Acid Fracturing and Hydraulic Fracturing Applied in a Single Well for a Deep Carbonate Reservoir Appraisal; The Completion, Perforation, Stimulation and Data Gathering Experiences for 7 Zones
Notice bibliographique
Résumé
Abstract Acid Fracturing and Hydraulic Fracturing were used to stimulate and appraise a very challenging deep, low-porosity, carbonate reservoir with a complex combination of matrix background fractures (BF), and some large fault corridors and fault associated fractures (FAF). The well was drilled deviated through the reservoir to increase probability of intercepting fault corridors for production. The execution of the stimulations proved challenging due to the high strike-slip stress regime. Data from the pre-stimulation diagnostic tests was meticulously used to successfully plan and optimize each stimulation. Acid stimulations on previous 4 wells showed challenges in formation breakdown as well as potential for shear-dilation pre-existing fractures during the injection phase. While during the production phase a rapid collapse of fracture conductivity was observed. Therefore, proppant stimulations were selected to ‘preserve’ the conductivity increase from the fracture dilation. For the larger fault zones, high leak-off was expected and acid fracturing was applied with emphasis on etching of the fracture surfaces and maximizing the acid diversion. Design preparations included laboratory conductivity testing of acid recipes and proppant on outcrop material. Pre-stimulation diagnostic tests were designed for the acid and the proppant fracs, considering the different reservoir characteristics of each stimulation target. After the well was drilled, an integrated evaluation of mud logs, wireline logs, MPD drilling data and structural geology modeling was performed and 7 stimulation targets were identified. Acid stimulations were chosen for zones where faults and "fault-associated fractures" were identified and high leak-off was expected. The designs incorporated several diversion techniques that were successfully implemented to improve stimulation effectiveness. Proppant stimulations were placed in zones where only "background fractures" predominated, and initially followed designs typically pumped in unconventionals. The proppant stimulations, however proved to be the most challenging, and the pumping designs and perforation strategy were changed substantially after DFIT analyses. One hybrid stimulation treatment was conducted to create an etched fracture and then tail in with a proppant treatment at the end of the stimulation. This practice is rarely attempted in the oil and gas industry but here the feasibility is demonstrated. Intermediate clean-up and well testing were done to understand the reservoir characteristics and quantify whether acid fracturing or proppant fracturing is an appropriate stimulation method for the different zones in the reservoir. A small group of 7 seismic stations were deployed to track the area natural seismicity and monitor the stimulation events. The quality of diagnostic information exceeded expectations, and the results could be linked to the performance of the stimulations. It showcases the feasibility of utilizing only a small array of geophones to diagnose the stimulation effectiveness. Learnings and experiences for designing and execution of proppant fracturing, as an alternative to matrix or fracture acidizing in these complex carbonate formations are presented. Learnings for, successful diversion practices for acid fracturing in faulted carbonate zones with high leak-off are also shared.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».