Optimizing Shale Oil Enhanced Recovery with Tuned Hydrocarbon Solvents
Notice bibliographique
Résumé
Abstract Shale oil development began around 2006, and since then more than 100,000 horizontal shale oil wells have been drilled in several basins across the U.S. All of these wells are characterizes by high initial flowrates and steep declines, and oil recovery of only 2-8%. as a percentage of OOIP in the well drainage or spacing area. Shale Enhanced oil recovery (EOR) has been conducted in more than 50 separate projects since around 2013, mostly via cyclic, or "Huff and Puff" operation using natural gas as the injection fluid. Recently, a few shale oil EOR projects have been successfully conducted using natural gas liquids, and these projects have demonstrated the ability to recover much more oil from these shale formations, and much faster than natural gas. Field tests to date show the key to optimizing the oil recovery from shale formations via Huff and Puff EOR is largely a function of the injectant fluid. Our objective is to illustrate via compositional reservoir simulation and core testing conducted in a representative Utica shale oil well how injected solvent composition may be adjusted, or "tuned" to optimize oil and gas recovery, at rates and volumes well in excess of that which may be recovered via dry or wet natural gas or carbon dioxide. Compositional reservoir simulation modeling was conducted in two Utica shale production history matched wells to evaluate enhanced oil recovery performance with varying hydrocarbon solvent injectant composition. Core testing of solvent huff and puff oil recovery was conducted in a representative Utica shale core with varying solvent composition to confirm the simulation model software results. Compositional simulation modeling of two Utica shale horizontal wells was conducted to obtain a history match on oil, gas, and water production. The matched models were then utilized to evaluate various liquid hydrocarbon solvent solutions for shale oil EOR. The modeling indicates that for these particular wells, incremental oil production of 300% over primary production may be achieved in the first five years of EOR operation. Using hydrocarbon solvent solutions optimized for each well and the in situ reservoir properties and hydrocarbon composition has a significant effect on oil recovery. The core tests, conducted with a more limited number of solvent compositions, confirmed the EOR performance as predicted via compositional reservoir simulation and the need for tuning of solvent composition for optimum oil recovery Tuned liquid hydrocarbon solvent huff and puff enhanced oil recovery in shale oil formations can significantly improve production and reserves. Tuning the liquid hydrocarbon solvent composition is required to achieve optimum oil recovery, as every shale oil play, and well location within the play, has specific rock and fluid characteristics. Tuned liquid hydrocarbon solvent huff and puff EOR has numerous advantages over cyclic gas injection, such as much greater oil recovery, much better economics/lower cost per barrel, better injection fluid containment, precludes the need for artificial lift, ability to optimize each cycle by integration with compositional reservoir simulation modeling, and lower emissions. Compositional reservoir simulation and core testing of a Utica shale core as described herein confirm the need for liquid hydrocarbon solvent composition tuning, which should enable recovery of far more oil, earlier and at lower cost, greatly improving profitability and extending the life of shale oil wells by several years, while precluding the need for artificial lift.
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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) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| 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 | 0,000 | 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 ».