New Hybrid Workflow for Field Development Planning and Execution: Early Time Fracs vs Long Term Production
Notice bibliographique
Résumé
Abstract Recently, a hybrid workflow for cost-effective and efficient field development planning and execution was introduced to accelerate learnings from post-frac data and to inform development decisions, without waiting for long-term production. This workflow combines typically gathered field data (i.e. geomechanics, flowback and short-term production) with existing integrated workflows to significantly reduce uncertainty and execution time associated with today’s asset development workflows. Although powerful, this workflow lacks ties with long-term production data and does not account for variability in fluid properties across the asset, which limits wide-spread applicability. In this paper, the hybrid workflow will be extended to incorporate long-term production data. The extended workflow incorporates a series of correlations and asset maps which can be used to benchmark and improve completion design selection and performance, with the ultimate goal of streamlining asset development. The workflow includes the development and application of the following: 1) asset map of effective stress; 2) asset map of permeability 3) fracture area correlation (effective stress vs. fracture area); 4) normalized rate correlation (90-day production rate vs. linear flow parameter, LFP); and 5) long-term production correlation (extended production vs LFP). Component 5 is the key new addition to the workflow, which adds significant value and improves the widespread applicability in complex assets. A mass-flow rate approach will also be utilized to enhance the ability to compare productivity from wells producing from varying fluid types. By integrating flowback analysis (FBA) with other multi-disciplinary analysis methods into this enhanced hybrid workflow, operators can effectively improve development efficiency and performance of their assets over both short and long-term production. The enhanced hybrid workflow will be demonstrated using a case study from a high-profile asset in a prolific North American unconventional reservoir. The studied asset shows significant variability in fluid properties, geomechanics and productivity. Using this workflow, operators can connect initial reservoir and fluid properties to long term asset performance and by integrating FBA, link geomechanical properties to stimulated area with a given design to achieve target frac and production performance. Incorporating this workflow also assists with minimizing both G&A and development capital costs. By incorporating long-term production into the hybrid workflow, the widespread applicability is significantly improved, while also providing a key link between early-time frac performance and long-term productivity. Using this enhanced hybrid workflow, operators can unlock the full potential of their assets, while dramatically improving the efficiency and cost-effectiveness of asset development.
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 | 0,000 | 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 ».