Notice bibliographique
Résumé
Guest Editorial The oil and gas industry has an amazing opportunity at present; the demand for energy around the globe is increasing and our industry has answered the call in the form of unconventional oil and gas production. Advancements in directional drilling and hydraulic fracturing have transformed low-quality resources into economically viable sources of energy. Over the past 7 years, exploration and production (E&P) companies have increased production rates, but not necessarily recovery rates, and therein lies the challenge. Predicting and maximizing estimated ultimate recovery (EUR) is crucial for moving from efficient to effective unconventional shale completion operations. E&P companies are drilling and producing shale reservoirs at an increasing rate, which is contributing significantly to North American oil and natural gas; however, significant reserves are being left behind. Techniques for predicting EUR vary by operator. Some calculate EUR by extrapolating from initial production rates while others may apply decline curve trends from one play to another. Each method has its advantages and each its caveats. Operators need 6 months to 1 year of production history to truly estimate EUR. However, extrapolation of initial production rates rarely tells the whole story. Some operators may have insufficient data to understand the longer-term behavior, and though traditional decline curve analysis has proven effective for prediction of conventional reservoir production, we are seeking different, more realistic methods to apply to unconventional shale reservoirs. The variability in unconventional shale plays is leading to unpredictable performance from wells. Therefore, as an industry, we must realize that understanding the composition and behavior of the target geology during fracturing is essential to hit the “sweet spots” and ultimately will result in maximizing the amount of recoverable reserves. One of the most important concepts that needs to be fully appreciated is that every well faces a unique set of circumstances. Well and stage spacing is at the forefront of maximizing the production and economics of each completion. We need to evaluate each distinct well to understand the relationship between EUR and well spacing, stage spacing, lateral lengths, and orientation to better optimize completions for each well. Services are needed for the recommendation of fracture treatment designs that maximize treatment efficiency, and a clear demonstration of the effect of completion designs on reservoir recovery. For instance, a technique that could predict initial production and EUR would enable operators to understand the economics of each well earlier to improve booked reserves. Today, approximately 62% of all wells fractured in the United States are horizontal wells. In an effort to reduce costs, E&P companies are “factory mode” drilling these wells, which means they are using identical well spacing, orientation, and fracturing techniques for every well. It is a one-size-fits-all approach that may keep costs low by shaving days off of the drilling process, but is failing to adequately deplete the reservoir.
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| 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 | 0,001 |
| 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 ».