MétaCan
Menu
Retour à la cohorte
Enregistrement W1966770196 · doi:10.1190/2014-0722-spseintro.1

Attenuation: Advances in analysis and estimation — Introduction

2014· article· en· W1966770196 sur OpenAlexaffabout
Baishali Roy, Laurence R. Lines, Mike Batzle, Jyoti Behura

Notice bibliographique

RevueGeophysics · 2014
Typearticle
Langueen
DomaineEarth and Planetary Sciences
ThématiqueSeismic Imaging and Inversion Techniques
Établissements canadiensGeoscience BCUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésAttenuationLithologyGeologyReflection (computer programming)Offset (computer science)Relation (database)SeismologyComputer scienceDatabasePetrologyPhysicsOptics

Résumé

récupéré en direct d'OpenAlex

PreviousNext You have accessGEOPHYSICSVolume 79, Issue 5Attenuation: Advances in analysis and estimation — IntroductionAuthors: Baishali RoyLaurence LinesMike BatzleJyoti BehuraBaishali RoyConocoPhillips, Houston, Texas, USA. E-mail: [email protected].Search for more papers by this authorEmail the author at [email protected], Laurence LinesUniversity of Calgary, Department of Geoscience, Alberta, Canada. E-mail: [email protected].Search for more papers by this authorEmail the author at [email protected], Mike BatzleColorado School of Mines, Department of Geophysics, Golden, Colorado, USA. E-mail: [email protected].Search for more papers by this authorEmail the author at [email protected], and Jyoti BehuraSeismic Science LLC, Littleton, Colorado, USA. E-mail: [email protected].Search for more papers by this authorEmail the author at [email protected]https://doi.org/10.1190/2014-0722-SPSEINTRO.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InReddit Attenuation analysis can provide valuable information about lithology, presence of fluids, and physical properties of subsurface rocks and fluids. In addition, other properties such as permeability, mobility of fluids, and fluid saturation, that cannot be inferred from velocity or amplitude-variation-with-offset (AVO) analysis, could possibly be extracted from attenuation analysis. Despite the many potential benefits of attenuation analysis, it has yet to become part of routine reservoir characterization primarily because of two reasons: first, challenges involved in its estimation from reflection seismic data, and second, understanding of the physical mechanisms and the relation to rock properties.Since estimating interval attenuation from reflection seismic data is challenging, most studies estimate the effective attenuation or use direct arrivals from crosswell and vertical seismic profile (VSP) data to estimate the local interval attenuation. Although interval attenuation estimation using layer-stripping approaches have been introduced recently, they are mostly confined to layer-cake subsurfaces. However, recent advances in attenuation tomography, waveform tomography, and full-waveform inversion hold promise in inverting for interval attenuation. These methods will require efficient modeling techniques, both high-frequency-asymptotics-based and finite-frequency-based.Attenuation could play a crucial role in the exploration and exploitation of unconventional hydrocarbon resources such as heavy oils and shale reservoirs. Because of layering, shales are expected to exhibit strong attenuation anisotropy with possibly new loss mechanisms linking attenuation to kerogen maturity and organic content. Moreover, the properties of these attenuative rocks in the seismic band will be significantly different from those in the logging-frequency range and the ultrasonic band. Although many publications have studied the magnitude of attenuation and attenuation mechanisms in the laboratory, almost all existing measurements are in the ultrasonic frequency bandwidth and primarily conducted on sandstones. In contrast, the bandwidth of typical seismic surveys is between 5 and 100 Hz. Consequently, there might not be a direct correspondence between most existing laboratory measurements and field data. Moreover, hydraulic fracturing, pumping and extraction of fluids undoubtedly change the attenuation of the reservoir and surrounding rocks. Laboratory measurements of such changes and time-lapse seismic monitoring techniques would add significant value to field development.The technical articles included in this special section address some of the above issues, most notably on the understanding of attenuation mechanisms and their relation to other physical properties of the rock through controlled laboratory experiments, numerical modeling, and theoretical development. We hope this special section will encourage readers to delve more into understanding and utilizing attenuation, thereby shedding more light on this interesting yet challenging topic.Tisato and Quintal performed laboratory experiments to measure seismic attenuation in the extensional mode in Berea sandstone at strains that were similar to those typically observed in seismic exploration. Their main results indicate that the frequency-dependent component of attenuation, which is associated to fluid saturation, is approximately insensitive to strain and the overall attenuation can be considered as the sum of a frequency-independent component and a frequency-dependent component.Quintal et al. numerically study the effects of fracture connectivity on S-wave attenuation caused by wave-induced fluid flow at the mesoscopic scale. Their results point to the promising perspective of combining estimates of attenuation of P- and S-waves to infer information on fracture connectivity of a fluid-saturated reservoir.Yan et al. have improved Cheng’s pore-aspect ratio spectrum inversion methodology by relating the closure and deformation of soft pores to the measured pressure-dependent porosity data. The inverted pore-aspect ratio spectra are input into modified and extended Tang’s unified velocity dispersion and attenuation model to predict velocity dispersion and attenuation in the full frequency range at various differential pressure conditions.Qi et al. use the concept of patch membrane stiffness and investigate the combined effect of wave-induced pressure diffusion and capillarity on attenuation and dispersion in partially saturated limestone; the authors report that the capillarity reinforcement results in increased phase velocity and reduced attenuation. The capillarity-extended patchy saturation model can be used to consistently interpret the ultrasonic data acquired during the performance of a small-capillary-number imbibition.A derivation of the asymptotic Green’s function in homogeneous, attenuative, arbitrarily anisotropic media using the steepest-descent method is presented in Shekar and Tsvankin. The authors also present numerical results from the asymptotic analysis and those obtained by the ray-perturbation method for P-waves in transversely isotropic media to illustrate the accuracy of the derivation.In addition, Shekar and Tsvankin present a methodology to generate reflection data from attenuative anisotropic media using the Kirchhoff scattering integral and summation of Gaussian beams. The Green’s functions are computed in the reference elastic model by Gaussian-beam summation, and the influence of attenuation is incorporated as a perturbation along the central ray.FiguresReferencesRelatedDetailsCited byHandling short-period scattering using augmented Marchenko autofocusing2 January 2019 | Geophysical Journal International, Vol. 216, No. 3Green’s Function Retrieval and Marchenko Imaging in a Dissipative Acoustic Medium22 April 2016 | Physical Review Letters, Vol. 116, No. 16 Volume 79 Issue 5 Sep 2014 Pages: 1SO-Z136 ISSN (print):0016-8033 ISSN (online):1942-2156 publication data© 2014 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 18 Sep 2014Published in print: 01 Sep 2014 CITATION INFORMATION Baishali Roy, Laurence Lines, Mike Batzle, and Jyoti Behura, (2014), "Attenuation: Advances in analysis and estimation — Introduction," GEOPHYSICS 79: WBi-WBii. https://doi.org/10.1190/2014-0722-SPSEINTRO.1 Plain-Language Summary PDF Download Metrics Loading ...

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,858
Score d'incertitude au seuil0,148

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,004
Tête enseignante GPT0,201
Écart entre enseignants0,197 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeAutre devis
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2014
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueGeophysicsMême sujetSeismic Imaging and Inversion TechniquesTravaux en français237 207