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Li, Yongyi, Lev Vernik, Mark Chapman, and Joel Sarout. "Introduction to this special section: Rock physics." Leading Edge 38, no. 5 (May 2019): 332. http://dx.doi.org/10.1190/tle38050332.1.
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Rock physics links the physical properties of rocks to geophysical and petrophysical observations and, in the process, serves as a focal point in many exploration and reservoir characterization studies. Today, the field of rock physics and seismic petrophysics embraces new directions with diverse applications in estimating static and dynamic reservoir properties through time-variant mechanical, thermal, chemical, and geologic processes. Integration with new digital and computing technologies is gradually gaining traction. The use of rock physics in seismic imaging, prestack seismic analysis, seismic inversion, and geomechanical model building also contributes to the increase in rock-physics influence. This special section highlights current rock-physics research and practices in several key areas, namely experimental rock physics, rock-physics theory and model studies, and the use of rock physics in reservoir characterizations.
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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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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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Saenger, Erik H., Stephanie Vialle, Maxim Lebedev, David Uribe, Maria Osorno, Mandy Duda, and Holger Steeb. "Digital carbonate rock physics." Solid Earth 7, no. 4 (August 4, 2016): 1185–97. http://dx.doi.org/10.5194/se-7-1185-2016.
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Abstract. Modern estimation of rock properties combines imaging with advanced numerical simulations, an approach known as digital rock physics (DRP). In this paper we suggest a specific segmentation procedure of X-ray micro-computed tomography data with two different resolutions in the µm range for two sets of carbonate rock samples. These carbonates were already characterized in detail in a previous laboratory study which we complement with nanoindentation experiments (for local elastic properties). In a first step a non-local mean filter is applied to the raw image data. We then apply different thresholds to identify pores and solid phases. Because of a non-neglectable amount of unresolved microporosity (micritic phase) we also define intermediate threshold values for distinct phases. Based on this segmentation we determine porosity-dependent values for effective P- and S-wave velocities as well as for the intrinsic permeability. For effective velocities we confirm an observed two-phase trend reported in another study using a different carbonate data set. As an upscaling approach we use this two-phase trend as an effective medium approach to estimate the porosity-dependent elastic properties of the micritic phase for the low-resolution images. The porosity measured in the laboratory is then used to predict the effective rock properties from the observed trends for a comparison with experimental data. The two-phase trend can be regarded as an upper bound for elastic properties; the use of the two-phase trend for low-resolution images led to a good estimate for a lower bound of effective elastic properties. Anisotropy is observed for some of the considered subvolumes, but seems to be insignificant for the analysed rocks at the DRP scale. Because of the complexity of carbonates we suggest using DRP as a complementary tool for rock characterization in addition to classical experimental methods.
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Hunter, Sander, Ronny Hofmann, and Irene Espejo. "Integrating grain-scale geology in digital rock physics." Leading Edge 37, no. 6 (June 2018): 428–34. http://dx.doi.org/10.1190/tle37060428.1.
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Digital rock physics (DRP) is a rapidly evolving field of study. One component of digital rock that has not received sufficient attention is how well actual rocks are represented in DRP. Instead, the digital rock community is focused on characterizing the pore space in volumes of rock imaged by microcomputed tomography (micro-CT) and simulating flow through that digitized pore network. This enables computational simulations of routine core analysis measurements, which may be completed in hours instead of days or weeks. Although this alone makes digital rock a worthwhile endeavor, it overlooks much of the detailed textural and compositional information stored within digital rock images below the resolution of micro-CT imaging. This information may be observed in high-resolution 2D transmitted light microscopy images. Textural information impacts not only the tortuosity of the flow path, impacting permeability, but also influences how the rock will respond to stress. Compositional information could also be extracted to not only better characterize the wettability of rocks for relative permeability simulations, but also to supplement petrographic information in diagenetic modeling, among other applications. Ultimately, a full characterization of a digital rock should replicate the acoustic, geomechanical, and petrophysical properties of the imaged sample. The first step toward achieving full digital simulation of rock properties is the fundamental characterization of the sample — extracting the textural and compositional information from digital rock images. Unfortunately, this is a nontrivial undertaking. It involves acquiring sample images, segmenting pores from individual rock minerals, separating these minerals into individual grains and cements, and computing multiple attributes from the segmented grains. To address this issue, we are developing a workflow to compute key textural attributes from images with a long-term vision for the incorporation of geologic characterization into DRP using machine learning.
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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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Ball, Vaughn, J. P. Blangy, Christian Schiott, and Alvaro Chaveste. "Relative rock physics." Leading Edge 33, no. 3 (March 2014): 276–86. http://dx.doi.org/10.1190/tle33030276.1.
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Handoyo, Handoyo, Fatkhan Fatkhan, Fourier D. E. Latief, and Harnanti Y. Putri. "Estimation of Rock Physical Parameters Based on Digital Rock Physics Image, Case Study: Blok Cepu Oil Field, Central Java, Indonesia." Jurnal Geofisika 16, no. 1 (March 22, 2018): 21. http://dx.doi.org/10.36435/jgf.v16i1.53.
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Modern technique to estimate of the physical properties of rocks can be done by means of digital imagingand numerical simulation, an approach known as digital rock physics (DRP: Digital Rock Physics). Digital rockphysics modeling is useful to understand microstructural parameters of rocks (pores and rock matrks), quite quickly and in detail. In this paper a study was conducted on sandstone reservoir samples in a rock formation. The core of sandstone samples were calculated porosity, permeability, and elasticity parameters in the laboratory. Then performed digital image processing using CT-Scan that utilizes X-ray tomography. The result of digital image is processed and done by calculation of digital simulation to calculate porosity, permeability, and elastic parameter of sandstones. In addition, there are also predictions of p-wave velocity and wave -S using the empirical equations given by Han (1986), Raymer (1990), and Nur (1998). The results of digital simulation (DRP) in this study provide a higher than the calculations in the laboratory. The digital rock physicsmethod (DRP) combined with rock physics modeling can be a practical and rapid method for determining the rock properties of tiny (microscopic) rock fragments
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Avseth, Per, Tor Arne Johansen, Aiman Bakhorji, and Husam M. Mustafa. "Rock-physics modeling guided by depositional and burial history in low-to-intermediate-porosity sandstones." GEOPHYSICS 79, no. 2 (March 1, 2014): D115—D121. http://dx.doi.org/10.1190/geo2013-0226.1.
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We present a new rock-physics modeling approach to describe the elastic properties of low-to-intermediate-porosity sandstones that incorporates the depositional and burial history of the rock. The studied rocks have been exposed to complex burial and diagenetic history and show great variability in rock texture and reservoir properties. Our approach combines granular medium contact theory with inclusion-based models to build rock-physics templates that take into account the complex burial history of the rock. These models are used to describe well log data from tight gas sandstone reservoirs in Saudi Arabia, and successfully explain the pore fluid, rock porosity, and pore shape trends in these complex reservoirs.
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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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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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Carcione, José M., and Per Avseth. "Rock-physics templates for clay-rich source rocks." GEOPHYSICS 80, no. 5 (September 2015): D481—D500. http://dx.doi.org/10.1190/geo2014-0510.1.
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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.
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Sidiq, Irsyad Nuruzzaman, and Thaqibul Fikri Niyartama. "Porosity Identification of Carbonate Core Reservoir Using Digital Rock Physics Method." Proceeding International Conference on Science and Engineering 1 (October 31, 2017): 175–81. http://dx.doi.org/10.14421/icse.v1.297.
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Indonesia is an archipelago country so rich with coral reefs that are a major component of the carbonate rock constituents. Where as much as 40% of carbonate rocks in Indonesia is a hydrocarbon reservoir is still rarely done exploration. This is because conventional hydrocarbon exploration technology has not been able to provide detailed information about the physical quantities. Hydrocarbon exploration technologies currently leads on digital technology to know the physical quantities of a reservoir of more detail such as porosity. Porosity which is physical quantities related to the presence of hydrocarbons in the pores of rocks. Digital Rock Physics (DRP) is a digital image-based method as an alternative method to find the physical quantities of rock samples to make it more effective and efficient. This study aims to identify the physical quantity using the method of porosity of the DRP until obtaining porosity of rock core carbonate reservoir by analyzing the binary image of the two rock cores from the same reservoir but has different dimensions to find out the exact core rocks to analyze the value of porosity. Binary image obtained from a scanned image of a projection of rock that has been reconstructed to become the greyscale image and have gone through the process of thresholding. The results of this study showed that the method can identify the physical quantities of DRP porosity and non-damaging rock pore structure (non-destructive). Analysis of the porosity of the rock core with histogram variations performed (by adjustingting the histogram), using the otsu method of thresholding and pixel size of the image has high (5.343750 μm) used to analyze the value of porosity. The porosity values acquired for 18.040 and has precision 96.20%.
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Carpenter, Chris. "Rock Physics Model Provides Insight Into Reservoir Characterization." Journal of Petroleum Technology 74, no. 02 (February 1, 2022): 78–80. http://dx.doi.org/10.2118/0222-0078-jpt.
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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 30359, “Reservoir Characterization Supported by Rock Physics Diagnostics,” by Stephan Gelinsky, Shell. The paper has not been peer reviewed. Copyright 2021 Offshore Technology Conference. Reproduced by permission. Rock physics models are often perceived as too complicated and dependent on too many difficult-to-measure parameters to be of practical use for reservoir characterization. In the complete paper, the author demonstrates how careful calibration of a common and simple rock physics model can provide valuable insights into the reservoir and seal elastic properties such as function of porosity, fluid, and mineral properties and net-to-gross. The model can be used for quantitative seismic interpretation of reservoirs with variable net-to-gross and porosity away from well control. Methodology Rock physics describes the application of fundamental physical principles to establish the elastic properties of porous rocks as a function of their mineral composition, pore fill, and microstructure and texture. Once a suitable rock physics model is calibrated locally, the use of seismic data to estimate reservoir rock and fluid properties from the elastic rock properties is possible—specifically, from the observed seismic amplitudes and if seismic data were inverted from the seismically derived acoustic and shear impedances. The Hashin-Shtrikman (HS) effective medium bounds can be used to characterize both sandstones and shales. While the application of this model for clean sandstones is straightforward and, thus, is not included in this synopsis, the application for shales is more complex.
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Denney, Dennis. "Carbonate-Rock-Physics Issues." Journal of Petroleum Technology 62, no. 03 (March 1, 2010): 73–74. http://dx.doi.org/10.2118/0310-0073-jpt.
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Thompson, A. H. "Fractals in Rock Physics." Annual Review of Earth and Planetary Sciences 19, no. 1 (May 1991): 237–62. http://dx.doi.org/10.1146/annurev.ea.19.050191.001321.
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Haneberg, W. C. "The Rock Physics Handbook." Environmental & Engineering Geoscience V, no. 4 (December 1, 1999): 489–90. http://dx.doi.org/10.2113/gseegeosci.v.4.489.
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Dræge, Anders. "Constrained rock physics modeling." Leading Edge 28, no. 1 (January 2009): 76–80. http://dx.doi.org/10.1190/1.3064149.
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Gutierrez, Mario A., Jack Dvorkin, and Amos Nur. "Stratigraphy-guided rock physics." Leading Edge 21, no. 1 (January 2002): 98–103. http://dx.doi.org/10.1190/1.1445859.
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Nababan, Benyamin Elilaski, Harnanti Yogaputri Hutami, and Fatkhan Fatkhan. "The Feasibility Study of Reservoir Geomechanics from Brittleness Evaluation." Scientific Contributions Oil and Gas 45, no. 1 (April 1, 2022): 10–20. http://dx.doi.org/10.29017/scog.45.1.920.
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A detailed understanding regarding the rocks Brittleness Index is helpful in oil and gas exploration as upfront information to determine the rock fracture gradient. Researchers have proposed several methods to estimate the rock Brittleness Index. However, different ways may yield different results and lead to varying interpretations regarding the Brittleness Index classifi cation. This paper evaluates the Brittleness Index of an Indonesian gas well using three approaches based on the elastic properties log data, elastic properties rock physics modeling, and mineralogical rock physics modeling to assess the consistency of the methods. The results obtained in this study suggest that elastic properties-based and mineralogical methods produced a consistent Brittleness Index. However, the vertical resolution is different. It indicates that the Brittleness Index estimated from the actual log data showed higher resolution than the Brittleness Index calculated from the rock physics modeling. Combining TOC data with the Brittleness Index is recommended to optimize hydraulic fracturing design and planning. For further investigation, the authors will be suggesting direct sampling from cores and laboratory measurements to obtain the in-situ mechanical properties of shale rocks.
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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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Uribe, David, Erik H. Saenger, and Holger Steeb. "Digital Rock Physics: A case study of carbonate rocks." PAMM 16, no. 1 (October 2016): 399–400. http://dx.doi.org/10.1002/pamm.201610188.
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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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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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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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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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Vanorio, Tiziana. "Recent advances in time-lapse, laboratory rock physics for the characterization and monitoring of fluid-rock interactions." GEOPHYSICS 80, no. 2 (March 1, 2015): WA49—WA59. http://dx.doi.org/10.1190/geo2014-0202.1.
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Monitoring thermo-chemo-mechanical processes geophysically — e.g., fluid disposal or storage, thermal and chemical stimulation of reservoirs, or natural fluids simply entering a new system — raises numerous concerns because of the likelihood of fluid-rock chemical interactions and our limited ability to decipher the geophysical signature of coupled processes. One of the missing links is understanding the evolution of seismic properties together with reactive transport because rock properties evolve as a result of chemical reactions and vice versa. Capturing this coupling experimentally is one of the missing elements in the existing literature. This paper describes recent advances in rock-physics experiments to understand the effects of dissolution-induced compaction on acoustic velocity, porosity, and permeability. This paper has a dual aim: understanding the mechanisms underlying permanent modifications to the rock microstructure and providing a richer set of experimental information to inform the formulation of new simulations and rock modeling. Data observation included time-lapse experiments and imaging tracking transport and elastic properties, the rock microstructure, and the pH and chemical composition of the fluid permeating the rock. Results show that the removal of high surface area, mineral phases such as microcrystalline calcite and clay appears to be mostly responsible for dissolution-induced compaction. Nevertheless, it was the original rock microstructure and its response to stress that ultimately defined how solution-transfer and rock compaction feed back upon each other. The change in pore volume to the applied stress, the permeability characterizing the formation, and the reactive transport of phases characterized by a high surface area were strongly coupled during injection, controlling how velocity evolved. In less stiff rocks, rock-fluid interactions led to grain-slip-driven compaction and a consequent decrease in velocity. In tight and stiff rocks, rock-fluid interactions led to minimal compaction, a larger increase in permeability, and crack opening. Nevertheless, the change in velocity of these tight rocks was almost negligible.
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Yi, Shengbo, Shulin Pan, Hengyu Zuo, Yinghe Wu, Guojie Song, and Qiyong Gou. "Research on Rock Physics Modeling Methods for Fractured Shale Reservoirs." Energies 16, no. 1 (December 25, 2022): 226. http://dx.doi.org/10.3390/en16010226.
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The Sichuan Basin is a significant region for exploration and development of shale gas in China, and it is essential to clarify the impact of deep shale gas reservoir parameters on cost-effective development at scale to ensure national energy security. Rock physics modeling is a significant means of communicating the physical and elastic parameters of rocks. A rock physics modeling method applicable to fractured shale gas reservoirs is proposed for the current situation of complex fluid relationships in shale gas reservoirs and unclear characteristics of gas identification seismic response. In this paper, based on the Self Consistent Approximation (SCA) model and the differential effective medium (DEM) model, the anisotropic source of shale is used as a starting point to add bound water, kerogen, clay, and brittle minerals, the Schoenberg linear slip theory is used to add fracture disturbance effects, and then the Brown–Korringa model is used to perform fluid replacement under anisotropic conditions. Finally a rock physics model applicable to fractured shale gas reservoirs is obtained, and the established rock physics model is used for analysis of elastic parameters, Thomsen parameters, and fracture weakness parameters. Rock physics tests were performed on shale in southern Sichuan as an example. The experimental results show that the model established by the process can accurately invert the longitudinal and transverse wave velocities of the shale, which can provide a conceptual basis for the study of fractured shale gas reservoirs.
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Sengupta, Mita, and Shannon L. Eichmann. "Computing elastic properties of organic-rich source rocks using digital images." Leading Edge 40, no. 9 (September 2021): 662–66. http://dx.doi.org/10.1190/tle40090662.1.
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Digital rocks are 3D image-based representations of pore-scale geometries that reside in virtual laboratories. High-resolution 3D images that capture microstructural details of the real rock are used to build a digital rock. The digital rock, which is a data-driven model, is used to simulate physical processes such as fluid flow, heat flow, electricity, and elastic deformation through basic laws of physics and numerical simulations. Unconventional reservoirs are chemically heterogeneous where the rock matrix is composed of inorganic minerals, and hydrocarbons are held in the pores of thermally matured organic matter, all of which vary spatially at the nanoscale. This nanoscale heterogeneity poses challenges in measuring the petrophysical properties of source rocks and interpreting the data with reference to the changing rock structure. Focused ion beam scanning electron microscopy is a powerful 3D imaging technique used to study source rock structure where significant micro- and nanoscale heterogeneity exists. Compared to conventional rocks, the imaging resolution required to image source rocks is much higher due to the nanoscale pores, while the field of view becomes smaller. Moreover, pore connectivity and resulting permeability are extremely low, making flow property computations much more challenging than in conventional rocks. Elastic properties of source rocks are significantly more anisotropic than those of conventional reservoirs. However, one advantage of unconventional rocks is that the soft organic matter can be captured at the same imaging resolution as the stiff inorganic matrix, making digital elasticity computations feasible. Physical measurement of kerogen elastic properties is difficult because of the tiny sample size. Digital rock physics provides a unique and powerful tool in the elastic characterization of kerogen.
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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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Nababan, Benyamin Elilaski, Eliza Veronica Zanetta, Nahdah Novia, and Handoyo Handoyo. "ESTIMASI NILAI POROSITAS DAN PERMEABILITAS DENGAN PENDEKATAN DIGITAL ROCK PHYSICS (DRP) PADA SAMPEL BATUPASIR FORMASI NGRAYONG, CEKUNGAN JAWA TIMUR BAGIAN UTARA." Jurnal Geofisika Eksplorasi 5, no. 3 (January 17, 2020): 34–44. http://dx.doi.org/10.23960/jge.v5i3.34.
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Reservoir rock permeability and porosity are physical properties of rocks that control reservoir quality. Conventionally, rock porosity and permeability values are obtained from measurements in the laboratory or through well logs. At present, calculation of porosity and permeability can be calculated using digital image processing / Digital Rock Physics (DRP). Core data samples are processed by X-ray diffraction using CT-micro-tomography scan. The result is an image model of the core sample, 2D and 3D images. The combination of theoretical processing and digital images can be obtained from the value of porosity and permeability of rock samples. In this study, we calculated porosity and permeability values using the Digital Rock Physics (DRP) approach in sandstone samples from the Ngrayong Formation, North East Java Basin. The results of the digital image simulation and processing on the Ngrayong Formation sandstone samples ranged in value from 33.50% and permeability around 1267.02 mDarcy.
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Wang, Lin, Feng Zhang, Xiang-Yang Li, Bang-Rang Di, and Lian-Bo Zeng. "Quantitative seismic interpretation of rock brittleness based on statistical rock physics." GEOPHYSICS 84, no. 4 (July 1, 2019): IM63—IM75. http://dx.doi.org/10.1190/geo2018-0094.1.
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Rock brittleness is one of the important properties for fracability evaluation, and it can be represented by different physical properties. The mineralogy-based brittleness index (BIM) builds a simple relationship between mineralogy and brittleness, but it may be ambiguous for rocks with a complex microstructure; whereas the elastic moduli-based brittleness index (BIE) is applicable in the field, but BIE interpretation needs to be constrained by lithofacies information. We have developed a new workflow for quantitative seismic interpretation of rock brittleness: Lithofacies are defined by a criterion combining BIM and BIE for comprehensive brittleness evaluation; statistical rock-physics methods are applied for quantitative interpretation by using inverted elastic parameters; acoustic impedance and elastic impedance are selected as the optimized pair of attributes for lithofacies classification. To improve the continuity and accuracy of the interpreted results, a Markov random field is applied in the Bayesian rule as the spatial constraint. A 2D synthetic test demonstrates the feasibility of the Bayesian classification with a Markov random field. This new interpretation framework is also applied to a shale reservoir formation from China. Comparison analysis indicates that brittle shale sections can be efficiently discriminated from ductile shale sections and tight sand sections by using the inverted elastic parameters.
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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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Azevedo, Leonardo, Dario Grana, and Catarina Amaro. "Geostatistical rock physics AVA inversion." Geophysical Journal International 216, no. 3 (November 30, 2018): 1728–39. http://dx.doi.org/10.1093/gji/ggy511.
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Vernik, Lev, and Jadranka Milovac. "Rock physics of organic shales." Leading Edge 30, no. 3 (March 2011): 318–23. http://dx.doi.org/10.1190/1.3567263.
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Capello de P., Maria Angela, and Michael Batzle. "Rock physics in seismic monitoring." Leading Edge 16, no. 9 (September 1997): 1255–60. http://dx.doi.org/10.1190/1.1437774.
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Dvorkin, Jack. "Self-similarity in rock physics." Leading Edge 26, no. 8 (August 2007): 946–50. http://dx.doi.org/10.1190/1.2775996.
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Prasad, Manika, Arpita Pal-Bathija, Merrick Johnston, Marisa Rydzy, and Mike Batzle. "Rock physics of the unconventional." Leading Edge 28, no. 1 (January 2009): 34–38. http://dx.doi.org/10.1190/1.3064144.
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Dvorkin, Jack, Naum Derzhi, Elizabeth Diaz, and Qian Fang. "Relevance of computational rock physics." GEOPHYSICS 76, no. 5 (September 2011): E141—E153. http://dx.doi.org/10.1190/geo2010-0352.1.
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To validate the transport (fluid and electrical) and elastic properties computed on CT scan pore-scale volumes of natural rock, we first contrast these values to physical laboratory measurements. We find that computational and physical data obtained on the same rock material source often differ from each other. This mismatch, however, does not preclude the validity of either of the data type — it only implies that expecting a direct match between the effective properties of two volumes of very different sizes taken from the same heterogeneous material is generally incorrect. To address this situation, instead of directly comparing data points generated by different methods of measurement, we compare trends formed by such data points. These trends include permeability versus porosity; electrical formation factor versus porosity; and elastic moduli (elastic-wave velocity) versus porosity. In the physical laboratory, these trends are generated by measuring a significant number of samples. In contrast, in the computational laboratory, these trends are often hidden inside a very small digital sample and can be derived by subsampling it. Hence, we base our validation paradigm on the assumption that if these computational trends match relevant physical trends and/or theoretical rock physics transforms, the computational results are correct. We present examples of such validation for clastic and carbonate samples, including drill cuttings.
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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.
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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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Wang, Zhijing (Zee). "Fundamentals of seismic rock physics." GEOPHYSICS 66, no. 2 (March 2001): 398–412. http://dx.doi.org/10.1190/1.1444931.
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During the past 50 years or so, tremendous progress has been made in studying physical properties of rocks and minerals in relation to seismic exploration and earthquake seismology. During this period, many theories have been developed and many experiments have been carried out. Some of these theories and experimental results have played important roles in advancing earth sciences and exploration technologies. This tutorial paper attempts to summarize some of these results.
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Ba, Jing, Hesong Zhu, Li-Yun Fu, and Luanxiao Zhao. "Challenges in seismic rock physics." Journal of Geophysics and Engineering 19, no. 6 (November 7, 2022): 1367–69. http://dx.doi.org/10.1093/jge/gxac094.
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Mavko, Gary, and Nishank Saxena. "Rock-physics models for heterogeneous creeping rocks and viscous fluids." GEOPHYSICS 81, no. 4 (July 2016): D427—D440. http://dx.doi.org/10.1190/geo2015-0531.1.
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Rock-physics models are used to explore how small-scale heterogeneity can affect the larger scale viscoelasticity of rocks. Applications include mixtures of creeping clay and elastic quartz, mixtures of different creeping materials (e.g., clay and kerogen), or viscous fluids containing bubbles or solid fines. We have found that elastic inclusions in a Maxwell viscoelastic background change the effective viscosity and the high-frequency limiting elastic modulus. The viscosity response was similar to that observed for a Newtonian fluid, and the high-frequency elastic modulus varied as predicted by elastic effective media models. The characteristic frequency of the effective medium scales with the ratio of effective modulus and effective viscosity. Inclusions also distribute the relaxation times, converting the Maxwell material to resemble a Cole-Cole material. Elastic inclusions in a creeping background decrease the effective viscoelastic Poisson’s ratio of the composite. As with elastic media, geometric alignment of phases with contrasting properties leads to viscoelastic anisotropy. Our modeling has illustrated how the amount of heterogeneity and the microgeometry of heterogeneity affects anisotropy; for example, aligned oblate elastic inclusions can increase the amount of creep in the symmetry direction while decreasing creep normal to the symmetry direction. We have developed a suggested interpretation template for how creep function parameters vary with the amount and microgeometry of elastic phases. Interpretation also depends strongly on the material properties of the creeping phase in the absence of elastic inclusions. Extrapolating from dynamic to quasi-static viscoelastic response is intrinsically nonunique without knowledge of the material microstructure. Dominant relaxation mechanisms can be different at different measurement scales and at different measurement strain amplitudes. For example, the observed dynamic response can be fit with an infinite number of microgeometries, each of which has a different long-term behavior.
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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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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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Al-Marzouqi, Hasan. "Digital Rock Physics: Using CT Scans to Compute Rock Properties." IEEE Signal Processing Magazine 35, no. 2 (March 2018): 121–31. http://dx.doi.org/10.1109/msp.2017.2784459.
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Abbas Babasafari, Amir, Deva Ghosh, Ahmed M. A. Salim, and S. Y. Moussavi Alashloo. "Rock Physics Modeling Assisted Reservoir Properties Prediction: Case Study in Malay Basin." International Journal of Engineering & Technology 7, no. 3.32 (August 26, 2018): 24. http://dx.doi.org/10.14419/ijet.v7i3.32.18385.
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Shear velocity log is not measured at all wells in oil and gas fields, thus rock physics modeling plays an important role to predict this type of log. Therefore, seismic pre stack inversion is performed and elastic properties are estimated more accurately. Subsequently, a robust Petro-Elastic relationship arising from rock physics model leads to far more precise prediction of petrophysical properties. The more accurate rock physics modeling results in less uncertainty of reservoir modeling. Therefore, a valid rock physics model is intended to be built. For a better understanding of reservoir properties prediction, first of all rock physics modeling for each identified litho-facies classes should be performed separately through well log analysis.
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Vashisth, Divakar, and Tapan Mukerji. "Direct estimation of porosity from seismic data using rock- and wave-physics-informed neural networks." Leading Edge 41, no. 12 (December 2022): 840–46. http://dx.doi.org/10.1190/tle41120840.1.
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Petrophysical inversion is an important aspect of reservoir modeling. However, due to the lack of a unique and straightforward relationship between seismic traces and rock properties, predicting petrophysical properties directly from seismic data is a complex task. Many studies have attempted to identify the direct end-to-end link using supervised machine learning techniques, but they face challenges such as lack of a large petrophysical training data set or estimates that may not conform with physics or depositional history of the rocks. We present a rock- and wave-physics-informed neural network (RW-PINN) model that can estimate porosity directly from seismic image traces with no wells or with a limited number of wells and with predictions that are consistent with rock physics and geologic knowledge of deposition. The RW-PINN takes advantage of auto-differentiation to compute the gradients across the rock- and wave-physics models. As an example, we use the uncemented-sand rock-physics model and normal-incidence wave physics to guide the learning of the RW-PINN to eventually get good estimates of porosities from normal-incidence seismic traces and limited well data. Training the RW-PINN with few wells (weakly supervised scenario) helps in tackling the problem of nonuniqueness as different porosity logs can give similar seismic traces. We use a weighted normalized root mean square error loss function to train the weakly supervised network and demonstrate the impact of different weights on porosity predictions. The RW-PINN's estimated porosities and seismic traces are compared to predictions from a completely supervised model, which gives slightly better porosity estimates but matches the seismic traces poorly and requires a large amount of labeled training data. We demonstrate the complete workflow for executing petrophysical inversion of seismic data using self-supervised or weakly supervised RW-PINNs.
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Abid, Muhammad, and Syed Haroon Ali. "Modified approach to calculate brittleness index in shale reservoirs." SOCAR Proceedings, no. 1 (March 30, 2024): 3–9. http://dx.doi.org/10.5510/ogp20240100933.
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The successful production of unconventional reservoirs through hydraulic fracturing is heavily dependent on the brittleness of the shale's. To precisely evaluate rock brittleness the mineral-based brittleness indices are considered reliable. Initially, only the weight fraction of quartz accounted for mineralogical brittleness, later dolomite was included in the group, further, it was divided into the silicate and carbonate groups. Due to its heterogeneous nature, shales contain some other minerals, however, the impact of these minerals on rock brittleness has yet to be analyzed. The mineral pyrite is commonly found in shales but its impact on rock brittleness is still undefined. In this research, we proposed a modified mineral-based brittleness index including mineral pyrite, and investigated its impact on rock brittleness. The modified index was evaluated using the data from three different shale reservoirs in China. In the next step, the Fracability Index (FI) model of rocks including pyrite mineral is calculated, and finally, the rock physics modeling is performed to evaluate the elastic response of the rock to this mineral. The modified brittleness index was found to be more accurate in predicting rock brittleness than other mineral-based brittleness indices. The correlation between the modified brittleness index and the FI model was higher than the correlation with other mineral-based brittleness indices. Further, rock physics modeling also proved that the rock with high pyrite content has low Poisson's ratio and high Young's modulus.. These properties are associated with brittle rocks, which further supports the inclusion of pyrite as a brittle mineral in the modified brittleness index. Hence, the findings of this research indicate that the modified brittleness index based on minerals is a reliable approach for predicting rock brittleness in shale reservoirs that contain pyrite. This study has an important application for the development and management of shale reservoirs. The modified brittleness index can be used to identify rocks that are more likely to be successfully fractured during hydraulic fracking process. Keywords: shale reservoirs; hydraulic fracturing; mineral brittleness; fracability index.