Velocities of low-frequency seismic waves and, in most rocks, sonic logging waves depend on the compressibility of the undrained rock, which is conventionally computed from the drained rock compressibility using Gassmann's equation. Although more comprehensive and accurate alternatives exist, the simplicity of the equation has made it the preferred fluid substitution model for geoscience applications. In line with recent publications, we show that Gassmann's equation strictly applies only to rocks with a microhomogeneous void space microstructure that is devoid of cracks and microcracks. We use a rock physics model that separates the respective compliance contributions of pores and cracks on dry (drained) moduli and show that Gassmann's model does not apply to rocks with measurable crack density. A fourth independent bulk modulus (in addition to the bulk moduli of the mineral matrix, dry frame, and saturating fluid) is required to take the effect of cracks into account and perform fluid substitution modeling for rocks with pores and cracks more accurately than prescribed by Gassmann's equation. Therefore, we propose combining the Vernik-Kachanov model with Brown-Korringa's equation for more reliable modeling of undrained bulk compressibility for reservoir rocks with measurable crack density. To conclude, a practical quantification of the applicability of Gassmann's equation based on the combined effects of crack density and stress sensitivity is proposed.

Deep neural networks (DNNs) have the potential to streamline the integration of seismic data for reservoir characterization by providing estimates of rock properties that are directly interpretable by geologists and reservoir engineers instead of elastic attributes like most standard seismic inversion methods. However, they have yet to be applied widely in the energy industry because training DNNs requires a large amount of labeled data that is rarely available. Training set augmentation, routinely used in other scientific fields such as image recognition, can address this issue and open the door to DNNs for geophysical applications. Although this approach has been explored in the past, creating realistic synthetic well and seismic data representative of the variable geology of a reservoir remains challenging. Recently introduced theory-guided techniques can help achieve this goal. A key step in these hybrid techniques is the use of theoretical rock-physics models to derive elastic pseudologs from variations of existing petrophysical logs. Rock-physics theories are already commonly relied on to generalize and extrapolate the relationship between rock and elastic properties. Therefore, they are a useful tool to generate a large catalog of alternative pseudologs representing realistic geologic variations away from the existing well locations. While not directly driven by rock physics, neural networks trained on such synthetic catalogs extract the intrinsic rock-physics relationships and are therefore capable of directly estimating rock properties from seismic amplitudes. Neural networks trained on purely synthetic data are applied to a set of 2D poststack seismic lines to characterize a geothermal reservoir located in the Dogger Formation northeast of Paris, France. The goal of the study is to determine the extent of porous and permeable layers encountered at existing geothermal wells and ultimately guide the location and design of future geothermal wells in the area.

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.

The main objective of this project was to evaluate the ability to derive petrophysical properties like porosity from seismic data in a carbonate environment. A special attention has been given to the possibility of characterizing the geometry of the pore space directly from the pre-stack seismic data. We apply a direct petrophysical inversion technique to a carbonate reservoir offshore Brazil. Starting from an initial geological model in depth and a number of carefully conditioned seismic angle stacks, we derive a detailed 3-D model of the porosity and hydrocarbon saturation matching the observed seismic data. We use a well-calibrated Petro-Elastic Model (PEM) to link the petrophysical properties to the seismic velocities. We compare inversion results obtained using the Xu-Payne and T-matrix PEMs which both account for carbonate pore geometry, lithology, porosity and fluid content but have different elastic sensitivity to fluid saturations. The inverted results provide detailed images of the spatial variations of porosity and fluid content across the reservoir interval. Obtaining estimates of absolute saturations values is more difficult, as saturation estimation is strongly dependent on the choice of PEM.

In this paper, we present a method for the direct stratigraphic inversion of lithology from pre-stack seismic data. This approach is a generalization to discrete variables of a methodology for petrophysical seismic inversion of continuous attributes.

Abstract We introduce a Stratigraphic inversion method that simultaneously integrates pre-stack seismic data with petrophysical and geological data. We use simulated annealing to invert directly for reservoir properties such as porosity, lithology and fluid content in a 3D geocellular model. Well and seismic data are integrated in their respective domains along with physical constraints at different vertical scales to produce an optimal solution. Application of user-defined Petro-Elastic Models (PEM) is a key element of the proposed methodology. In addition to connecting the inverted properties to the seismic response, the PEMs are used to maintain consistency between the time, depth and derived velocities throughout the inversion process. The proposed methodology overcomes the limitations faced by many existing techniques with regards to vertical resolution, time-to-depth conversion and the link between seismic response and reservoir properties. The result of our petrophysical seismic inversion is a fine-scale shared earth model in depth that is consistent with both log and seismic data and can be used for reservoir performance prediction. After demonstrating the robustness of the method on synthetic data, we present a result from a real dataset. The proposed methodology has been successfully applied to porosity inversion on one of the largest undeveloped oil fields in the North Sea. A fine-scale reservoir model has been obtained which reveals previously undetected geological structures and leads to a better understanding of the reservoir zone.

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2005Petrophysical Seismic InversionAuthors: Thierry ColéouFabien AlloRaphaél BornardJeff HammanDon CaldwellThierry ColéouCGG, Fabien AlloCGG, Raphaél BornardCGG, Jeff HammanMarathon Oil, and Don CaldwellMarathon Oilhttps://doi.org/10.1190/1.2147938 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract We present a seismic inversion method driven by a petroelastic model, providing fine‐scale geological models, in depth, fully compatible with pre‐stack seismic measurements.Permalink: https://doi.org/10.1190/1.2147938FiguresReferencesRelatedDetailsCited ByModel reduction in geostatistical seismic inversion with functional data analysisLeonardo Azevedo10 November 2021 | GEOPHYSICS, Vol. 87, No. 1Selection of Sensitive Post-Stack and Pre-Stack Seismic Inversion Attributes for Improved Characterization of Thin Gas-Bearing Sands20 November 2021 | Pure and Applied Geophysics, Vol. 179, No. 1Geological reservoir modeling and seismic reservoir monitoringCharacterization of gas hydrate systems on the Hikurangi margin (New Zealand) through geostatistical seismic and petrophysical inversionFrancesco Turco, Leonardo Azevedo, Dario Grana, Gareth J. 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Glinsky3 April 2007 | GEOPHYSICS, Vol. 72, No. 3 SEG Technical Program Expanded Abstracts 2005ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2005 Pages: 2668 publication data© 2005 Copyright © 2005 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 07 Dec 2005 CITATION INFORMATION Thierry Coléou, Fabien Allo, Raphaél Bornard, Jeff Hamman, and Don Caldwell, (2005), "Petrophysical Seismic Inversion," SEG Technical Program Expanded Abstracts : 1355-1358. https://doi.org/10.1190/1.2147938 Plain-Language Summary PDF DownloadLoading ...