Formation Damage Assessment and Filter Cake Characterization of NPs/Ca-Bentonite Fluids for Drilling Harsh Environments Using Computed-Tomography Scan
Notice bibliographique
Résumé
Abstract Invasion of mud filtrate while drilling is considered as one of the most common sources of formation damage. Minimizing formation damage, using appropriate drilling fluid additives that can generate good-quality filter cake, provides one of the key elements for the success of the drilling operation. This study focuses on assessing the effect of using different types of nanoparticles (NPs) with Ca-bentonite on the formation damage and filter cake properties under downhole conditions. Four types of oxide NPs were added to a suspension of 7 wt% of Ca-bentonite with deionized water: ferric oxide (Fe2O3), magnetic iron oxide (Fe3O4), zinc oxide (ZnO), and silica (SiO2) NPs. The NPs/Ca-bentonite suspensions were then used to conduct the filtration process at a differential pressure of 300 psi and 250°F, using a standard filter press. Indiana limestone disks of 1 in. thickness were examined, as the filter medium, to simulate the formation in the filtration experiments. Computed-tomography (CT) scan technique was used to characterize the deposited filter cake and evaluate the formation damage that was caused by using different fluid samples. The results of this study showed that the filtrate invasion is affected by the type of NPs, which is also affecting the disk-porosity. Using 0.5 wt% of Fe2O3 NPs with the 7 wt% Ca-bentonite fluid showed a higher potential to minimize the amount of damage. The average porosity of the disk was reduced by 1.0%. However, adding 0.5 wt% of Fe3O4, SiO2, and ZnO NPs yielded a disk-porosity decrease by 4.7, 13.7, and 30%, respectively. The decrease in the disk-porosity after the filtration is directly proportional to the volume of invaded filtrate. Compared to that of the base fluid, the best reduction in the filtrate invasion was achieved when adding 0.5 wt% of Fe2O3 and Fe3O4 NPs by 42.5 and 23%, respectively. The results revealed that Fe2O3 and Fe3O4 NPs can build better Ca-bentonite-platelet structure and thus, a good-quality filter cake. This is due to their positive surface charge and stability in suspensions, as demonstrated by zeta potential (ζ-potential) measurements, which can minimize formation damage. Increasing the concentration of Fe3O4 NPs from 0.5 to 1.5 wt% showed an insignificant variation in the filtrate invasion, spurt loss, and filter cake permeability; however, an increase in the filter cake thickness as well as the amount of damage created was observed. The 1.5 wt% of ZnO NPs showed a better performance compared to the case having 0.5 wt% of ZnO NPs, but in the meanwhile it showed the lowest efficiency when compared to the other types of NPs. This could be due to their surface charge and suspensions’ instability. Results of this work are useful in evaluating the drilling applications using Ca-bentonite based fluids modified with NPs as an alternative to the commonly used Na-bentonite. Additionally, it might help in understanding the NPs/Ca-bentonite interaction for providing more efficient drilling operations and less formation damage.
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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 | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 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 ».