Formulation of a Water-Based Drilling Fluid with Natural Aloe Vera Additive for Enhancing Filtration Properties
Bibliographic record
Abstract
The search for natural additives to improve the performance of water-based drilling fluids (WBDF) has been fueled by the growing need for environmentally friendly and sustainable drilling processes. The objective of this study is to examine the potential utilization of Aloe Vera (AV) powder as a natural additive in WBDF through an experimental analysis of filtration properties. All experimental studies adhered to the American Petroleum Institute (API) standard procedure in order to accomplish the objectives mentioned above. In this experiment, a mixture of water phase volume of (350 ml) and chemical components like xanthan gum (0.8 gm), sodium hydroxide (0.8 gm), bentonite (13 gm), and barite (18 gm) were used to prepare base mud. Samples 1, 2, and 3 are created using 0.25%, 0.5%, and 0.75% weight of aloe vera powder (size less than 150 µm) in relation to water volume, while sample 4 was created using 0.5% weight of aloe vera powder (size less than 212 µm) in relation to water volume. The standard cell is used in the low-pressure mud filtration test, which was conducted for 30 minutes at room temperature with an API condition of 100 psi. Based on the laboratory analysis, this study suggests that AV powder has the potential to be an environmentally beneficial additive for usage in specialized drilling environments rather than generalized drilling operations. Specially, the 0.5% concentration of AV powder with particle size less than 150 µm demonstrated improved fluid loss management for water-based drilling fluid. This study offers a comprehensive evaluation of its influence on the fluid's behavior, with a focus on the drilling fluid's filtration performance. The outcome of the experiment indicates that WBDF containing aloe vera is an economical and environmentally beneficial additive for reducing filtration loss and preventing significant fluid loss during drilling operations.
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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) | 0.000 | 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".