Identification and Remediation of High Water Cut Stages in Multi-Stage Hydraulically Fractured Wells: A Case Study in the Midale Formation
Notice bibliographique
Résumé
Abstract A new stage-by-stage analytical framework to identify and control high water influx stages in horizontal wells was successfully used in a multi-layer reservoir in Southeast Saskatchewan, Canada. The wells of interest were drilled in the Midale formation and hydraulically fractured through coil with cemented liners using a borate cross-linked guar gellant. The objective of the project was to develop a robust method to aggregate and analyze multidisciplinary data to identify stages with prolific water inflow and employ multi-cycle closeable sleeves to isolate them. When the correct data is available this framework can be applied at the time of completion to improve well economics through reduced lifting and disposal costs. A multivariate approach is necessary to begin to understand flow per stage in the absence of production logging. This framework involves collecting all available data: geological, geophysical, raw data-van feeds, pressure signatures, frac fluid viscosity profiles, break times, production and tracer concentrations to build a model for targeting sleeve closures. Data collected through on-site laboratories during the hydraulic fracturing treatment allow early identification of stages for further analysis. Stages where full breaking of frac fluids under reservoir conditions is not observed are identified as candidates for possible intervention. High frac fluid viscosities have the potential to increase fracture height growth and connect the wellbore to layers that have mobile water, depending on fracture height growth limiting mechanisms (Fisher, M. K. et al 2012). Particulate water tracers allow for water inflow to be uniquely attributed to individual stages, where higher concentrations are associated with higher inflow. Combined with pressure signatures and initial swabbing data, this technique can allow the operator to better optimize the well from day one. Production analysis before and after closing sleeves serves as the ultimate score-card for this framework. Mitigation of unwanted water influx was achieved using this framework to shut in certain stages early in the well's life. On-site quality control, detailed data collection during hydraulic fracturing, tracer concentrations and initial swabbing provided the necessary signals. Closeable sleeves permitted the fractures to heal immediately after the completion, but also allowed for the optionality to mitigate high water inflow if it was identified. This paper details the results from a well, where the water production decreased by approximately 30% while the oil rate increased. This framework allows for real-time data driven decision making. This enables significant efficiency gains in completion and intervention costs, equipment sizing, facility infrastructure, and lifting costs. This is accomplished through combining multiple datasets, including detailed on-site and after-the-fact frac analysis and solid particulate water tracers to leverage closeable sleeve technology. Potential areas for further applications of this framework include infill drilling in legacy waterfloods or when drilling wells in close vertical proximity to aquifers. This framework is most applicable when attempting to optimize fracture designs when working in new areas and new reservoirs.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 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 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 ».