Practical Reservoir Management Strategy to Optimize Waterflooded Pools with Minimum Capital Employed
Notice bibliographique
Résumé
Abstract In this consistently low oil price environment, where infill drilling and new field developments struggle to meet economic metrics, production optimization continues to be a focus and driver for the industry. Currently, waterflooding contributes significantly to global oil production and is one of the main non-thermal techniques that can be applied to increase pool recovery. Although proper reservoir management of assets under Waterflood (WF) is critical to reaching the highest recovery factor (RF) possible, it is difficult to achieve and maintain given the inherent dynamic nature of the production mechanism. Further, to achieve optimum reservoir management while providing the opportunity to leverage alternative enhanced oil recovery (EOR) technology, the existing subsurface and surface infrastructure should be fully optimized. By optimizing the subsurface and surface infrastructure in parallel with achieving optimum reservoir management it will result in higher capital efficiency while improving key economic metrics such as operating expenditure (Opex), reserve replacement ratio, depreciation depletion & amortization (DD&A), and overall earnings. Given the existing challenges that include reservoir conformance problems, lack of reservoir energy, excess fluid production, wellbore and pipeline integrity issues, and infrastructure constraints, how to fully optimize the current infrastructure while achieving optimum reservoir management in parallel is the main question and challenge. Husky Energy’s medium oil reservoir management strategy has been highly successful in reinforcing WF as a sustainable long-term recovery method. This paper will present a practical workflow to tackle the challenges highlighted by using a systematic reservoir and production engineering approach with minimum additional capital expenditure (Capex). First, a robust framework was developed to answer three main questions: "What is happening?", "Why is it happening?" and "How can it be improved?". Then, a comprehensive dynamic surveillance methodology, consisting of both numerical and analytical techniques and a 10-step workflow for optimizing a WF project, is discussed. This is followed by the results achieved by employing this strategy in three of Husky’s WF fields; the Wainwright Sparky, Wildmere Lloydminster and Marsden-Manitou Sparky pools located in the Lloydminster oil block. The positive impact that this reservoir management process has had on all key financial metrics will be discussed. As an example, since the beginning of the optimization initiative the Wainwright and Wildmere pool production has increased 21% and 23% respectively, while the Opex has decreased by 33% and 42%, respectively. Further, since implementing a similar strategy in 2016 at the Marsden-Manitou WF, its production has increased by 30% and its Opex has decreased by more than 18%. Finally, this paper will present a WF protocol check list that has been developed as a guideline for engineers who need to optimize pool performance even in a capital constrained environment.
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Prédiction distillée sur la base complète
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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 | 0,001 | 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.
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