Sensitivity Studies of Resistivity Monitoring for Shallow Enhanced Recovery Processes- A Numerical Case History
Notice bibliographique
Résumé
Abstract Enhanced Oil Recovery (EOR) processes have been monitored using gas analysis, geophysical logging, observation wells (pressure and temperature), well testing methods, seismic velocity tomography, and inversion of deformation measurements. Each of these methods has various strengths and weaknesses. Most EOR processes involve large changes in the formation resistivity because of the introduction of a fluid phase with a different electrical conductivity, or because of a large temperature change. These massive changes in subsurface conductivity may be monitored remotely using subsurface-to-surface resistivity techniques to investigate propagation of fluid fronts and growth of affected zones with time. In this paper, we present general requirements for resistivity techniques in EOR monitoring, typical electrode installations to reduce noise and seasonal variations, and approaches to the inversion of three-dimensional (3D) EOR resistivity problems. Sensitivity analyses for a real reservoir case subjected to EOR are presented, and a method of rapid design evaluation for resistivity monitoring is outlined. It appears that that the analysis tools and the technology is now adequate to systematically develop electrical monitoring for various EOR applications. Introduction Geophysical techniques have been used for reservoir delineation and description for several decades. Currently, attempts are being made to modify geophysical techniques to actively monitor secondary and tertiary recovery processes such as water injection, cyclic steam stimulation, and in situ combustion. In principle, changes in geophysical responses with time can be used to estimate the lateral extent and volume of the reservoir affected by the EOR process. Thus, the main objective of EOR monitoring is to provide reliable spatial and temporal descriptions of fluid or temperature fronts. These data may be supplemented with production and observation well histories to estimate recovery efficiency and to develop production strategies. In practice, high-quality remote geophysical data should lead to improved process control and greater recovery ratios because they represent independent measures of recovery behavior which can be used to guide production strategies. Case histories of reservoir monitoring based on 3D/4D reflection seismology (Greaves and Fulp, 1987; Pullin et al., 1987; King et al., 1988; Eastwood et al., 1994; Mathisen et al., 1995a and 1995b, Lumley, 1995, and many more), crosshole seismic transmission (Justice et al., 1989; Bregman et al., 1989), surface deformation inversion (Dusseault et al., 1993) and electrical methods (Dorfman et al., 1977; Bartel, 1982; and many more) have been reported in the literature. These studies demonstrate the feasibility of geophysically monitoring EOR processes. With technological advances and innovative techniques, one may expect that it will become possible to more reliably monitor chemical flood evolution, gas distribution changes, and hydraulically induced fracture propagation. The most widely used and successful geophysical technique for describing reservoir processes is surface 3D/4D reflection seismics. Compared to surface seismics, crosshole methods or vertical seismic profiling (VSP) have greater vertical resolution. They are important aids in calibrating surface seismic survey for use in lateral variation studies of seismic velocity, but are invariably more costly than surface methods. Surface, VSP and crosshole methods are based on ray-path tracing and analysis of velocity changes.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».