Notice bibliographique
Résumé
Abstract Geomechanics issues are vital in all reservoir processes, but particularly so in weak, unconsolidated sandstones. Coupled stress-flow simulation is necessary to analyze and understand effects such as changes in reservoir volume that arise from heating and pressurization. Also, non-linear plasticity models incorporating shear dilatancy are needed to simulate the dilation effects that are observed in thermal extraction processes in unconsolidated sands. Stress-flow coupling is based on the volume changes that arise with pressure and temperature changes (?p, ?T). Incorporating shear dilation is based on computation of effective stresses from ?T and ?p, then assessing the state of the rock to see if it is shearing and by how much it must dilate. These processes are poorly quantified at present, so it is necessary to monitor the process to calibrate simulation models. The two monitoring domains of greatest interest to coupled geomechanics simulation are the deformation field and the seismic attributes field. How these fields evolve in space and with time are the key factors to tracking processes, to calibrate geomechanics models and to successfully optimize complex in situ processes. A general geomechanics view of how to achieve process monitoring and optimization goals is presented here. Though recent developments have been promising, further progress in monitoring, inversion and coupled geomechanics simulation is needed. Introduction Conventional monitoring in petroleum engineering addresses pressure, temperature and rate measurements, as well as data collected by wellbore logs such as temperature or rate surveys (e.g. spinner surveys). Oil, gas and water production and injection rates are required for regulatory purposes and to help calculate saturations and recovery factors (RF). Changes in reservoir response were commonly assessed using classical well tests and analyses(1, 2). Because classic reservoir simulation in conventional low viscosity cases deals only with mass and heat transport (Darcy and Fourier diffusion processes) combined with saturation changes and relative permeability calculations, these measures were deemed sufficient for reservoir management. Flow rates (Q), well test data and facilities capability analyses are also used for production optimization(3). These measures are considered insufficient for heavy oil (HO) thermal extraction, HPHT reservoir management, high compaction cases and gravitationally-dominated production technologies. In such cases, we are more interested in measures such as the reservoir pore volume change (?V), gas saturation changes in situ (?Sg), swept volume distribution, and so on. These cannot be measured by conventional p-T-Q methods or geophysical wellbore logging, nor are they easily amenable to calculation. When shear dilation, compaction or induced fracturing take place, major changes in rock mass properties occur; understanding what is happening and where it is taking place requires different monitoring and simulation methods. To make monitoring data more useful, flow-stress coupled modelling, also referred to as coupled geomechanical modelling, is carried out. Changes in p and T are analyzed in terms of effective stress changes (? s'ij) through their effect on rock and pore volumes. The links between ?V and the changes ?p, ?T and ? s'ij are established through p-, T-, s'-compressibilities of the bulk rock and of the mineral matter comprising the matrix.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».