Assessment of Mud-Filtrate-Invasion Effects on Borehole Acoustic Logs and Radial Profiling of Formation Elastic Properties
Notice bibliographique
Résumé
Summary Despite continued improvements in acoustic-logging technology, sonic logs processed with industry-standard methods often remain affected by formation damage and mud-filtrate invasion. Quantitative understanding of the process of mud-filtrate invasion is necessary to identity and assess biases in the standard estimates of in-situ compressional- and shear-wave (P- and S-wave) velocities. We describe a systematic approach to quantify the effects of mud-filtrate invasion on borehole acoustic logs and introduce a new algorithm to estimate radial distributions of elastic properties away from the borehole wall. Radial saturation distributions of mud filtrate and connate formation fluids are obtained by simulating the process of mud-filtrate invasion. Subsequently, we calculate radial distributions of the elastic properties using the Biot-Gassmann fluid-substitution model. The calculated radial distributions of formation elastic properties are used to simulate array sonic waveforms. Finally, estimated P- and S-wave velocities for homogeneous, stepwise, and multilayered formation models are compared to quantify mud-filtrate-invasion effects on sonic measurements. We use a nonlinear Gauss-Newton inversion algorithm to estimate radial distributions of formation elastic parameters in the presence of invaded zones using normalized spectral ratios of array waveform data. Inversion examples using synthetic and field data indicate that physically consistent distributions of formation elastic properties can be reconstructed from array waveform data. In turn, radial distributions of formation elastic properties can be used to construct more-realistic near-wellbore petrophysical models for applications in reservoir simulation and production. Introduction During and after drilling, the near-wellbore formation is often altered by stress buildup and release, mud-filtrate invasion, chemical reactions, and many other factors. These alterations cause the physical properties in the near-wellbore region to be different from those of the virgin rock formation. Stress concentration around a wellbore may cause near-wellbore damage and induce formation anisotropy on P- and S-wave velocities. The stress-induced anisotropy can be identified by dispersion analysis (Plona et al. 2002). Positive radial velocity gradients focus the elastic waves propagating away from the wellbore back toward the borehole wall. Such a phenomenon can be identified easily from high-amplitude acoustic arrivals. In this paper, we focus our attention on mud-filtrate-invasion effects only. It is well known that formation properties inferred from wireline logging measurements may not reflect true properties of virgin formations. A realistic description of the invaded zone is important for the processing and interpretation of sonic logs. A common model used in the open literature assumes that a sharp interface exists between the altered zone and the undisturbed formation (Baker 1984). The term "stepwise" is used to describe this type of mud-filtrate-invasion model. Linear-gradient models have been described for syntheses of acoustic waveforms as well (Stephen et al. 1985). Actual radial distributions of elastic wave properties resulting from invasion can be complex and are dependent on the specific petrophysical properties of the rock as well as on the static and dynamic properties of the fluids involved. We divide the invaded zone into a set of concentric radial layers to represent an actual invasion profile and call it a "multilayered" model. Subsequently, we describe a procedure for calculating the radial distributions of formation elastic properties with the Biot-Gassmann fluid-substitution model starting from numerically simulated radial distributions of flow saturation.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».