Experimental study of hydrophilic additives on filter cake permeability and filtrate losses
Bibliographic record
Abstract
Abstract In the past few years, great emphasis has been placed on developing the water‐based mud system in drilling operations because its properties are suitable for the environment and it is a better choice than oil‐ and synthetic‐based muds. Despite research and development in this field, water‐based muds still have filtration issues that lead to drilling problems, and attempts must be continued in this regard. Therefore, this work aims to reduce the filtrate loss of water‐based mud by affecting the drilling cake and making the permeability as low as possible with enhanced properties that resist the filtration of the drilling fluid. Special care was taken to develop suitable mud rheological properties in terms of plastic viscosity, yield point, and gel strength compared to API standards. To this end, some hydrophilic materials were added to the mud, such as thinners (spersene and trisodium phosphate [TSP]) and some polymers (sodium silicate [SS] and poly acryl amide [PAA] (to compare with the basic mud. The results showed that using thinners and polymers without carboxymethyl cellulose (CMC) and baryte reduced filtrate loss and permeability by a small percentage. On the other hand, adding CMC and baryte to the four additives (spersene, TSP, SS, and PAA) each separately reduced the permeability by 66.1%, 67.7%, 74.1%, and 79%, and reduced filtrate loss by 50.1%, 51.4%, 55.3%, and 58.6%, respectively. It was concluded that adding PAA with CMC and baryte can effectively reduce filtrate losses due to its ability to provide a membrane of low permeability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".