Optimization of Recovery by Huff ‘n’ Puff Gas Injection in Shale-Oil Reservoirs Using the Climbing-Swarm Derivative-Free Algorithm
Notice bibliographique
Résumé
Summary Recent improved-oil-recovery and enhanced-oil-recovery (EOR) methods in shale reservoirs use huff ‘n’ puff gas injection (H&P). Investigating the technical and economic impact of this technology for one well is challenging and time consuming, and even more so when the petroleum company is planning H&P and refracturing (RF) jobs in multiple wells. Thus, in this paper we present an original methodology to learn how to perform these tasks faster and at lower cost to improve oil recovery. The procedure is explained with the use of an actual H&P gas-injection pilot horizontal well in the Eagle Ford Shale, the performance of which is matched using the methodology developed in this paper. The methodology includes use of an original climbing-swarm (CS) derivative-free algorithm that drives, without human intervention, desktop computer or laptop material-balance (MatBal) and net-present-value (NPV) calculations. The code was written in open-source Python programming language. Following history match, the methodology demonstrates that significant improvements in oil recovery can be obtained by injecting gas at larger rates during shorter periods of time (as opposed to injecting gas at lower rates during longer periods of time). Once oil-recovery improvement in a pilot horizontal well is demonstrated, the methodology is extended to the analysis of H&P gas injection and RF in horizontal wells and shale reservoirs that have not yet been developed or are in initial stages of development; this provides a preliminary assessment of H&P and RF potential. Results indicate that oil recovery and NPV from multiple wells can be improved significantly by a strategic combination of H&P gas injection and RF. A combination of derivative-free optimization algorithms, MatBal calculations, and NPVs permits optimizing when to start the H&P gas-injection project, the optimum gas-injection rates (GIRs) and time span of injection, the reservoir pressure at which gas injection should be started in each cycle, and the time span during which the well should produce oil, before starting a new cycle of gas injection. The development strategy of shale-oil reservoirs could be improved significantly if the possibility of H&P gas injection is considered before field development. This could be the case of the Eagle Ford Shale in Mexico, La Luna Shale in Colombia and Venezuela, Vaca Muerta Shale in Argentina, and other shale-oil reservoirs worldwide. The paper contributes the development of an original methodology, which includes use of a derivative-free algorithm we call CS. CS drives the desktop computer or laptop to perform MatBal and NPV calculations, without human intervention, once the optimization process is started. The methodology improves oil recovery and NPV from a single horizontal well or from multiple horizontal wells operating under H&P gas injection.
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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 | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 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 ».