Notice bibliographique
Résumé
The energy transition is in a continuous pursuit of innovative technology applications from all corners of the oil and gas industry. With the exponential growth of carbon capture and sequestration (CCS) projects, similar subsurface appraisal objectives remain. Derisking dynamic reservoir performance and characterizing storage pore space are key enablers for prospective CCS projects. The mention of well testing often takes readers to a visualization of hydrocarbon disposition by a flare. Once a bright and vibrant spectacle of our industry’s exploration updates, the days of flaunting a chairman’s flow or banker’s burn are past. Waning application of well testing during our energy transition is a specious assumption, and it’s the optics that are transforming as the well testing discipline proves its value to long-term carbon storage projects. Injecting CO2 into the subsurface is hardly a new concept; operators have been doing this for decades as part of enhanced oil recovery. However, the application of injecting CO2 is changing in response to operators’ environmental, social, and governance ambitions. While carbon storage concepts exist in many forms, anchoring a storage project requires pore space of adequate quantity and quality in proximity to a fixed source of emissions. Carbon storage projects also must manage additional risks such as sustained injectivity performance, geologic seal integrity, and plume migration, to name a few. Enter well testing. Quantifying injection performance of supercritical fluids is crucial for optimizing wells to meet project needs while minimizing the number of required penetrations through a structural seal. Whether for a saline aquifer or a previously depleted field, pressure transient analysis enhances understanding of the pore space intended for storage and the potential heterogeneities within. Furthermore, integrating well testing with other reservoir-description tools may be used to monitor migration of stored fluids within the reservoir. With corporate and regulatory targets driving the unprecedented pace and scale of CCS opportunities, well testing is quickly reaffirming itself as a powerful tool in characterizing pore space essential to our lower-carbon goals. This month’s papers highlight ongoing developments within the well testing discipline and important reminders about how to properly use dynamic data. The application of these well testing fundamentals to a nascent carbon storage market is still evolving. Recommended additional reading at OnePetro: www.onepetro.org. SPE 208967 Rate-Pseudopressure Deconvolution Enhances Rate-Time Models Production History-Matches and Forecasts of Shale Gas Wells by L.M. Ruiz Maraggi, The University of Texas at Austin, et al. URTeC 3705570 Analysis of Multiple Flow/Buildup Tests Including a 5-year Buildup: Case Study of an Australian Shale Gas Well by Christopher R. Clarkson, University of Calgary, et al. OTC 31691 Challenges and Mitigation Strategies for High-Rate Gas Well Testing in High-Pressure/High-Temperature DST Operation by Jakpakorn Hemaprasertsuk, PTTEP, et al.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».