Relevant bibliographies by topics / Rock physics; Seismic data

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Rock physics; Seismic data'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Rock physics; Seismic data"

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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.
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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.
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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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Dissertations / Theses on the topic "Rock physics; Seismic data"

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Said, Dhiya Mustafa Mohamed. "Reservoir geophysics of the Clyde field : the development and application of quantitative analysis techniques." Thesis, University of Aberdeen, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327396.

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Fanka, Walter Roye Taju. "Well Log and Seismic Data Interpretation : Rock Physics Study of Poorly Consolidated Sandstones in The North Sea." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for petroleumsteknologi og anvendt geofysikk, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-18608.

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We use rock physics models for poorly consolidated rocks to diagnose reservoir sandstones in the Alvheim Field, North Sea. Geological factors that will control the rock physics and seismic properties include clay content, sorting, diagenesis, mineralogy, and bedding configuration. The various geologic factors will affect the fluid and stress sensitivity in these rocks. We investigate the interrelationships between various geological factors and seismic fluid and stress sensitivity, by combining well log data and rock physics models. Finally, we determine inter-well characteristics in terms of varying geological factors at different locations and discuss the results in terms of expected seismic signatures in the area.
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Yan, Jun. "Improved rock physical models for the integration of core, log and seismic data." Thesis, University of Edinburgh, 2003. http://hdl.handle.net/1842/11633.

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In this thesis, I propose the following: - The P and S-wave velocities can provide a suitable link between reservoir parameters and rock properties using core, log and seismic data. - The pore aspect ratios as key parameters of rock geometry can be used to explain the different responses of elastic properties in clay-sand rocks (especially for thin and varying lithology formations). The use of fixed aspect ratio for physical velocity models will result in obvious errors in the prediction of elastic moduli and velocities (in particular for formations at shallow depth, or in loose and thin layers). - The time-average equation (Wyllie et al., 1956) ignored the effects of pore geometry, degree of consolidation fluid and clay content. It results in a hidden defect in the transformation between porosity (form core and well-log) and velocity (from seismic) when the rock contains clay. - The current models of Gassmann (1951), Kuster & Toksöz (1974) and Xu-White (1995) have some difficulties in calculating elastic moduli for rocks containing aligned pores and minerals in anisotropic formations. To investigate these, I first use method of multiple regression and artificial networks to establish an empirical correlation between reservoir parameters and P and S-wave velocities. This correlation includes porosity, clay content, aspect ratio and velocities, which can be used as an extension of the empirical model of Han et al (1956). Second, in order to overcome the weakness of empirical models, physically realistic theoretical models are established. The first theoretical model is the isotropic dual porosity model (IDP). The aim of the IDP is to develop a general rock physical model that provides a satisfactory integrated approach to the evaluation and prediction of reservoir parameters and rock properties for the purpose of reservoir characterization. Third, because the IDP model does not consider the effects of pore orientation, clay content and velocity anisotropy etc., a refined anisotropic dual porosity model (ADP) is then developed for anisotropic porous media.
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Adrian, Jorge Isaac. "Applicability of rock physics models in conjunction with seismic inverted data to characterize a low poro-perm gas-bearing sandstone reservoir for well location optimization, Bredasdorp Basin, SA." Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/19963.

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The primary focus of this dissertation is to develop a predictive rock physics theory that establishes relations between rock properties and the observed seismic and to present the results of different seismic characterization techniques to interpret a tight gas sand reservoir off the south coast of South Africa using as input rock physics analysis and inverted seismic outcomes. To perform the aims and goals of this study a workflow that involves the execution of three main processes was implemented: (1) rock physics modelling, (2) a simultaneous seismic inversion, and (3) seismic reservoir characterization techniques. First, a rock physics model was generated as a bridge between the seismic observables (density, Vp and Vs) and reservoir parameters such as fluid content, porosity and mineralogy. In situ and perturbational log - derived forward modelling was performed. Both in situ and perturbational forward modelling were used to generate synthetic seismic gathers, which were used to study the AVA attribute responses. Overall, the effect of fluid fill on this tight gas sand seismically is modest compared with the effect of porosity changes. Second, there follows a detailed description of a workflow implemented to simultaneously invert P and S pre - stack seismic data. The derived elastic properties (acoustic impedance, Vp/Vs and density) were then used in combination with the rock physics analysis to characterize seismically the reservoir. The predicted acoustic impedance and Vp/Vs volumes show a good tie with the log data. However, the density outcome was of limited quality compared with the two mentioned above. Finally, using outcomes from rock physic s analysis and/or inverted data, four seismic techniques to characterize the reservoir were conducted. The techniques involved are: (1) AVO cross - plotting to generate a good facies property based on AVO attributes (intercept - gradient) and rock physics in the area of study , (2) rock physics templates (RPTs) to compute discrete rock property volumes (litho - Sw, litho - porosity) using a collection of curves that cover all possible "what if" lithology - fluid content - porosity scenarios for the reservoir and the inverted data, (3) a lithological classification to calculate litho - facies probability volumes based on a litho - facies classification using petrophysical cut - off s , multivariate probability functions (PDFs) and inverted data, and (4) an extended elastic impedance (EEI) inversion to derive rock property volumes (Vclay, porosity) based on AVO attributes (intercept, gradient). Despite differences in the input and theory behind each technique, all outcomes share parallels in the distribution of good and poor facies or reservoir and non - reservoir zones.
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Zhang, John Jianlin. "Time-lapse seismic surveys, rock physics basis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ65147.pdf.

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Hoang, Phuong. "Rock physics depth trend analysis using seismic stacking velocity." Thesis, Norwegian University of Science and Technology, Department of Petroleum Engineering and Applied Geophysics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-1631.

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Quantitative seismic interpretation is becoming more and more important in exploration and characterization of petroleum reservoirs. In this technology, rock physic analysis combined with seismic attributes has become a key strategy.

Nature creates inhomogeneous anisotropic rocks where the rock physics properties vary at different positions and directions. It is important to analyze and quantify the property changes as a function of depositional and burial trends in order to improve our detectability of petroleum reservoirs from seismic data.

In this thesis, we have presented a new methodology to obtain rock physics properties as a function of burial depth, i.e., rock physics depth trends (RPDTs), from well log and seismic data. To obtain RPDTs, several authors have suggested using rock physics models calibrated to well log data or constrained by diagenetic models. We present an alternative way to extract these from seismic stacking velocities. This is the main focus of the thesis.

We apply our methodology to extract RPDTs from seismic stacking velocities in the Njord Field area, located in the Norwegian Sea. We find that the seismic interval velocity trend matches nicely to the sonic velocity at the well location, especially above Base Cretaceous. By combining empirical RPDTs with seismic RPDTs, we are able to interpret and quantify the rock properties of different rock physics events that have occurred in Njord Field at well location and in the areas without well log information.

In this thesis we have successfully demonstrated how stacking velocities can be used to improve our understanding about normal mechanical compaction trends, tectonic activity and diagenetic events. This information is important for improved overburden and reservoir characterization, especially in areas with sparse or no well log data.

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Gloria, Lopez Juan Carlos. "Integrating AVO, Seismic Inversion, and Rock Physics in Agua Fría 3D Seismic Cube." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for petroleumsteknologi og anvendt geofysikk, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-26114.

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Ten exploratory wells have been drilled in the Agua Fría area, led by amplitude anomalies and structural highs. Five of them resulted in dry wells and the other five in gas and oil discoveries. In some of these wells, water sands respond seismically as amplitude anomalies. On the other hand, some oil and gas sands are not easily recognizable from post-stack seismic data. Bright spots are also observed in the study area.Seismic interpretation can be uncertain if no geology is related to elastic response of the subsurface rocks. The purpose of this thesis is to integrate diagenesis data from log and core data, rock physics models, AVO analysis and seismic inversion information to characterize the Agua Fría 3D seismic cube. Mechanical compaction and sorting are the main factors affecting the porosity trend in the selected wells according to the rock physics modeling. AVO class III are the main class present in the study area. However, these responses can be related to brine, oil or gas sands. Rock physics templates and seismic inversion data are useful to understand these responses and to decrease uncertainty to the analysis of these anomalies.The integration of these methodologies allow to improve the understanding of the seismic amplitude response to different geological facies present in the study area.
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Harrison, Christopher Bernard. "Feasibility of rock characterization for mineral exploration using seismic data." Curtin University of Technology, Western Australia School of Mines, Department of Exploration Geophysics, 2009. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=129417.

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The use of seismic methods in hard rock environments in Western Australia for mineral exploration is a new and burgeoning technology. Traditionally, mineral exploration has relied upon potential field methods and surface prospecting to reveal shallow targets for economic exploitation. These methods have been and will continue to be effective but lack lateral and depth resolution needed to image deeper mineral deposits for targeted mining. With global need for minerals, and gold in particular, increasing in demand, and with shallower targets harder to find, new methods to uncover deeper mineral reserves are needed. Seismic reflection imaging, hard rock borehole data analysis, seismic inversion and seismic attribute analysis all give the spatial and volumetric exploration techniques the mineral industry can use to reveal high value deeper mineral targets.
In 2002, two high resolution seismic lines, the East Victory and Intrepid, were acquired along with sonic logging, to assess the feasibility of seismic imaging and rock characterisation at the St. Ives gold camp in Western Australia. An innovative research project was undertaken combining seismic processing, rock characterization, reflection calibration, seismic inversion and seismic attribute analysis to show that volumetric predictions of rock type and gold-content may be viable in hard rock environments. Accurate seismic imaging and reflection identification proved to be challenging but achievable task in the all-out hard rock environment of the Yilgarn craton. Accurate results were confounded by crocked seismic line acquisition, low signal-to-noise ratio, regolith distortions, small elastic property variations in the rock, and a limited volume of sonic logging. Each of these challenges, however, did have a systematic solution which allowed for accurate results to be achieved.
Seismic imaging was successfully completed on both the East Victory and Intrepid data sets revealing complex structures in the Earth as shallow as 100 metres to as deep as 3000 metres. The successful imaging required homogenization of the regolith to eliminate regolith travel-time distortions and accurate constant velocity analysis for reflection focusing using migration. Verification of the high amplitude reflections within each image was achieved through integration of surface geological and underground mine data as well as calibration with log derived synthetic seismograms. The most accurate imaging results were ultimately achieved on the East Victory line which had good signal-to-noise ratio and close-to-straight data acquisition direction compared to the more crooked Intrepid seismic line.
The sonic logs from both the East Victory and Intrepid seismic lines were comprehensively analysed by re-sampling and separating the data based on rock type, structure type, alteration type, and Au assay. Cross plotting of the log data revealed statistically accurate separation between harder and softer rocks, as well as sheared and un-sheared rock, were possible based solely on compressional-wave, shear-wave, density, acoustic and elastic impedance. These results were used successfully to derive empirical relationships between seismic attributes and geology. Calibrations of the logs and seismic data provided proof that reflections, especially high-amplitude reflections, correlated well with certain rock properties as expected from the sonic data, including high gold content sheared zones. The correlation value, however, varied with signal-to-noise ratio and crookedness of the seismic line. Subsequent numerical modelling confirmed that separating soft from hard rocks can be based on both general reflectivity pattern and impedance contrasts.
Indeed impedance inversions on the calibrated seismic and sonic data produced reliable volumetric separations between harder rocks (basalt and dolerite) and softer rock (intermediate intrusive, mafic, and volcaniclastic). Acoustic impedance inversions produced the most statistically valid volumetric predictions with the simultaneous use of acoustic and elastic inversions producing stable separation of softer and harder rocks zones. Similarly, Lambda-Mu-Rho inversions showed good separations between softer and harder rock zones. With high gold content rock associated more with “softer” hard rocks and sheared zones, these volumetric inversion provide valuable information for targeted mining. The geostatistical method applied to attribute analysis, however, was highly ambiguous due to low correlations and thus produced overly generalized predictions. Overall reliability of the seismic inversion results were based on quality and quantity of sonic data leaving the East Victory data set, again with superior results as compared to the Intrepid data set.
In general, detailed processing and analysis of the 2D seismic data and the study of the relationship between the recorded wave-field and rock properties measured from borehole logs, core samples and open cut mining, revealed that positive correlations can be developed between the two. The results of rigorous research show that rock characterization using seismic methodology will greatly benefit the mineral industry.
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Rimstad, Kjartan. "Bayesian Seismic Lithology/Fluid Inversion Constrained by Rock Physics Depth Trends." Thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9772.

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In this study we consider 2D seismic lithology/fluid inversion constrained by rock physics depth trends and a prior lithology/fluid Markov random field. A stochastic relation from porosity and lithology/fluid to seismic observations is established. The inversion is done in a Bayesian framework with an approximate posterior distribution. Block Gibbs samplers are used to estimate the approximate posterior distribution. Two different inversion algorithms are established, one with the support of well observations and one without. Both inversion algorithms are tested on a synthetic reservoir and the algorithm with well observations is also tested on a data set from the North Sea. The classification results with both algorithms are good. Without the support of well observations it is problematic to estimate the level of the porosity trends, however the classification results are approximately translation invariant with respect to porosity trends.

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Spikes, Kyle Thomas. "Probabilistic seismic inversion based on rock-physics models for reservoir characterization /." May be available electronically:, 2008. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

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Books on the topic "Rock physics; Seismic data"

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Avseth, Per. Quantitative seismic interpretation: Applying rock physics tools to reduce interpretation risk. Cambridge: Cambridge University Press, 2010.

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Mavko, Gary. The rock physics handbook: Tools for seismic analysis in porous media. Cambridge: Cambridge University Press, 1998.

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1965-, Mukerji Tapan, and Dvorkin Jack 1953-, eds. The rock physics handbook: Tools for seismic analysis of porous media. 2nd ed. Cambridge: Cambridge University Press, 2009.

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Makuch, A. Design of a new macroseismic monitoring system. Ottawa: Minister of Supply and Services Canada, 1987.

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Dell'Aversana, Paolo. Integrated Geophysical Models - Combining Rock Physics with Seismic, Electromagnetic and Gravity Data. EAGE Publications bv, 2014. http://dx.doi.org/10.3997/9789073834927.

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Ebook: Integrated Geophysical Models - Combining Rock Physics with Seismic, Electromagnetic and Gravity Data. EAGE Publications bv, 2014. http://dx.doi.org/10.3997/9789462820067.

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Bregman, Nina Diane. Tomographic inversion of crosshole seismic data. 1987.

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1938-, Kozák Jan, Waniek Ludvik 1930-, Československá akademie věd. Geofysikální ústav., and Symposium on Physics of Fracturing and Seismic Energy Release (1985 : Liblice Manor), eds. Physics of fracturing and seismic energy release. Basel: Birkhauser Verlag, 1987.

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Quantitative Seismic Interpretation: Applying Rock Physics Tools to Reduce Interpretation Risk. Cambridge University Press, 2005.

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Mukerji, Tapan, Jack Dvorkin, and Gary Mavko. The Rock Physics Handbook: Tools for Seismic Analysis of Porous Media. Cambridge University Press, 2003.

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Book chapters on the topic "Rock physics; Seismic data"

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Avseth, Per, Tapan Mukerji, Gary Mavko, and Ezequiel Gonzalez. "Integrating statistical rock physics and sedimentology for quantitative seismic interpretation." In Subsurface Hydrology: Data Integration for Properties and Processes, 45–60. Washington, D. C.: American Geophysical Union, 2007. http://dx.doi.org/10.1029/171gm06.

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Danaei, Shahram, and Deva Ghosh. "Sensitivity Analysis of Sandstone Rock Elastic Properties to Effective Pressure Using a New Rock Physics Workflow and Its Application for Time-Lapse Seismic Data Analysis." In ICIPEG 2016, 45–59. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3650-7_4.

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Nanda, Niranjan C. "Seismic Wave and Rock-Fluid Properties." In Seismic Data Interpretation and Evaluation for Hydrocarbon Exploration and Production, 3–23. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75301-6_1.

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Vasudevan, K. "Advanced Seismic Reservoir Characterization of Carbonate Reservoirs: A Case Study." In Petro-physics and Rock Physics of Carbonate Reservoirs, 191–205. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1211-3_14.

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Nanda, Niranjan C. "Seismic Wave Propagation and Rock-Fluid Properties." In Seismic Data Interpretation and Evaluation for Hydrocarbon Exploration and Production, 3–17. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-26491-2_1.

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Glazer, S. N. "Applications of Seismic Monitoring in Combating Rock Burst Hazard." In Mine Seismology: Data Analysis and Interpretation, 9–29. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32612-2_2.

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Baud, P., S. Vinciguerra, C. David, A. Cavallo, E. Walker, and T. Reuschlé. "Compaction and Failure in High Porosity Carbonates: Mechanical Data and Microstructural Observations." In Rock Physics and Natural Hazards, 869–98. Basel: Birkhäuser Basel, 2009. http://dx.doi.org/10.1007/978-3-0346-0122-1_7.

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Jackson, Ian, and M. S. Paterson. "A High-pressure, High-temperature Apparatus for Studies of Seismic Wave Dispersion and Attenuation." In Experimental Techniques in Mineral and Rock Physics, 445–66. Basel: Birkhäuser Basel, 1993. http://dx.doi.org/10.1007/978-3-0348-5108-4_12.

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Boon, C. W., M. Lazari, and S. Utili. "A New Rock Slicing Algorithm with Reduced Data Structure for Discrete Element Method Analyses for Rock Mechanics." In Springer Proceedings in Physics, 863–70. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1926-5_90.

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Singh, N. P., S. P. Maurya, and Kumar Hemant Singh. "Petrophysical Characterization of Sandstone Reservoir from Well Log Data: A Case Study from South Tapti Formation, India." In Petro-physics and Rock Physics of Carbonate Reservoirs, 251–65. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1211-3_18.

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Conference papers on the topic "Rock physics; Seismic data"

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Shen, Yi, and Jack Dvorkin. "Using rock physics to improve Qp quantification in seismic data." In SEG Technical Program Expanded Abstracts 2016. Society of Exploration Geophysicists, 2016. http://dx.doi.org/10.1190/segam2016-13847246.1.

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Mukherjee, A., G. Nyein, K. Elsadany, I. Kiyotaka, E. Shimpei, and I. Shunsuke. "Validation by Pre-stack Inversion of an Optimized Seismic Data Pre-conditioning Processing Sequence- Case Study from UAE." In Fourth EAGE Workshop on Rock Physics. Netherlands: EAGE Publications BV, 2017. http://dx.doi.org/10.3997/2214-4609.201702455.

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Li, Y., B. Biondi, G. Mavko, and D. Nichols. "Integrated VTI Model Building with Seismic, Geological and Rock Physics Data." In 77th EAGE Conference and Exhibition 2015. Netherlands: EAGE Publications BV, 2015. http://dx.doi.org/10.3997/2214-4609.201413046.

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Lang, Xiaozheng, and Dario Grana. "Bayesian petrophysics inversion of seismic data based on linearized seismic and rock physics modeling." In SEG Technical Program Expanded Abstracts 2017. Society of Exploration Geophysicists, 2017. http://dx.doi.org/10.1190/segam2017-17588921.1.

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Avseth*, Per, and Ivan Lehocki. "Quantitative interpretation of rock stiffness and hydrocarbon softening from seismic inversion data using rock physics templates." In SEG Technical Program Expanded Abstracts 2015. Society of Exploration Geophysicists, 2015. http://dx.doi.org/10.1190/segam2015-5839092.1.

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Mukherjee, A., H. Xu, and N. C. Dutta. "Use of Rock Physics Principles for Inversion of Pre-Stack Seismic Data." In 68th EAGE Conference and Exhibition incorporating SPE EUROPEC 2006. European Association of Geoscientists & Engineers, 2006. http://dx.doi.org/10.3997/2214-4609.201402001.

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Chatterjee, Shraddha, Tanya Colwell, Jon Downton, Olivia Collet, and Beth Rees. "Rock Physics Guided Data Science Technique to Improve Neural Network Training to Accurately Quantify Rock Properties from Seismic Data." In Abu Dhabi International Petroleum Exhibition & Conference. Society of Petroleum Engineers, 2020. http://dx.doi.org/10.2118/202685-ms.

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Brevik, I., Pål T. Gabrielsen, and Jan Petter Morten. "The role of EM rock physics and seismic data in integrated 3D CSEM data analysis." In SEG Technical Program Expanded Abstracts 2009. Society of Exploration Geophysicists, 2009. http://dx.doi.org/10.1190/1.3255881.

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Bachrach, Ran, and Amos Nur. "Ultra shallow seismic reflection in unconsolidated sediments: Rock physics base for data acquisition." In SEG Technical Program Expanded Abstracts 1998. Society of Exploration Geophysicists, 1998. http://dx.doi.org/10.1190/1.1820624.

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Spikes, Kyle. "Thickness estimates of heterogeneous reservoirs using seismic data, rock physics, and wavelet transforms." In SEG Technical Program Expanded Abstracts 2009. Society of Exploration Geophysicists, 2009. http://dx.doi.org/10.1190/1.3255212.

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Reports on the topic "Rock physics; Seismic data"

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Mavko, G. Final Technical Report DE-FG02-99ER14933 Inversion of multicomponent seismic data and rock physics interpretation. Office of Scientific and Technical Information (OSTI), March 2006. http://dx.doi.org/10.2172/877424.

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Ilya Tsvankin and Kenneth L. Larner. Inversion of multicomponent seismic data and rock-physics intepretation for evaluating lithology, fracture and fluid distribution in heterogeneous anisotropic reservoirs. Office of Scientific and Technical Information (OSTI), November 2004. http://dx.doi.org/10.2172/834389.

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Amos Nur. Seismic-Scale Rock Physics of Methane Hydrate. Office of Scientific and Technical Information (OSTI), January 2009. http://dx.doi.org/10.2172/945215.

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Gary Mavko. SEISMIC AND ROCK PHYSICS DIAGNOSTICS OF MULTISCALE RESERVOIR TEXTURES. Office of Scientific and Technical Information (OSTI), October 2003. http://dx.doi.org/10.2172/822709.

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Gary Mavko. SEISMIC AND ROCK PHYSICS DIAGNOSTICS OF MULTISCALE RESERVOIR TEXTURES. Office of Scientific and Technical Information (OSTI), June 2003. http://dx.doi.org/10.2172/822710.

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Gary Mavko. SEISMIC AND ROCK PHYSICS DIAGNOSTICS OF MULTISCALE RESERVOIR TEXTURES. Office of Scientific and Technical Information (OSTI), May 2002. http://dx.doi.org/10.2172/822711.

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Gary Mavko. SEISMIC AND ROCK PHYSICS DIAGNOSTICS OF MULTISCALE RESERVOIR TEXTURES. Office of Scientific and Technical Information (OSTI), June 2003. http://dx.doi.org/10.2172/822712.

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Gary Mavko. SEISMIC AND ROCK PHYSICS DIAGNOSTICS OF MULTISCALE RESERVOIR TEXTURES. Office of Scientific and Technical Information (OSTI), June 2003. http://dx.doi.org/10.2172/822713.

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Gary Mavko. SEISMIC AND ROCK PHYSICS DIAGNOSTICS OF MULTISCALE RESERVOIR TEXTURES. Office of Scientific and Technical Information (OSTI), August 2004. http://dx.doi.org/10.2172/834112.

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Gary Mavko. Multi-Attribute Seismic/Rock Physics Approach to Characterizing Fractured Reservoirs. Office of Scientific and Technical Information (OSTI), November 2004. http://dx.doi.org/10.2172/927585.

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