Formation Damage Assessment and Filter Cake Characterization of NPs/Ca-Bentonite Fluids for Drilling Harsh Environments Using Computed-Tomography Scan
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".