Selection of Stimulation Fluids and Treatment Design for Low-Permeability Reservoirs
Notice bibliographique
Résumé
Abstract From the inception of commercial hydraulic fracturing, using first gelled napalm and then thin fluids, until today, a half century later, the selection of the optimum stimulation fluid and treatment design continues to be highly controversial. From a theoretical standpoint, a long, conductive fracture is required to effectively recover reserves in ultra low permeability reservoirs.1 The 1980's and 90's saw the use of massive hydraulic fracturing treatments with up to five million pounds of proppant in crosslinked gel fluids employed to achieve the desired results. Many of these reservoirs are deep and hot, and service companies expend a significant effort to develop fluids with adequate rheological stability to pump long jobs at temperatures in the range of 250 - 350°F. Traditionally, gelled fluid formulations are selected based on the viscosity stability measured by Model 50 Fann viscometer studies.2 Fracturing fluids are marketed based on viscosity stability and seldom, if ever, is the fracture conductivity and cleanup information available for the fluid formulation alternatives to assist in the decision regarding fluid selection. Since the inception of the development of these types of fluids, industry experts voiced concerns about fracture cleanup3 and more recent studies demonstrate that filtercakes from crosslinked gels do not thermally decompose at 350°F.4 However, this strategy with viscous gels was not universally successful, and in some reservoirs, most notably the Austin Chalk5,6,7 and the Barnett Shale8,9 large "water fracs" pumped at high rate with up to 30,000 barrels water and only token amounts of proppant reportedly emerged as the economic treatment of choice.10,11 This strategy is not a new approach but a revival of the use of high rate water/sand treatments which proved to be very successful in a number of reservoirs years earlier.12 Reservoir discontinuities and complex dual porosity reservoirs certainly contribute to the complexity of developing the optimum treatment strategy. Natural fractures promote screenouts, and an industry trend to attempt to minimize screenouts by using overstabilized fluids developed. Unfortunately, using overstabilized fluids can seriously impact the conductivity of natural unpropped fractures and the proppant pack.13 Prefrac injection/fall-off tests proved to be one of the tools that can be used to identify the reservoir characteristics and assist in the treatment design.14 With a reliable measure of expected productivity, one can then imply completion efficiency. Traditionally, fluid design for completions in the 180-220°F reservoir temperature range remains problematic. We are aware of a number of cases where post-frac performance just does not meet expectations. In a study in the deep Upper Morrow in the Anadarko Basin, 17% of the wells produce less after frac than before.15 In this paper we will describe the use of prefrac injection/fall-off tests to characterize reservoir potential and compare the stimulation treatment proppant placement issues, treatment design, fluid formulation and production response for a number of producing horizons in the Deep Basin in Northern Alberta.
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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 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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) | 0,003 | 0,001 |
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 ».