Performance Evaluation of a Biomaterial in an Aqueous-Based Drilling Mud at High Pressure High Temperature
Bibliographic record
Abstract
Oil reserves are found in deep formations where the conditions of temperature and pressure are always high. These conditions have direct effects on the rheological properties of drilling fluid as they vary with increasing temperatures and pressures. Two sets of experiment were carried out on weighted and unweighted mud samples at a temperature of 250oF and 500psi pressure. The presence of locally sourced additives helped the mud to remain pseudoplastic at these conditions and also retained essential properties of the mud need for drilling operations. The plastic viscosities of the weighted and unweighted mud were 36cP and 27cP respectively. The yield stresses were 149lb/1002ft and 110lb/1002ft for the weighted and unweighted samples while the fluid loss volumes were approximately equal. The thickness of the cake for the weighted sample is 3.7mm and 4mm for the unweighted sample. The weighted sample with a reasonably higher PV has a better hole cleaning ability than the unweighted sample. Mucunna Solannie performed excellently to retain the essential properties of these formulations at 250oF and is therefore recommended as a HPHT drilling mud additive.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".