linearized inversion. The prior distribution is used to define a mixture model for elastic and petrophysical properties and introduce physics-based correlations between the properties. Using the rock-physics prior model and a convolutional seismic forward model in the Bayesian inversion framework, we obtain an analytical expression of the spatially independent conditional distributions to be used as a proposal distribution in a Gibbs sampling algorithm. We then combine the sampling algorithm with geostatistical simulation methods to compute the spatially correlated posterior distribution of the model parameters. We apply our method to a real angle-stack seismic data set to generate multiple geostatistical realizations of facies, P-velocity, S-velocity, density, porosity, and water saturation. The method is validated through a blind well test and a comparison with the standard Bayesian linearized inversion.