Evaluation of Enhanced Oil Recovery Potential of the Montney Shale Via the SuperEOR and UltraEOR Processes
Notice bibliographique
Résumé
Abstract The Montney shale is the largest shale oil and gas producing formation in the Western Canadian Sedimentary Basin and has the largest resource estimated at 121 trillion m3 of gas (4274 trillion cubic feet, TCF), 20 billion m3 of NGLs (127 billion barrels) and 22.5 billion m3 of oil (141.5 billion barrels)1. However only about 10% of the gas, 11% of the NGLs and less than 1% of oil is considered to be marketable based on current practises33. The Montney Formation, due to varying paleo- depth of burial and basin heat flow, has reservoir hydrocarbons ranging from black oil in the east to dry gas in the west. Tight siltstone (unconventional) reservoirs within the Montney are currently the primary target for many oil/gas companies in western Canada. Economic hydrocarbon flow rates in the Montney Formation, with nano- and micro-Darcy unstimulated (matrix) permeabilities, requires horizontal drilling and multistage hydraulic fracturing completions. This paper introduces two novel EOR processes that can greatly increase the production and recovery of oil and gas from the Montney shale, while reducing the cost per barrel of recovered oil, and reducing GHG emissions and water consumption/production/disposal. Core tests, compositional reservoir simulation, and rock mechanical analyses demonstrate the potential of these novel EOR processes. The EOR methods utilize a triplex pump to inject a solvent liquid into the shale oil reservoir, and an efficient method to recover the injectant at the surface, for storage and reinjection. Compositional simulation modeling of a Montney shale horizontal well producing rich gas condensate was conducted to obtain a history match on oil, gas, and water production. The matched model was then utilized to evaluate two novel shale oil EOR methods under a variety of operating conditions. The modeling indicates that for this particular well, incremental oil production of 300% over primary EUR may be achieved in the first five years of EOR operation via the SuperEOR method. A further enhanced EOR method, UltraEOR, is shown to potentially increase oil recovery by 500% in the first five years of EOR operation. Core tests of the SuperEOR method were conducted to confirm the potential of the recovery process and to validate the compositional reservoir simulation model. These methods, which are patent-pending, have numerous advantages over cyclic gas injection, including much greater oil recovery, better economics/lower cost per barrel, reduced gas containment issues, requires less power and fuel, shorter injection time, longer production time, smaller injection volumes, scalability, faster implementation, precludes the need for artificial lift, elimination of the need to buy and sell injectant during each cycle, ability to optimize each cycle by integration with compositional reservoir simulation modeling, and lower GHG emissions. Laboratory HnP experiments and other petrophysical analyses of Montney core and associated produced oil were undertaken under reservoir conditions to provide additional metrics for the compositional reservoir simulation model and confirm the physics of the model. The results show the advantage of the liquid hydrocarbon solvent over carbon dioxide and natural gas in terms of ultimate recovery of oil. These shale oil EOR methods have also been modeled in seven major US shale oil plays, indicating large incremental oil recovery potential. Core tests have confirmed the SuperEOR modeling results and demonstrated high oil recovery, and field tests have been successfully completed that confirm reservoir simulation modelling projections. If implemented early in the life of a shale oil well, application of these processes can slow the production decline rate, recover far more oil earlier and at lower cost, greatly improve profitability and extend the life of the well by several years, while precluding the need for artificial lift.
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Prédiction machine sur la base complète
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Scores du classifieur distillé par catégorie (deux têtes)
| 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 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».