Shape Memory Polyurethane as a Drilling Fluid Lost Circulation Material
Bibliographic record
Abstract
Abstract Drilling fluid loss is a major problem with serious economic and environmental consequences. The use of traditional lost circulation materials (LCMs) to seal wide fractures increases the risk of bit nozzle plugging. In this work, smart LCMs based on shape memory polyurethane (SMPU) are proposed for the first time. SMPU can be programmed to recover at temperatures suited to a given well. As such, SMPU smoothly passes through the bit nozzles, while effectively seal wide fractures once activated. The SMPU is prepared by two step pre‐polymerization and characterized by Fourier transform infrared spectra, X‐ray diffraction, and differential scanning calorimeter. The SMPU is programmed by changing and fixing the original shape to a temporary shape through a thermo‐mechanical process. The shape memory behavior of SMPU is analyzed by tensile apparatus. Compatibility of SMPU with WBMs is determined from mud rheology and filtration tests. Fracture sealing efficiency and shape recovery of SMPU are evaluated by a modified particle permeability apparatus fitted with a model fracture. The results confirm high sealing and shape recovery attributes of SMPU. The plug formed at 114 kg m–3SMPU and 80 °C experiences a sealing pressure of 100 bar with 71.5 cm3cumulative fluid loss.
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) | 0.001 | 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".